#Loading your work directory and RNASeq counts and filtering the counts

setwd("//files.wustl.edu/Shares/DOM/ONC/Hirbe_Lab/Diana/UBR5 KO RNASeq/analysis/HOM VS WT_2")
Counts <- read.csv("Counts.csv")
# Remove duplicate rows from Counts
Counts <- Counts[!duplicated(Counts[, 1]), ]
rownames(Counts) <- Counts[, 1]
Counts<- Counts [, -1]

# Calculate row means
row_means <- rowMeans(Counts)

# Order genes by row means in descending order
ordered_counts <- Counts[rev(order(row_means)), ]
#alternative code to the above is (ordered_counts <- Counts[order(row_means, decreasing = TRUE), ])
# Filter out rows with row means less than 10
filtered_counts <- ordered_counts[rowMeans(ordered_counts) >= 10, ]
#save filtered data frame
write.csv(filtered_counts, "filtered_counts.csv")

#prepare metadata telling R the conditions (columns) 
metadata <- data.frame( 
  
  sample_id = colnames(filtered_counts),  # Assuming you have loaded the filtered expression data 
  
  condition = c(rep("UBR5 WT", 3), rep("UBR5 HOM", 3)),  # Treatment conditions 
  
  replicate = c(1, 2, 3, 1, 2, 3)  # Sample replicates 
  
) 

metadata$condition <- factor(metadata$condition, levels = c("UBR5 WT", "UBR5 HOM"))

#Load DESEQ2 for normalization
library(DESeq2) 

#Use the DESeqDataSetFromMatrix function from DESeq2 to create a DESeqDataSet object
dds <- DESeqDataSetFromMatrix(countData = filtered_counts,
                              colData = metadata,
                              design = ~ condition)



#Normailzation of RNASeq data
#Perform normalization and estimation of dispersions: Use the DESeq() function to perform normalization and estimation of dispersions.

dds <- DESeq(dds)
results <- results(dds, alpha = 0.05)
DEGs <- subset(results, abs(log2FoldChange) > 1 & padj < 0.05)


#save the de_genes data frame
write.csv(DEGs, file = "DEG_HOM_VS_WT.csv",)
write.csv(results, file = "DeseqResults_HOM_VS_WT.csv",)



#Visualizing your differentially expressed genes

#create volcano plot
library(ggplot2)

# Add column to classify genes as DEG or not
results_df <- as.data.frame(results)
results_df$gene <- rownames(results_df)
results_df$threshold <- "Unchanged"
results_df$threshold[results_df$padj < 0.05 & abs(results_df$log2FoldChange) > 1] <- "DEG"


library(ggrepel)


# Volcano plot
ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HOM vs WT",
       x = "log2 Fold Change (HOM vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black")


# Select top 50 significant genes by padj
top100 <- results_df[order(results_df$padj), ][1:100, ]

# Volcano plot
ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HOM vs WT",
       x = "log2 Fold Change (HOM vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  geom_text_repel(data = top100,
                  aes(label = gene),
                  size = 3,
                  max.overlaps = Inf,
                  box.padding = 0.3,
                  point.padding = 0.2,
                  segment.color = "grey50")

write.csv(top100, file = "Top100_HOM_vs_WT.csv", row.names = FALSE)


#Querrying genes of interest i.e UBR5 substrates as determined form the papers you have read

# Define genes of interest
genes_of_interest <- c("Egfr", "Hsp90ab1", "Map2k2", "Cerk", "Pdgfra", "Tyk2", "Jak1",
                       "Yap1", "Taz", "Kdr", "Aurka", "Pten", "Csf1r","Ptch1", "Smo", "Gli2", "Gli3", "Wnt10a", "Rac2", "Rspo2", "Apc",
                       "Cd274", "Pdcd1", "Id1", "Id3", "Cdh1", "Cdc73", "Hrpt2","Csf1","Golph3", "Cdk1", "Acsl4", "Ptk2b", "Akt1", "Akt2", "Akt3", "Pik3ca", "Pik3c2a", "Pik3cb" , "Pik3c3", "Pik3c2b", "Pik3cd", "Atmin", "Cdkn1a", "Cdk9", "Rela", "Nfkb1", "Nfkb2", "Capza1", "Stat1", "Stat3", "Irf1", "Irf3")

# Subset DEGs for these genes (case-sensitive match!)
genes_subset <- results_df[rownames(results_df) %in% genes_of_interest, ]

# Save genes of interest with stats
write.csv(genes_subset, file = "GenesOfInterest_HOM_vs_WT.csv", row.names = TRUE)

ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HET vs WT",
       x = "log2 Fold Change (HET vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  geom_text_repel(
    data = genes_subset,
    aes(label = gene),
    color = "black",       # <-- force label text to black
    size = 3,
    max.overlaps = Inf,
    box.padding = 0.3,
    point.padding = 0.2,
    segment.color = "grey50"
  )


# Subset DEGs only
DEGs_df <- as.data.frame(DEGs)
DEGs_df$gene <- rownames(DEGs_df)

# Find overlap between DEGs and genes of interest
genes_subset <- DEGs_df[rownames(DEGs_df) %in% genes_of_interest, ]

# Save overlapping genes with stats
write.csv(genes_subset, file = "GenesOfInterest_DEGs_HOM_vs_WT.csv", row.names = TRUE)

# Volcano plot with labels ONLY for genes of interest that are DEGs
ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HET vs WT",
       x = "log2 Fold Change (HET vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  geom_text_repel(
    data = genes_subset,
    aes(label = gene),
    color = "black",       # labels in black
    size = 5,
    max.overlaps = Inf,
    box.padding = 0.3,
    point.padding = 0.2,
    segment.color = "grey50"
  )




#Running pathway enrichment analysis to determine pathways enriched following UBR5 KO

if (!requireNamespace("clusterProfiler", quietly = TRUE)) {
  BiocManager::install("clusterProfiler")
}
if (!requireNamespace("msigdbr", quietly = TRUE)) {
  install.packages("msigdbr")
}
library(clusterProfiler)
library(msigdbr)


# Convert results to dataframe
res_df <- as.data.frame(results)

# Remove NA log2FC
res_df <- res_df[!is.na(res_df$log2FoldChange), ]

# Create named vector: names = gene symbols, values = log2FC
gene_list <- res_df$log2FoldChange
names(gene_list) <- rownames(res_df)

# Sort decreasing for clusterProfiler
gene_list <- sort(gene_list, decreasing = TRUE)


# Mouse Hallmark gene sets
hallmark_sets <- msigdbr(species = "Mus musculus", category = "H")  # H = Hallmark

# Use as two-column dataframe: gs_name (pathway), gene_symbol
term2gene <- hallmark_sets[, c("gs_name", "gene_symbol")]

# Make sure your DESeq2 results have no NA log2FC
res_df <- as.data.frame(results)
res_df <- res_df[!is.na(res_df$log2FoldChange), ]

# Named vector: names = gene symbols, values = log2FC
gene_list <- res_df$log2FoldChange
names(gene_list) <- rownames(res_df)
gene_list <- sort(gene_list, decreasing = TRUE)


gsea_res <- GSEA(
  geneList = gene_list,
  TERM2GENE = term2gene,  # <- must be dataframe, not list
  pvalueCutoff = 0.1,
  verbose = FALSE
)

# View top pathways
head(as.data.frame(gsea_res))

# Save results
write.csv(as.data.frame(gsea_res), "GSEA_Hallmark_Mouse_HOM_vs_WT.csv", row.names = FALSE)

library(enrichplot)

# Convert GSEA results to dataframe
gsea_df <- as.data.frame(gsea_res)

# Suppose the top (and only) enriched pathway:
top_pathway <- gsea_df$ID[5]  # or use $Description if you prefer

# Classic GSEA plot for the top pathway
gseaplot2(
  gsea_res,
  geneSetID = top_pathway,   # pathway ID
  title = gsea_df$Description[5],  # nice descriptive title
  color = "red"
)
#PLOT HALLMARK PATHWAYS
library(ggplot2)

# Convert GSEA results to dataframe
gsea_df <- as.data.frame(gsea_res)

# Order pathways by NES (normalized enrichment score)
gsea_df <- gsea_df[order(gsea_df$NES, decreasing = TRUE), ]

# Plot ALL enriched pathways
ggplot(gsea_df, aes(x = reorder(Description, NES), y = NES, fill = -log10(p.adjust))) +
  geom_col() +
  coord_flip() +
  labs(
    title = "GSEA: All Enriched Hallmark Pathways",
    x = "Pathway",
    y = "Normalized Enrichment Score (NES)",
    fill = "-log10 adj p-value"
  ) +
  theme_minimal(base_size = 14)


#plot GSEA KEGG, GO, REACTOME --------------------------
# Prepare ranked gene list with Entrez IDs for GSEA
# --------------------------
library(clusterProfiler)
library(org.Mm.eg.db)
library(ReactomePA)

# Convert gene symbols to Entrez IDs
entrez_map <- bitr(names(gene_list), fromType="SYMBOL", toType="ENTREZID", OrgDb=org.Mm.eg.db)
gene_list_df <- merge(entrez_map, data.frame(log2FC = gene_list), by.x="SYMBOL", by.y="row.names")
gene_list_df <- gene_list_df[!duplicated(gene_list_df$ENTREZID), ]
gene_list_named <- gene_list_df$log2FC
names(gene_list_named) <- gene_list_df$ENTREZID

# Sort decreasing for GSEA
gene_list_named <- sort(gene_list_named, decreasing = TRUE)

# --------------------------
# 1) GSEA: KEGG Pathways
# --------------------------
gsea_kegg <- gseKEGG(
  geneList = gene_list_named,
  organism = "mmu",
  minGSSize = 10,
  pvalueCutoff = 0.1,
  verbose = TRUE
)

# Save KEGG GSEA results
write.csv(as.data.frame(gsea_kegg), "GSEA_KEGG_HOM_vs_WT.csv", row.names = FALSE)

# Top 30 KEGG pathways barplot
library(enrichplot)
library(ggplot2)

# Convert gseaResult to dataframe to see top pathways
gsea_df <- as.data.frame(gsea_kegg)

# Select top 30 pathways by NES or pvalue
top30 <- gsea_df[order(gsea_df$NES, decreasing = TRUE)[1:30], ]

# Ridgeplot (shows enrichment distribution for multiple pathways)
ridgeplot(gsea_kegg, showCategory = 30) +
  ggtitle("GSEA: KEGG Top 30 Pathways") +
  theme_minimal(base_size = 14)

# Optional: classic GSEA plot for the top pathway
top_pathway <- top30$ID[1]
gseaplot2(gsea_kegg, geneSetID = top_pathway,
          title = top30$Description[1], color = "red")


# --------------------------
# 2) GSEA: GO Biological Process (BP)
# --------------------------
gsea_go_bp <- gseGO(
  geneList = gene_list_named,
  OrgDb = org.Mm.eg.db,
  ont = "ALL",
  keyType = "ENTREZID",
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.1,
  verbose = TRUE
)

# Save GO BP GSEA results
write.csv(as.data.frame(gsea_go_bp), "GSEA_GO_BP_HOM_vs_WT.csv", row.names = FALSE)

# Top 30 GO BP pathways barplot
barplot(gsea_go_bp, showCategory = 30, title = "GSEA: GO BP Top 30 Pathways")

# --------------------------
# 3) GSEA: Reactome Pathways
# --------------------------
gsea_reactome <- gsePathway(
  geneList = gene_list_named,
  organism = "mouse",
  minGSSize = 10,
  pvalueCutoff = 0.1,
  verbose = TRUE
)

# Save Reactome GSEA results
write.csv(as.data.frame(gsea_reactome), "GSEA_Reactome_HOM_vs_WT.csv", row.names = FALSE)

# Top 30 Reactome pathways barplot
barplot(gsea_reactome, showCategory = 30, title = "GSEA: Reactome Top 30 Pathways")

# --------------------------


library(ggplot2)
library(dplyr)

# Convert GSEA Reactome results to dataframe
gsea_reactome_df <- as.data.frame(gsea_reactome)

# Select top 30 pathways by NES magnitude
top30_reactome <- gsea_reactome_df %>%
  arrange(desc(abs(NES))) %>%
  slice(1:30)

# Reorder for plotting (highest NES on top)
top30_reactome$Description <- factor(top30_reactome$Description, levels = rev(top30_reactome$Description))

# Plot barplot: NES on x-axis, pathways on y-axis, fill by -log10(padj)
ggplot(top30_reactome, aes(x = NES, y = Description, fill = -log10(p.adjust))) +
  geom_bar(stat = "identity") +
  scale_fill_gradient(low = "red", high = "darkred") +
  theme_minimal(base_size = 14) +
  labs(title = "GSEA: Top 30 Reactome Pathways",
       x = "Normalized Enrichment Score (NES)",
       y = "",
       fill = "-log10(adj.p)")



#Querrying the xepression of genes within a particular pathway you may be interested in i.e type I interferon signaling

ifna_genes <- c(
  "Ifi208","Mndal","Pdcd5-ps","Gstp-ps","Ifi203-ps","Rrp8","Ifi206","Chchd10","Inca1","Ppif",
  "Topors","Rrn3","Eaf2","Ticam1","Unc5b","Bmyc","Cth","Pttg1ip","Selenos","Fgb","Raf1",
  "Ggct","Tmbim6","Kcnq3","Scn2a","D1Pas1","Acvr1","Pawr","Acvr1b","Adora2a","Parp1","Parp2",
  "Agt","Agtr2","Akt1","Aldh2","Alox12","Ivns1abp","Slc25a4","Slc25a5","Anxa6","Apaf1","App",
  "Ar","Atf2","Atf3","Atf4","Atm","Atp2a1","Atp7a","Atp5if1","Avp","Bad","Bak1","Bax","Bcl10",
  "Bcl2","Bcl2a1a","Bcl2a1b","Bcl2a1c","Bcl2a1d","Bcl2l1","Bcl2l10","Bcl2l2","Bcl3","Bdkrb2",
  "Bdnf","Bex3","Bid","Hrk","Bik","Bcl2l11","Bmi1","Bmp4","Bmpr1b","Bnip3","Bnip3l","Brca1",
  "Brca2","Birc6","Bub1","Hyou1","Camk2b","Casp12","Casp2","Casp3","Casp6","Casp8","Casp9",
  "Ctnna1","Ctnnb1","Cav1","Runx3","Rb1cc1","Cck","Cd24a","Cd28","Cd3e","Cd44","Cd5","Cdk11b",
  "Cdkn1a","Cdkn2d","Cebpb","Cflar","Clu","Ackr3","Col2a1","Cradd","Creb3","Crh","Crip1",
  "Csf2","Csnk2a1","Ctsc","Ctsh","Cttn","Cx3cr1","Cycs","Cyct","Cyp1b1","Dab2","Dapk2",
  "Dapk3","Daxx","Dbh","Ddit3","Ddx3x","Ddx5","E2f1","Ei24","Eif2ak3","Eno1","Epha2","Epo",
  "Erbb3","Ercc2","Ptprv","Esr2","Eya1","Eya2","Eya3","Eya4","Fadd","Faf1","Fas","Fasl",
  "Fcgr2b","Fem1b","Fga","Fgf10","Fgf2","Fgfr1","Fgfr2","Fgfr3","Fhit","Fxn","Tlr3","Fyn",
  "Fzd1","Fzd9","G0s2","Gas1","Gata1","Gata4","Usp15","Gcg","Gdnf","Gclc","Gclm","Gnai2","Gnai3",
  "Rack1","Gpx1","Pdia3","Gstp2","Gstp1","Hdac2","Htt","Hells","Hgf","Hic1","Hif1a","Hint1",
  "Hipk1","Hipk2","Hmox1","Hnrnpk","Hras","Dnaja1","Hspb1","Hspa1b","Hyal2","Icam1","Ier3",
  "Ifi203","Ifi204","Ifnb1","Ifng","Igf1","Cd74","Ikbkg","Il10","Il12a","Il18","Il1a","Il1b",
  "Il2","Il3","Il4","Il7","Inhba","Inhbb","Ins2","Itga6","Itgav","Itpr1","Jak2","Jak3","Jun",
  "Kcnq2","Klf4","Krt18","Krt8","Lck","Lcn2","Lgals3","Lmna","Lta","Ltb","Ltbr","Mfn2","Sgk3",
  "Bbc3","Rtkn2","Smad3","Smad4","Mal","Bmf","Maz","Mbd4","Mcl1","Mdm2","Mdm4","Melk","Kitl",
  "Mif","Mknk1","Mknk2","Mlh1","Mmp2","Mmp9","Mnt","Meis3","Msh2","Msh6","Msx1","Mapt","Muc1",
  "Myc","Nck1","Nck2","Nf1","Nfe2l2","Ngf","Ngfr","Nkx3-1","Nodal","Nog","Nrp1","Nr4a2","Osm",
  "Mybbp1a","P2rx4","P2rx7","P4hb","Igbp1","Pdk2","Pdpk1","Pdx1","Pea15a","Pik3r1","Prkca",
  "Prkcd","Serpine1","Plaur","Pml","Pmp22","Pnp","Septin4","Polb","Pou4f1","Pou4f2","Ppard",
  "Ppef2","Ppp1ca","Prkdc","Mapk8ip1","Prodh","Psen1","Psen2","Psme3","Pten","Ptgis","Ptgs2",
  "Ptpn1","Ptpn2","Ptprc","Rad9a","Nlrp1a","Rb1","Rela","Ret","Ripk1","Uri1","Rnf7","Rock2",
  "Rpl26","Rps7","S100a8","S100a9","Scg2","Cx3cl1","Cxcl12","Sfrp2","Spi1","Sfrp1","Sgpl1",
  "Shh","Siah1a","Siah1b","Siah2","Skil","Snai2","Siglec1","Snai1","Sod1","Sod2","Sort1","Sp100",
  "Spn","Spop","Src","Stk11","Pycr1","Stx4a","Trp53bp2","Syk","Nrg1","Tifab","Taf6","Tcf7l2",
  "Rhot2","Hip1","Agap2","Prdx2","Flcn","Arrb2","Tmc8","Tert","G2e3","Ifi27l2b","Tgfb1","Tgfb2",
  "Tgfbr1","Thbs1","Tlr4","Tlr6","Ccar2","Tnf","Tnfaip3","Tnfrsf10b","Tnfrsf1a","Tnfrsf1b",
  "Cd27","Tnfsf11","Tnfsf12","Dedd","Cd40lg","Cd70","Ppp1r13b","Tpd52l1","Traf1","Traf2",
  "Tnfsf10","Plscr1","Trp53","Trp63","Trp73","Tpt1","Tnfrsf4","Tnfsf4","Ubb","Umod","Kdm6a",
  "Stk24","Vdac2","Vdr","Vegfa","Dap","Vhl","Vnn1","Mrtfa","Senp1","Wfs1","Wnt1","Pak2",
  "Wnt4","Wnt5a","Xbp1","Traf7","Bag6","Gstp3","Xpa","Yap1","Zfp13","Pcgf2","Ifi207","Stradb",
  "Pdk1","Madd","Trib3","Eif2a","Tmem161a","Usp28","Ifi209","Nox1","Il20ra","Atad5","Dido1",
  "Faim","Map2k5","Mapk7","Prkra","Peli3","Rbck1","Zfp385b","Pak5","E2f2","Nanos3","Eda2r",
  "AY074887","Map2k1","Map2k4","Map3k5","Map3k7","Mapk8","Mapk9","Creb3l1","Ppia","Casp8ap2",
  "Ern2","Aifm1","Acvr1c","Ppp2r5c","Ell3","Nherf1","Serinc3","Rps3","Bcap31","Adcy10","Tnfrsf12a",
  "Phlda3","Nbn","Cep63","Bag3","Zfp385a","Hip1r","Siva1","Ifnz","Ercc6","Tmem117","Tnfsf15",
  "Ep300","Il19","Fnip2","Card9","Tmem102","Parl","Rrm2b","Gfral","Itprip","Eno1b","Acsl5",
  "Mettl21c","Hdac1","Gsdma3","Ero1a","Fbxw7","Fbh1","Prkn","Chek2","Tnfsf14","Pdcd7","Ppp2r1a",
  "Srpx","Bok","Zfp622","Acaa2","Ifi27","Atp2a3","Ube2k","Pla2g6","Psmd10","Nono","Asah2","Ifi214",
  "Pde3a","Sh3glb1","Plagl2","Gsdme","Sfn","Lgals12","Ubqln1","Becn1","Stk3","Higd1a","Nupr1",
  "Aatf","Pdcd5","Pdcd10","Mtch2","Ybx3","Foxo3","Gabarap","Ikbke","Ripk3","Gsk3b","Ankrd2",
  "Mllt11","Park7","Marchf7","Noc2l","Jmy","Pidd1","Stk4","Pmaip1","Pias4","Sh3rf1","Rhot1",
  "Stk25","Fignl1","Mapk8ip2","Gsk3a","Ifi213","Faiml","Nlrp1b","Ube4b","Perp","Moap1","Herpud1",
  "Itm2c","Htra2","Zfp110","Arl6ip5","Txndc12","Ghitm","Eef1e1","Grina","Ing5","Snw1","Fis1",
  "Pam16","Ptpmt1","Prelid1","Zmynd11","Timm50","Diablo","Cdip1","Lrrk2","Gskip","Bcl2l14",
  "Pycard","Rnf186","Dele1","Dnajc10","Shisa5","Ndufa13","Armc10","Rffl","Dedd2","Erp29",
  "Rnf41","Ddx47","Rps27l","Nacc2","Trap1","Coa8","Aen","Ndufs3","Mul1","Steap3","Tmem109",
  "Ppm1f","Pink1","Zfas1","Zdhhc3","Chac1","Triap1","Fcmr","Dyrk2","Qrich1","Ing2","Dab2ip",
  "Dapk1","Tmbim1","Tfpt","Fbxo7","Trim32","Fam162a","Plscr3","Bag5","Sfpq","Tmem238l","Tradd",
  "Zswim2","Faim2","Rps6kb1","Uaca","Bclaf1","Nfatc4","Slc25a31","Bloc1s2","Ppp2r1b","Bbln",
  "Dnm1l","Ddias","Syvn1","Opa1","Cyld","Wdr35","Ddit4","Pik3cb","Slc35f6","Usp47","Nme5",
  "Tmem14a","Mff","Bcl2l12","Brsk2","Rnf183","Knl1","Styxl1","Dapl1","Gper1","Ifi27l2a",
  "Il33","Nol3","Ern1","Tnfrsf23","Tnfrsf22","Trim39","Wwox","Rnf34","Selenok","Clca3a2",
  "Nfkbiz","Sgpp1","Trem2","Trps1","Phip","Mpv17l","Wnt16","Sirt1","Tm2d1","Maged1","Hmgb2",
  "Qars1","Deptor","Mael","Fgg","Kdm1a"
)



# Get normalized counts from DESeq2
norm_counts <- counts(dds, normalized=TRUE)

# Subset for IFN-α response genes (keep only genes present in your dataset)
ifna_counts <- norm_counts[rownames(norm_counts) %in% ifna_genes, ]

# Optionally, z-score normalize each gene for heatmap visualization
ifna_counts_z <- t(scale(t(ifna_counts)))


# Create annotation for columns
ann_col <- data.frame(
  Condition = metadata$condition
)
rownames(ann_col) <- metadata$sample_id


library(pheatmap)

# Make sure columns are in your desired order
desired_order <- c("sample.WT_1", "sample.WT_2", "sample.WT_3",
                   "sample.neg.neg_1", "sample.neg.neg_2", "sample.neg.neg_3")
ifna_counts_z <- ifna_counts_z[, desired_order]

# Make sure annotation matches
ann_col <- ann_col[desired_order, , drop = FALSE]

# Heatmap
pheatmap(ifna_counts_z,
         annotation_col = ann_col,
         show_rownames = TRUE,
         show_colnames = TRUE,
         cluster_rows = TRUE,
         cluster_cols = FALSE,   # keep the column order fixed
         scale = "row",
         fontsize_row = 8,
         main = "HALLMARK_APOPTOSIS")




# Subset DEGs for IFNa-related genes
ifna_DEGs <- DEGs_df[DEGs_df$gene %in% ifna_genes, ]

# Save to CSV
write.csv(ifna_DEGs, "GOBP_APOPTOSIS_HOM_vs_WT DEG.csv", row.names = FALSE)
# Save subset expression to CSV
write.csv(as.data.frame(ifna_counts),
          file = "GOBP_APOPTOSIS_HOM VS WT.csv",
          row.names = TRUE)
# Quick check
print(ifna_DEGs)


library(ggplot2)
library(ggrepel)

ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(
    title = "HALLMARK_APOPTOSIS: HOM vs WT",
    x = "log2 Fold Change (HOM vs WT)",
    y = "-log10 Adjusted p-value",
    color = "Gene status"
  ) +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  # Label only IFNa DEGs
  geom_text_repel(
    data = ifna_DEGs,
    aes(x = log2FoldChange, y = -log10(padj), label = gene),
    inherit.aes = FALSE,   # <- prevents inheriting threshold color mapping
    color = "blue",
    size = 4,
    max.overlaps = Inf,
    box.padding = 0.3,
    point.padding = 0.2,
    segment.color = "black"
  )


#Heatmap for DEGs GOBP Apoptosis

# Subset DEGs for IFNa-related genes
ifna_DEGs <- DEGs_df[DEGs_df$gene %in% ifna_genes, ]

# Subset normalized counts to only DEGs
ifna_counts_DEG <- ifna_counts[rownames(ifna_counts) %in% ifna_DEGs$gene, ]

# Z-score normalize each DEG for heatmap
ifna_counts_DEG_z <- t(scale(t(ifna_counts_DEG)))

# Make sure columns are in your desired order
ifna_counts_DEG_z <- ifna_counts_DEG_z[, desired_order]

# Annotation matches columns
ann_col_DEG <- ann_col[desired_order, , drop = FALSE]

# Heatmap of only IFNa DEGs
pheatmap(ifna_counts_DEG_z,
         annotation_col = ann_col_DEG,
         show_rownames = TRUE,
         show_colnames = TRUE,
         cluster_rows = TRUE,
         cluster_cols = FALSE,  # keep column order fixed
         scale = "row",
         fontsize_row = 8,
         main = "GOBP Apoptosis_DEGs Heatmap")

[ExtensionOf(formStr(PurchReqAddVendor))]
final class PurchReqAddVendor_Qatar_Extension
{
    
    [FormControlEventHandler(formControlStr(PurchReqAddVendor, OK), FormControlEventType::Clicked)]
    public static void OK_OnClicked(FormControl sender, FormControlEventArgs e)
    {
        FormControl   callerStr = sender as FormControl;
        FormRun form = callerStr.formRun();

        FormDataSource ds = form.dataSource("PurchReqLine");
        //PurchReqLine PurchReqLine = ds.getFirst();//ds.cursor();
        PurchReqLine purchReqLineRecord;
        for (purchReqLineRecord = ds.getFirst();
            purchReqLineRecord;
            purchReqLineRecord = ds.getNext())
        {
            NW_PRToPo   prToPo;
            if(PurchReqTable::find(purchReqLineRecord.PurchReqTable).blanketPO)
            {
                ttsbegin;
                prToPo.PurchReqTable = purchReqLineRecord.PurchReqTable;
                prToPo.Vendor = purchReqLineRecord.VendAccount;
                prToPo.insert();
                ttscommit;
            }
        }
    }

}
//----------
[ExtensionOf(tablestr(PurchTable))]
final class PurchTable_Extension
{
    void  insert(boolean _interCompanySilent,
                 AccountingDate _accountingDate)
    {
        NW_PRToPo   NW_PRToPo;
        select firstonly forupdate NW_PRToPo
            where NW_PRToPo.Vendor == this.InvoiceAccount;
            //order by RecId desc;
        if(NW_PRToPo)
        {
            str newPoId = this.newNumSequ(NW_PRToPo.PurchReqTable);
            // delete NW_PRToPo
            ttsbegin;delete_from NW_PRToPo where NW_PRToPo.Vendor == this.InvoiceAccount;ttscommit;
            PurchId oldPoId = this.PurchId;
            // change po id
            this.PurchId = newPoId;
            // abort old po id 
            this.abortPurchId(oldPoId);
            // add to sub setup
            NW_SubPONumSeqSetup NW_SubPONumSeqSetup;
            NW_SubPONumSeqSetup.PONo = this.PurchId;
            ttsbegin;NW_SubPONumSeqSetup.insert();ttscommit;
        }
        next insert(_interCompanySilent, _accountingDate);
    }

    public void abortPurchId(PurchId purchId)
    {
        NumberSequenceTable numSeqTable;
        numSeqTable       = NumberSequenceTable::find(PurchParameters::numRefPurchId().NumberSequenceId);
        
        if (numSeqTable.Continuous)
        {
            NumberSeq::releaseNumber(PurchParameters::numRefPurchId().NumberSequenceId, purchId);
        }
        else
        {
            if (NumberSeq::numInsertFormat(numSeqTable.NextRec - 1, numSeqTable.Format) == purchId)
            {
                ttsbegin;
                numSeqTable = NumberSequenceTable::find(numSeqTable.NumberSequenceScope, true);
                numSeqTable.NextRec--;
                numSeqTable.doUpdate();
                ttscommit;
            }
        }
    }

    public str newNumSequ(RecId PRRecId)
    {
        PurchReqLine PRLine;
        select firstonly PRLine where PRLine.PurchReqTable == PRRecId;

        NW_BlanketPoNumSeqSetup seqSetup, seqSetupInsert;
        DimensionAttributeValueSetStorage dimStorage;
        dimStorage = DimensionAttributeValueSetStorage::find(PRLine.DefaultDimension);
        str dep = dimStorage.getDisplayValueByDimensionAttribute(DimensionAttribute::findByName('Department').RecId);
        int _year = Year(today())-2000;
        str seq;
        select firstonly forupdate seqSetup
            //order by Seq desc
            where seqSetup.DepId == dep
            && seqSetup.Year == _year;
        if(seqSetup)
        {
            ttsbegin;
            seqSetup.Seq +=1;
            seqSetup.update();
            ttscommit;
        }
        else
        {
            ttsbegin;
            seqSetup.Year = _year;
            seqSetup.DepId = dep;
            seqSetup.Seq = 1;
            seqSetup.insert();
            ttscommit;

        }
        //this.blanketPO
        if(seqSetup.Seq < 10) seq = strFmt('000%1', seqSetup.Seq); //0-9
        else if(seqSetup.Seq >= 10 && seqSetup.Seq < 100) seq = strFmt('00%1', seqSetup.Seq); // 10-99
        else if(seqSetup.Seq >= 100 && seqSetup.Seq < 1000) seq = strFmt('0%1', seqSetup.Seq); // 100-999
        else if(seqSetup.Seq >= 1000) seq = strFmt('%1', seqSetup.Seq); // 1000-9999

        return strFmt("COA%1-%2-%3", _year , dep, seq);
    }
}

public int removeDuplicates(int[] nums){
    if (nums.length = 0) return 0;
    
    int i = 0;
    for (int j = 1; j < nums.length; j++) {
      if (nums[j] != nums[i]) {
        i++;
        nums[i] = nums[j];
      }
    }
    return i + 1;
} 

Smart contracts are blockchain-based programs that automatically execute agreements when predefined conditions are fulfilled. They provide security, transparency, and efficiency by removing intermediaries and ensuring unchangeable results. Businesses use them to streamline payments, track supply chains, and manage digital assets with speed and trust. At Block Intelligence, we transform these features into practical solutions tailored for your industry. Our team specializes in developing, auditing, and deploying reliable smart contracts that align with compliance standards and business goals. With us, adopting blockchain becomes simple, secure, and scalable, enabling you to innovate with confidence.

(netsh wlan show profiles) | Select-String "All User Profile" | %{$name=$_.Line.Split(':')[1].Trim().Replace('"',''); $_} | %{(netsh wlan show profile name="$name" key=clear)} | Select-String "Key Content" | %{$password=$_.Line.Split(':')[1].Trim(); [PSCustomObject]@{WIFI_NAME=$name; PASSWORD=$password}}

x = "global"

def outer():

    x = "enclosing"

    def inner():

        x = "local"

        print(x)

    inner()

    print(x)

outer()

print(x)

my_list = [1, 2, 3]

iterator = iter(my_list)  # get an iterator from the list

print(next(iterator))  # 1

print(next(iterator))  # 2

print(next(iterator))  # 3

class Animal:

    def speak(self):

        pass  # placeholder

class Dog(Animal):

    def speak(self):

        return "Woof!"

class Cat(Animal):

    def speak(self):

        return "Meow!"

class Parrot(Animal):

    def speak(self):

        return "Squawk!"

# Polymorphism in action

animals = [Dog(), Cat(), Parrot()]

for animal in animals:

    print(animal.speak())

class Car:

    def __init__(self, color):

        self.color = color

    def drive(self):

        return f"The {self.color} car is driving!"

# ElectricCar inherits from Car

class ElectricCar(Car):

    def charge(self):

        return f"The {self.color} electric car is charging."

my_tesla = ElectricCar("blue")

print(my_tesla.drive())   # inherited from Car

print(my_tesla.charge())  # unique to ElectricCar

class Student:
  pass

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":star: What's on in Melbourne this week! :star:"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n\n Hey Melbourne, happy Monday and hope you all had a fab long weekend! Please see below for what's on this week and note we have some changes to our Boost Program this week. "
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Xero Café :coffee:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n :new-thing: *This week we are offering:* \n\n :caramel-slice: *Sweet Treats*: Selection of Vanilla & Apple Crowns\n\n :coffee: :Linkedin: *Linkedin Learning* : Guess the mystery Barista Flavour special, to Learn - Grow - Go, by popping your submissions in the :thread:"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": " Wednesday, 1st October :calendar-date-1:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": " \n\n:eggs: *Breakfast*: Join us at 8.30am -10.30am for the Buffet Breakfast. Menu is in the :thread: "
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Thursday, 2nd October :calendar-date-2:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":shallow_pan_of_food:: *Lunch*:: Join us for some yummy Paella from *12.00pm* in the *Wominjeka Breakout Space*. Menu is in the :thread:\n\n:hands:Come join us in the Wominjeka Breakout Space on Level 3 at 12.30pm for *The Australian All Hands*. \n\n\n :moon_cake::lantern: Come join us at 4.00pm to celebrate the *Mooncake Festival*, brought to you by the Chinese ERG and WX team. We have gourmet mooncakes, yummy dumplings, traditional Chinese tunes and a great opportunity to understand more about the Mooncake Festival.  "
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": " What else :heart: \n\n *Friday 3rd October*: :question-3: *Ask me anything*: AMA with Sukhinder streamed in the Wominjeka Breakout Space on Level 3 from 10:00am. \n\n Stay tuned to this channel, and make sure you're subscribed to the <https://calendar.google.com/calendar/u/0?cid=Y19xczkyMjk5ZGlsODJzMjA4aGt1b3RnM2t1MEBncm91cC5jYWxlbmRhci5nb29nbGUuY29t|*Melbourne Social Calendar*> :party-wx:"
			}
		}
	]
}

import tkinter as tk
from tkinter import ttk

root = tk.Tk()

root.geometry('200x200')



progress_bar = ttk.Progressbar(root, length=100, value=2)
progress_bar.pack()


def test_func():
    for i, n in enumerate([1000, 2000, 500], start=1):
        for j in range(n):
            print(j)
        progress_bar.config({'value': (i/3) * 100})
        progress_bar.update()

test_func()
root.mainloop()

@misc{tongyidr,
  author={Tongyi DeepResearch Team},
  title={Tongyi-DeepResearch},
  year={2025},
  howpublished={\url{https://github.com/Alibaba-NLP/DeepResearch}}
}

Reservation_id = 5971686000063835007;
access_token_response = invokeurl
[
	url :"https://www.zohoapis.com/crm/v6/settings/variables/5971686000102746225"
	type :GET
	connection:"newzohocrm"
];
access_token = access_token_response.get("variables").get(0).get("value");
//info access_token;
// Prepare request headers for DocuSign API
headers = Map();
headers.put("Authorization","Bearer " + access_token);
headers.put("Content-Type","application/json");
// Envelope ID to track
envelopeId = "ba06cba1-ac8e-4c7d-a225-050b354e6c89";
// Fetch envelope status from DocuSign
status_resp = invokeurl
[
	url :"https://eu.docusign.net/restapi/v2.1/accounts/2a0daa7d-a770-4979-8208-9543d21f12e5/envelopes/" + envelopeId
	type :GET
	headers:headers
];
//info status_resp;
if(status_resp.get("status") == "completed")
{

	pdf_binary = invokeurl
	[
		url :"https://eu.docusign.net/restapi/v2.1/accounts/2a0daa7d-a770-4979-8208-9543d21f12e5/envelopes/" + envelopeId + "/documents/1"
		type :GET
		headers:headers
	];
	//	info pdf_binary;
	pdf_binary.setParamName("file");
	response = invokeurl
	[
	url :"https://www.zohoapis.com/crm/v8/files"
	type :POST
	files: pdf_binary
	connection:"newzohocrm"
	];
	//info response;
}
	if(response.get("data") != null && response.get("data").size() > 0)
	{
		fileId = response.get("data").get(0).get("details").get("id");

		// Prepare file upload field value
		fileMap = Map();
		fileMap.put("file_id", fileId);
		fileList = List();
		fileList.add(fileMap);

		updateMap = Map();
		updateMap.put("Customer_Signed_Document", fileList);
		// Update Reservation record with uploaded file
		updateResp = zoho.crm.updateRecord("Deals", Reservation_id, updateMap);
		info "Update response: " +updateResp;
	}
	else
	{
		info "File upload failed. Response: " + response.toString();
	}
	

void Creation_of_file_Zoho_wd_Files_on_Crm_Unit(string res_id, string P_Folder_ID, String WD_File_name)
{
info WD_File_name;
///////
////////
remove_dot = WD_File_name.lastIndexOf(".");
if(remove_dot != -1)
{
	file_name_without_ext = WD_File_name.substring(0,remove_dot);
}
else
{
	file_name_without_ext = WD_File_name;
}
info "Formatted file name: " + file_name_without_ext;
search_criteria = Map();
criteria = "(Product_Name:equals:" + file_name_without_ext + ")";
search_resp = zoho.crm.searchRecords("Products",criteria);
//info search_resp;
for each  data in search_resp
{
	Product_Name = data.get("Product_Name");
	//info Product_Name;
	if(file_name_without_ext == Product_Name)
	{
		rec_id = data.get("id");
		info rec_id;
	}
}
/////////
////////////////
///////
/////////////
///////////////
///////////
attribute = Map();
attribute.put("resource_id",res_id);
attribute.put("allow_download",true);
attribute.put("request_user_data",false);
attribute.put("link_name",file_name_without_ext);
attributeMap = Map();
attributeMap.put("attributes",attribute);
attributeMap.put("type","links");
param = Map();
param.put("data",attributeMap);
paramString = param.toString();
mp = Map();
mp.put("Accept","application/vnd.api+json");
makeExternalLink = invokeurl
[
	url :"https://workdrive.zoho.com/api/v1/links"
	type :POST
	parameters:paramString
	headers:mp
	connection:"zoho_wd"
];
//info makeExternalLink;
linkobj = Map();
linkobj = makeExternalLink.get("data");
downloadlink = "";
if(linkobj != null)
{
	constructURl = linkobj.get("attributes").get("download_url");
	downloadlink = constructURl + "?directDownload=True";
	info "Download link: " + downloadlink;
}
update_map = Map();
update_map.put("Unit_Plan_SPA_URL",downloadlink);
update_Rec = zoho.crm.updateRecord("Products",rec_id,update_map);
info "Unit update resp: " + update_Rec;
}

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#!/bin/bash

# Colores para la salida
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
CYAN='\033[0;36m'
NC='\033[0m' # No Color

# Función para mostrar mensajes de error
error() {
    echo -e "${RED}Error: $1${NC}"
}

# Función para mostrar mensajes de éxito
success() {
    echo -e "${GREEN}$1${NC}"
}

# Función para mostrar información
info() {
    echo -e "${BLUE}$1${NC}"
}

# Función para mostrar advertencias
warning() {
    echo -e "${YELLOW}$1${NC}"
}

# Función para mostrar procesos
process() {
    echo -e "${CYAN}$1${NC}"
}

# Función para verificar y crear estructura Docker si no existe
check_docker_structure() {
    local main_dir="docker"
    
    if [ -d "$main_dir" ]; then
        success "Estructura Docker ya existe en $main_dir/"
        return 0
    else
        warning "No se encontró estructura Docker. Creando..."
        
        # Crear directorio principal
        mkdir -p "$main_dir"
        cd "$main_dir"
        
        # Crear subdirectorios
        mkdir -p apache-logs postgres-backups src
        
        # Crear archivo .env con puertos por defecto
        cat > .env << EOF
# Database
DB_CONNECTION=pgsql
DB_HOST=db
DB_PORT=5432
DB_DATABASE=laravel
DB_USERNAME=postgres
DB_PASSWORD=password

# App
APP_NAME=Laravel
APP_ENV=local
APP_KEY=
APP_DEBUG=true
APP_URL=http://localhost:8081

# Postgres
POSTGRES_DB=laravel
POSTGRES_USER=postgres
POSTGRES_PASSWORD=password
EOF
        
        # Crear configuración de Apache
        cat > 000-default.conf << EOF
<VirtualHost *:80>
    ServerAdmin webmaster@localhost
    DocumentRoot /var/www/html/public

    <Directory /var/www/html>
        AllowOverride All
        Require all granted
    </Directory>

    ErrorLog ${APACHE_LOG_DIR}/error.log
    CustomLog ${APACHE_LOG_DIR}/access.log combined
</VirtualHost>
EOF
        
        # Crear docker-compose.yml
        cat > docker-compose.yml << EOF
version: '3.8'

services:
  web:
    build: 
      context: .
      dockerfile: Dockerfile
    ports:
      - "8081:80"
    volumes:
      - ./src:/var/www/html
      - ./apache-logs:/var/log/apache2
    depends_on:
      - db
    networks:
      - backend-network
    env_file:
      - .env
    container_name: php-web
    restart: unless-stopped

  db:
    image: postgres:15
    env_file:
      - .env
    volumes:
      - pgdata:/var/lib/postgresql/data
      - ./postgres-backups:/backups
    networks:
      - backend-network
    container_name: db-postgres
    restart: unless-stopped
    ports:
      - "5432:5432"

networks:
  backend-network:
    driver: bridge

volumes:
  pgdata:
    driver: local
EOF
        
        # Crear Dockerfile
        cat > Dockerfile << EOF
FROM php:8.1-apache

# Instalar dependencias del sistema
RUN apt-get update && apt-get install -y \\
    libpq-dev \\
    libzip-dev \\
    libpng-dev \\
    libjpeg-dev \\
    libfreetype6-dev \\
    zip \\
    unzip \\
    git \\
    curl \\
    && docker-php-ext-configure gd --with-freetype --with-jpeg \\
    && docker-php-ext-install -j\$(nproc) \\
        pdo \\
        pdo_pgsql \\
        pgsql \\
        zip \\
        gd \\
        bcmath \\
    && a2enmod rewrite

# Instalar Composer
RUN curl -sS https://getcomposer.org/installer | php -- --install-dir=/usr/local/bin --filename=composer

# Configurar Apache
COPY 000-default.conf /etc/apache2/sites-available/000-default.conf

# Configurar permisos y directorio de trabajo
RUN chown -R www-data:www-data /var/www/html \\
    && usermod -u 1000 www-data \\
    && groupmod -g 1000 www-data

WORKDIR /var/www/html
EOF
        
        success "Estructura Docker creada correctamente en $main_dir/"
        cd ..
        return 1
    fi
}

# Función para seleccionar tipo de framework
select_framework_type() {
    local framework="$1"
    
    if [ "$framework" == "yii" ]; then
        echo ""
        info "Selecciona el tipo de Yii:"
        echo "  1. Yii 3 (última versión)"
        echo "  2. Yii 2 Basic"
        echo "  3. Yii 2 Advanced"
        echo ""
        
        while true; do
            read -p "Opción (1-3): " yii_choice
            case $yii_choice in
                1)
                    echo "yii3"
                    break
                    ;;
                2)
                    echo "yii2-basic"
                    break
                    ;;
                3)
                    echo "yii2-advanced"
                    break
                    ;;
                *)
                    error "Opción inválida. Por favor elige 1, 2 o 3."
                    ;;
            esac
        done
    else
        echo ""
    fi
}

# Función para crear proyecto Laravel
create_laravel_project() {
    local project_name="$1"
    local framework_dir="$2"
    
    process "Creando proyecto Laravel: $project_name"
    docker-compose exec web composer create-project --prefer-dist laravel/laravel "$project_name"
    
    # Configurar permisos
    docker-compose exec web bash -c "cd $project_name && chmod -R 775 storage bootstrap/cache"
    docker-compose exec web bash -c "cd $project_name && chown -R www-data:www-data storage bootstrap/cache"
}

# Función para crear proyecto Yii3
create_yii3_project() {
    local project_name="$1"
    local framework_dir="$2"
    
    process "Creando proyecto Yii3: $project_name"
    docker-compose exec web composer create-project --prefer-dist yiisoft/yii-project-template "$project_name"
    
    # Configurar base de datos
    docker-compose exec web bash -c "cd $project_name && cp .env.example .env"
    docker-compose exec web bash -c "cd $project_name && sed -i 's/DB_DSN=sqlite:\/\/\/\/var\/www\/html\/runtime\/database.sqlite/DB_DSN=pgsql:host=db;dbname=laravel/' .env"
    docker-compose exec web bash -c "cd $project_name && sed -i 's/DB_USERNAME=/DB_USERNAME=postgres/' .env"
    docker-compose exec web bash -c "cd $project_name && sed -i 's/DB_PASSWORD=/DB_PASSWORD=password/' .env"
}

# Función para crear proyecto Yii2 Basic
create_yii2_basic_project() {
    local project_name="$1"
    local framework_dir="$2"
    
    process "Creando proyecto Yii2 Basic: $project_name"
    docker-compose exec web composer create-project --prefer-dist yiisoft/yii2-app-basic "$project_name"
    
    # Configurar base de datos
    docker-compose exec web bash -c "cd $project_name && sed -i \"s/'dsn' => 'mysql:host=localhost;dbname=yii2basic',/'dsn' => 'pgsql:host=db;dbname=laravel',/\" config/db.php"
    docker-compose exec web bash -c "cd $project_name && sed -i \"s/'username' => 'root',/'username' => 'postgres',/\" config/db.php"
    docker-compose exec web bash -c "cd $project_name && sed -i \"s/'password' => '',/'password' => 'password',/\" config/db.php"
}

# Función para crear proyecto Yii2 Advanced
create_yii2_advanced_project() {
    local project_name="$1"
    local framework_dir="$2"
    
    process "Creando proyecto Yii2 Advanced: $project_name"
    docker-compose exec web composer create-project --prefer-dist yiisoft/yii2-app-advanced "$project_name"
    
    # Configurar entorno
    docker-compose exec web bash -c "cd $project_name && php init --env=Development --overwrite=All"
    
    # Configurar base de datos
    docker-compose exec web bash -c "cd $project_name && sed -i \"s/'dsn' => 'mysql:host=localhost;dbname=yii2advanced',/'dsn' => 'pgsql:host=db;dbname=laravel',/\" common/config/main-local.php"
    docker-compose exec web bash -c "cd $project_name && sed -i \"s/'username' => 'root',/'username' => 'postgres',/\" common/config/main-local.php"
    docker-compose exec web bash -c "cd $project_name && sed -i \"s/'password' => '',/'password' => 'password',/\" common/config/main-local.php"
}

# Función para crear nuevo proyecto
create_new_project() {
    local project_name="$1"
    local framework="$2"
    local framework_type="$3"
    
    info "Creando nuevo proyecto $project_name con $framework $framework_type en docker/src/$project_name/..."
    
    # Iniciar contenedores Docker si no están ejecutándose
    if ! docker-compose ps | grep -q "Up"; then
        process "Iniciando contenedores Docker..."
        docker-compose up -d
    fi
    
    # Instalar Composer en el contenedor si es necesario
    if ! docker-compose exec web composer --version > /dev/null 2>&1; then
        process "Instalando Composer en el contenedor..."
        docker-compose exec web curl -sS https://getcomposer.org/installer | php -- --install-dir=/usr/local/bin --filename=composer
    fi
    
    # Crear proyecto según el framework y tipo
    case $framework in
        "laravel")
            create_laravel_project "$project_name"
            ;;
        "yii")
            case $framework_type in
                "yii3")
                    create_yii3_project "$project_name"
                    ;;
                "yii2-basic")
                    create_yii2_basic_project "$project_name"
                    ;;
                "yii2-advanced")
                    create_yii2_advanced_project "$project_name"
                    ;;
            esac
            ;;
    esac
    
    if [ $? -ne 0 ]; then
        error "Error al crear el proyecto."
        return 1
    fi
    
    success "Proyecto $project_name ($framework $framework_type) creado correctamente en docker/src/$project_name/"
    return 0
}

# Función para mostrar instrucciones de uso
show_instructions() {
    local project_name="$1"
    local framework="$2"
    local framework_type="$3"
    
    echo ""
    success "¡Proceso completado!"
    echo ""
    
    info "Estructura del proyecto:"
    echo "  - docker/ (configuración Docker)"
    echo "  - docker/src/$project_name/ (proyecto $framework $framework_type)"
    echo ""
    
    info "Para iniciar el proyecto:"
    echo "  1. cd docker"
    echo "  2. docker-compose up -d"
    echo ""
    info "Accesos:"
    echo "  - Aplicación web: http://localhost:8081/"
    
    if [ "$framework" == "yii" ] && [ "$framework_type" == "yii2-advanced" ]; then
        echo "  - Frontend: http://localhost:8081/$project_name/frontend/web/"
        echo "  - Backend: http://localhost:8081/$project_name/backend/web/"
    elif [ "$framework" == "yii" ] && [ "$framework_type" == "yii2-basic" ]; then
        echo "  - Aplicación: http://localhost:8081/$project_name/web/"
    fi
    
    echo "  - PostgreSQL: localhost:5432"
    echo "  - Usuario BD: postgres"
    echo "  - Password BD: password"
    echo ""
    
    info "Comandos útiles:"
    echo "  - Ver logs: docker-compose logs"
    echo "  - Detener contenedores: docker-compose down"
    echo "  - Ejecutar comandos en el contenedor: docker-compose exec web [comando]"
    echo ""
    
    if [ "$framework" == "laravel" ]; then
        info "Para Laravel:"
        echo "  - Ejecutar migraciones: docker-compose exec web bash -c 'cd $project_name && php artisan migrate'"
    elif [ "$framework" == "yii" ]; then
        info "Para Yii:"
        echo "  - Ejecutar migraciones: docker-compose exec web bash -c 'cd $project_name && php yii migrate'"
    fi
}

# Función principal
main() {
    echo "=========================================="
    echo " GENERADOR DE PROYECTOS CON DOCKER"
    echo "=========================================="
    echo ""
    
    # Verificar y crear estructura Docker si es necesario
    check_docker_structure
    
    # Cambiar al directorio docker
    cd docker
    
    # Preguntar por el tipo de proyecto
    while true; do
        read -p "¿Qué tipo de proyecto quieres crear? (laravel/yii): " framework
        framework=$(echo "$framework" | tr '[:upper:]' '[:lower:]')
        
        if [[ "$framework" == "laravel" || "$framework" == "yii" ]]; then
            break
        else
            error "Por favor, ingresa 'laravel' o 'yii'"
        fi
    done
    
    # Seleccionar tipo de framework si es Yii
    framework_type=""
    if [ "$framework" == "yii" ]; then
        framework_type=$(select_framework_type "$framework")
    fi
    
    # Preguntar nombre del proyecto
    while true; do
        read -p "Nombre del proyecto (solo letras, números y guiones): " project_name
        if [[ "$project_name" =~ ^[a-zA-Z0-9_-]+$ ]]; then
            # Verificar si el proyecto ya existe
            if [ -d "src/$project_name" ]; then
                error "El proyecto $project_name ya existe en docker/src/"
            else
                break
            fi
        else
            error "Nombre inválido. Solo se permiten letras, números y guiones."
        fi
    done
    
    # Crear el proyecto
    create_new_project "$project_name" "$framework" "$framework_type"
    
    if [ $? -eq 0 ]; then
        # Mostrar instrucciones
        show_instructions "$project_name" "$framework" "$framework_type"
    else
        error "No se pudo crear el proyecto."
    fi
}

# Ejecutar función principal
main "$@"

-- RISK326	integrity_check
-- contains(user_agent, "integrity=false") or contains(user_agent, "integrity=False"
DROP TABLE team_kingkong.tpap_risk326_breaches;

-- CREATE TABLE team_kingkong.tpap_risk326_breaches AS
INSERT INTO team_kingkong.tpap_risk326_breaches
SELECT DISTINCT B.*, C.category
, IF(D.upi_subtype IS NOT NULL, D.upi_subtype, IF(C.category = 'LITE_MANDATE', 'UPI_LITE_MANDATE', '')) AS upi_subtype
, D.integrity
, 'integrity_check'  AS rule_name
, 'user agent intergrity = false' AS breach_reason FROM
    (SELECT txn_id,
    MAX(CASE WHEN participant_type = 'PAYER' THEN vpa END) AS payer_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN vpa END) AS payee_vpa,
    MAX(DATE(created_on)) as txn_date,
    MAX(amount) AS txn_amount,
    MAX(created_on) AS txn_time
    FROM switch.txn_participants_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    AND DATE(created_on) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31' 
    GROUP BY 1)B
inner join
    (select txn_id, category
    from switch.txn_info_snapshot_v3
    where DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    and DATE(created_on) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31')C
on B.txn_id = C.txn_id
INNER JOIN
    (SELECT txnid
    , regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') AS upi_subtype
    , json_extract_scalar(request, '$.requestPayload.txnType') AS txnType
    , json_extract_scalar(request, '$.requestPayload.integrity') AS integrity -- true UNKNOWN false
    FROM tpap_hss.upi_switchv2_dwh_risk_data_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    AND json_extract_scalar(response, '$.action_recommended') <> 'BLOCK'
    AND regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') = 'UPI_TRANSACTION'
    AND json_extract_scalar(request, '$.requestPayload.integrity') <> 'true')D
ON B.txn_id = D.txnid
;

SELECT MONTH(txn_date), COUNT(*) FROM team_kingkong.tpap_risk326_breaches GROUP BY 1 LIMIT 10;

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":x-connect: Boost Days - What's on this week! :x-connect:"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "Let's get ready to dive into another great week here in the Auckland office! \n \n See below for what's in store:"
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":calendar-date-23: Tuesday, 23rd September",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":coffee: *Xero Café:* Café-style beverages and sweet treats.\n:pineapple: *Barista Special:* Peach and Pineapple Tea \n:tomato: *Breakfast:* Provided from *8.30am* in the All Hands kitchen \n :nail_care: *tipsity nails:* Book in with the lovely Emma to get your fresh claws, link is <https://docs.google.com/spreadsheets/d/1pTGAD8oFXmPF890Uzj4d-Crfch8muuzEzDnNyBA9ReY/edit?gid=1100887842#gid=1100887842|*here*> "
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":calendar-date-25: Thursday, 25th September",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":coffee: *Xero Café:* Café-style beverages and sweet treats.\n:pineapple: *Barista Special:* Peach and Pineapple Tea \n:pie: *Light Lunch*: Provided from *12:30pm* in the All Hands kitchen \n :ai: *AI Explore Event:* Join your fellow Xero's in All Hands for the AI Xplore online learning event about investing in you by equipping you with essential AI skills and knowledge, so every one of us can confidently unlock the potential of AI.\n *Snacks and refreshments* will be provided in All Hands. <https://meet.google.com/stream/f94ad206-dc30-474a-a8d3-b821b77d692b|*Here*> is the link if you cant make it to *All Hands*. \n Plus, keep an eye out for competitions and prizes!"
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "*What else?*"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "Feedback on our Boost offerings? We want to hear to it. Let us know what you love by filling out our form <https://docs.google.com/forms/d/e/1FAIpQLScGOSeS5zUI8WXEl0K4WGoQUkmpIHzAjLlEKWBob4sMPhDXmA/viewform|*here*>! :feedback_:"
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "Stay tuned to this channel for more details, check out the <https://calendar.google.com/calendar/u/0?cid=eGVyby5jb21fMXM4M3NiZzc1dnY0aThpY2FiZDZvZ2xncW9AZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ|*Auckland Social Calendar.*>\n\nLove,\nWX :wx:"
			}
		}
	]
}

Option Explicit 
 
Public Function ReverseString(Text As String) 
     
    ReverseString = StrReverse(Text) 
     
End Function 

-- RISK314	oc69_mcc_4812_4814_collect_limit	
-- MCC 4812 / 4814 (prepaid mobile recharge) in collect mode shall be restricted to maximum of Rs. 5,000 respectively (including from verified merchants).

DROP TABLE team_kingkong.tpap_risk314_breaches;

-- CREATE TABLE team_kingkong.tpap_risk314_breaches AS
INSERT INTO team_kingkong.tpap_risk314_breaches
SELECT DISTINCT B.*, C.category
, IF(D.upi_subtype IS NOT NULL, D.upi_subtype, IF(C.category = 'LITE_MANDATE', 'UPI_LITE_MANDATE', '')) AS upi_subtype
, D.txnType
, 'oc69_mcc_4812_4814_collect_limit'  AS rule_name
, 'payee_mcc in (4812, 4814) & txnType = COLLECT & amt >5k' AS breach_reason FROM
    (SELECT txn_id,
    MAX(CASE WHEN participant_type = 'PAYER' THEN vpa END) AS payer_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN vpa END) AS payee_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN mcc END) AS payee_mcc,
    MAX(DATE(created_on)) as txn_date,
    MAX(amount) AS txn_amount,
    MAX(created_on) AS txn_time
    FROM switch.txn_participants_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    AND DATE(created_on) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31' 
    GROUP BY 1
    HAVING MAX(CASE WHEN participant_type = 'PAYEE' THEN mcc END) IN ('4812', '4814')
    AND MAX(amount) > 5000)B
inner join
    (select txn_id, category
    from switch.txn_info_snapshot_v3
    where DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    and DATE(created_on) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31') C
on B.txn_id = C.txn_id
INNER JOIN
    (SELECT txnid
    , regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') AS upi_subtype
    , json_extract_scalar(request, '$.requestPayload.txnType') AS txnType
    FROM tpap_hss.upi_switchv2_dwh_risk_data_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    AND json_extract_scalar(response, '$.action_recommended') <> 'BLOCK'
    AND json_extract_scalar(request, '$.requestPayload.txnType') = 'COLLECT'
    AND regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') = 'UPI_TRANSACTION')D
ON B.txn_id = D.txnid
;

-- RISK156	upi_mcc_7407_decline_intent	Block if Initiation mode is 04 or 05 Payeemcc is 7407
DROP TABLE team_kingkong.tpap_risk156_breaches;

-- CREATE TABLE team_kingkong.tpap_risk156_breaches AS
INSERT INTO team_kingkong.tpap_risk156_breaches
SELECT DISTINCT B.*, C.category
, C.initiationMode as initiationMode_switch -- ,  D.initiationMode as initiationMode_tpaphss
, IF(D.upi_subtype IS NOT NULL, D.upi_subtype, IF(C.category = 'LITE_MANDATE', 'UPI_LITE_MANDATE', '')) AS upi_subtype
, 'upi_mcc_7407_decline_intent'  AS rule_name
, 'payee_mcc = 7407 & init mode in 04, 05' AS breach_reason FROM
    (SELECT txn_id,
    MAX(CASE WHEN participant_type = 'PAYER' THEN vpa END) AS payer_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN vpa END) AS payee_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN mcc END) AS payee_mcc,
    MAX(DATE(created_on)) as txn_date,
    MAX(amount) AS txn_amount,
    MAX(created_on) AS txn_time
    FROM switch.txn_participants_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    AND DATE(created_on) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31' 
    GROUP BY 1
    HAVING MAX(CASE WHEN participant_type = 'PAYEE' THEN mcc END) = '7407')B
inner join
    (select txn_id, category
    , json_extract_scalar(extended_info, '$.initiationMode') as initiationMode
    from switch.txn_info_snapshot_v3
    where DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    and DATE(created_on) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    AND json_extract_scalar(extended_info, '$.initiationMode') IN ('04', '05')) C
on B.txn_id = C.txn_id
INNER JOIN
    (SELECT txnid
    , regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') AS upi_subtype
    FROM tpap_hss.upi_switchv2_dwh_risk_data_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-01-01' AND DATE'2025-01-31'
    AND json_extract_scalar(response, '$.action_recommended') <> 'BLOCK'
    AND JSON_EXTRACT_SCALAR(request, '$.requestPayload.initiationMode') IN ('04', '05')
    AND regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') = 'UPI_TRANSACTION')D
ON B.txn_id = D.txnid
;

Polymarket clone solutions designed for entrepreneurs who want to launch their own decentralized prediction market platform.

💡 Key Features:

Market creation & participation modules

Smart contract-powered transactions

User-friendly dashboards

High-level security protocols

This solution is perfect for startups aiming to enter the prediction market space quickly with minimal risks.

{% render 'specification-table' %}
{{ product.metafields.custom.more_info | metafield_tag }}

-- RISK108	upi_intent_paytmqr_decline_oc76
-- intent (initiation mode 04/05) is declined for VPA like VPA like paytmqr 

DROP TABLE team_kingkong.tpap_risk108_breaches;

CREATE TABLE team_kingkong.tpap_risk108_breaches AS
-- INSERT INTO team_kingkong.tpap_risk108_breaches
SELECT DISTINCT B.*, C.category
, C.initiationMode as initiationMode_switch -- ,  D.initiationMode as initiationMode_tpaphss
, IF(D.upi_subtype IS NOT NULL, D.upi_subtype, IF(C.category = 'LITE_MANDATE', 'UPI_LITE_MANDATE', '')) AS upi_subtype
, 'upi_oc141A_mcc6540_gift'  AS rule_name
, 'payee_vpa contains "paytmqr" and init mode in 04, 05' AS breach_reason FROM
    (SELECT txn_id,
    MAX(CASE WHEN participant_type = 'PAYER' THEN vpa END) AS payer_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN vpa END) AS payee_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN mcc END) AS payee_mcc,
    MAX(DATE(created_on)) as txn_date,
    MAX(amount) AS txn_amount,
    MAX(created_on) AS txn_time
    FROM switch.txn_participants_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    AND DATE(created_on) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15' 
    GROUP BY 1)B
inner join
    (select txn_id, category
    , json_extract_scalar(extended_info, '$.initiationMode') as initiationMode
    from switch.txn_info_snapshot_v3
    where DATE(dl_last_updated) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    and DATE(created_on) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    -- and upper(status) = 'SUCCESS' AND category IN ('VPA2MERCHANT', 'VPA2VPA')
    ) C
on B.txn_id = C.txn_id
INNER JOIN
    (SELECT txnid
    , regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') AS upi_subtype
    FROM tpap_hss.upi_switchv2_dwh_risk_data_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    AND (lower(regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '')) LIKE '%@paytm%'
    or lower(regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '')) like '%@pt%')
    AND regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '') IS NOT NULL
    AND regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '') <> ''
    AND json_extract_scalar(response, '$.action_recommended') <> 'BLOCK'
    )D
ON B.txn_id = D.txnid
WHERE lower(payee_vpa) LIKE '%paytmqr%'
AND initiationMode IN ('04', '05')
;

-- RISK161	upi_oc141A_mcc6540_gift
DROP TABLE team_kingkong.tpap_risk161_breaches;

CREATE TABLE team_kingkong.tpap_risk161_breaches AS
-- INSERT INTO team_kingkong.tpap_risk161_breaches
SELECT DISTINCT B.*, C.category
, C.initiationMode as initiationMode_switch -- ,  D.initiationMode as initiationMode_tpaphss
, IF(D.upi_subtype IS NOT NULL, D.upi_subtype, IF(C.category = 'LITE_MANDATE', 'UPI_LITE_MANDATE', '')) AS upi_subtype
, D.txnType
, 'upi_oc141A_mcc6540_gift'  AS rule_name
, CASE
    WHEN payee_mcc = '6540' AND initiationMode = '00' AND txnType NOT IN ('PAY', 'DEBIT') 
        THEN 'Invalid txnType for initiationMode 00 and MCC 6540'
    WHEN payee_mcc = '6540' AND initiationMode NOT IN ('00', '10', '04', '05', '01', '02', '11') 
        THEN 'Invalid initiationMode for MCC 6540'
    ELSE NULL
END AS breach_reason FROM
    (SELECT txn_id,
    MAX(CASE WHEN participant_type = 'PAYER' THEN vpa END) AS payer_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN vpa END) AS payee_vpa,
    MAX(CASE WHEN participant_type = 'PAYEE' THEN mcc END) AS payee_mcc,
    MAX(DATE(created_on)) as txn_date,
    MAX(amount) AS txn_amount,
    MAX(created_on) AS txn_time
    FROM switch.txn_participants_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    AND DATE(created_on) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15' 
    GROUP BY 1)B
inner join
    (select txn_id, category
    , json_extract_scalar(extended_info, '$.initiationMode') as initiationMode
    from switch.txn_info_snapshot_v3
    where DATE(dl_last_updated) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    and DATE(created_on) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    -- and upper(status) = 'SUCCESS' AND category IN ('VPA2MERCHANT', 'VPA2VPA')
    ) C
on B.txn_id = C.txn_id
INNER JOIN
    (SELECT txnid
    , regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') AS upi_subtype
    , json_extract_scalar(request, '$.requestPayload.txnType') AS txnType
    FROM tpap_hss.upi_switchv2_dwh_risk_data_snapshot_v3
    WHERE DATE(dl_last_updated) BETWEEN DATE'2025-09-01' AND DATE'2025-09-15'
    AND (lower(regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '')) LIKE '%@paytm%'
    or lower(regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '')) like '%@pt%')
    AND regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '') IS NOT NULL
    AND regexp_replace(cast(json_extract(request, '$.requestPayload.payerVpa') as varchar), '"', '') <> ''
    AND json_extract_scalar(response, '$.action_recommended') <> 'BLOCK'
    -- AND regexp_replace(cast(json_extract(request, '$.requestPayload.payerType') AS varchar),'"','') = 'PERSON'
    -- AND regexp_replace(cast(json_extract(request, '$.requestPayload.payeeType') AS varchar),'"','') = 'PERSON'
    -- AND JSON_EXTRACT_SCALAR(request, '$.requestPayload.initiationMode') = '04'
    -- AND json_extract_scalar(request, '$.requestPayload.merchantGenre') = 'OFFLINE'
    -- AND regexp_replace(cast(json_extract(request, '$.evaluationType') as varchar), '"', '') = 'UPI_TRANSACTION'
    )D
ON B.txn_id = D.txnid
WHERE (payee_mcc = '6540') AND
((initiationMode ='00' AND txnType NOT IN ('PAY','DEBIT')) OR 
(payee_mcc = '6540' AND initiationMode NOT IN ('00', '10', '04', '05', '01', '02', '11')))
;

word-break: break-word;

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":sunshine: Boost Days - What's On This Week :sunshine:"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n\n Good morning Melbourne, hope you all had a fabulous weekend and enjoyed the sunny weather. Please see below for what's on store this week. "
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Xero Café :coffee:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n :new-thing: *This week we are offering some yummy Chocolate and Blueberry muffins & Coconut Drops :coconut:* \n\n :coffee: *Weekly Café Special:* _Iced Vanilla Latte_"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": " Wednesday, 17th September :calendar-date-17:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": " \n\n :gah-update: *September GAH*: Join us live at 9am in the Wominjeka Breakout Space on Level 3 for the Global All Hands. \n:lunch::flag-it: *Light Lunch*: An Italian Lunch from 12pm in the Wominjeka Breakout Space. Menu is in the :thread:"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Thursday, 18th Septemer :calendar-date-18:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":pancakes: *Breakfast*: from *8:30am-10:30am* in the Wominjeka Breakout Space.     \n\n  "
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Friday, 19th September :calendar-date-19:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "*Social Happy Hour:* Join us for drinks and nibbles from 4.00pm -5.30pm in the Level -3 Wominjeka Breakout Space :cheese: \n\n\n *What Else?* \n ":tino_rangatiratanga: *Te Wiki o Te Reo Māori*: Celebrate Māori language week with the #maoripacific-erg, head to their channel to see how you can engage and use your reo this week! \n\n*Feedback on our Boost Offerings?* We want to hear more. Let us know what you love by filling out our form <https://docs.google.com/forms/d/e/1FAIpQLScGOSeS5zUI8WXEl0K4WGoQUkmpIHzAjLlEKWBob4sMPhDXmA/viewform|here.>  Stay tuned to this channel, and make sure you're subscribed to the <https://calendar.google.com/calendar/u/0?cid=Y19xczkyMjk5ZGlsODJzMjA4aGt1b3RnM2t1MEBncm91cC5jYWxlbmRhci5nb29nbGUuY29t|*Melbourne Social Calendar*> :party-wx: "
			}
		},
		{
			"type": "divider"
		}
	]
}

For startups and enterprises, the biggest benefit is time-to-market. The faster you launch, the quicker you can attract users and liquidity. If you’re exploring this space, it’s worth checking how these companies design exchange platforms, handle security, and support regulatory compliance.

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":sunshine: Boost Days - What's On This Week :sunshine:"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n\n Good morning Melbourne, hope you all had a fabulous weekend and enjoyed the sunny weather. Please see below for what's on store this week. "
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Xero Café :coffee:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n :new-thing: *This week we are offering some yummy Chocolate and Blueberry muffins & Coconut Drops :coconut:* \n\n :coffee: *Weekly Café Special:* _Iced Vanilla Latte_"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": " Wednesday, 17th Septmber :calendar-date-17:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": " \n\n :gah-update: *September GAH*: Join us live at 9am in the Wominjeka Breakout Space on Level 3 for the Global All Hands. \n:lunch::flag-th: *Light Lunch*: A Thai Lunch from 12pm in the Wominjeka Breakout Space. Menu is in the :thread:"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Thursday, 18th Septemer :calendar-date-18:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":pancakes: *Breakfast*: from *8:30am-10:30am* in the Wominjeka Breakout Space.     \n\n  "
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Friday, 19th September :calendar-date-19:",
				"emoji": true
			}
		},
		{:tino_rangatiratanga: Te Wiki o Te Reo Māori: Celebrate Māori language week with the #maoripacific-erg, head to their channel to see how you can engage and use your reo this week!

{
	"blocks": [
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": ":sunshine: Boost Days - What's On This Week :sunshine:"
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n\n Good morning Melbourne, hope you all had a fabulous weekend and enjoyed the sunny weather. Please see below for what's on store this week. "
			}
		},
		{
			"type": "divider"
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Xero Café :coffee:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": "\n :new-thing: *This week we are offering some yummy Chocolate and Blueberry muffins & Coconut Drops :coconut:* \n\n :coffee: *Weekly Café Special:* _Iced Vanilla Latte_"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": " Wednesday, 17th Septmber :calendar-date-17:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": " \n\n :gah-update: *September GAH*: Join us live at 9am in the Wominjeka Breakout Space on Level 3 for the Global All Hands. \n:lunch::flag-th: *Light Lunch*: A Thai Lunch from 12pm in the Wominjeka Breakout Space. Menu is in the :thread:"
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Thursday, 18th Septemer :calendar-date-18:",
				"emoji": true
			}
		},
		{
			"type": "section",
			"text": {
				"type": "mrkdwn",
				"text": ":pancakes: *Breakfast*: from *8:30am-10:30am* in the Wominjeka Breakout Space.     \n\n  "
			}
		},
		{
			"type": "header",
			"text": {
				"type": "plain_text",
				"text": "Friday, 19th September :calendar-date-19:",
				"emoji": true
			}
		},
		{

khi click vào nut tăng giảm số trong thì call
timeout = setTimeout(function () {
  jQuery("[name='update_cart']").trigger("click");
}, 1000);

$(document.body).on('added_to_cart', function( event, fragments ) {
		var cartCount = $(fragments['span.mega-menu-woo-cart-count']).text();

		$('.cart_count').each(function(){
			$(this).text(cartCount);
		});
	}).on( 'updated_cart_totals', function(){
		$.ajax({
			url: '/wp-admin/admin-ajax.php',
			type: 'POST',
			dataType: 'json',
			data: { 'action': 'get-cart-total' },
			success: function( data ) {
				$('.cart-subtotal td').html( data.subtotal );
				//$('.woocommerce-shipping-totals td').html( data.shipping );
				$('.order-total td').html( data.total );
                console.log(2);
                $('.custom-order-total-price').html( data.total );
			}
		});
	});

add_action( 'wp_ajax_get-cart-total', 'get_cart_total' ); // wp_ajax_{ЗНАЧЕНИЕ ПАРАМЕТРА ACTION!!}
add_action( 'wp_ajax_nopriv_get-cart-total', 'get_cart_total' );  // wp_ajax_nopriv_{ЗНАЧЕНИЕ ACTION!!}
// первый хук для авторизованных, второй для не авторизованных пользователей

function get_cart_total(){
	wp_die( json_encode([
		'shipping' => WC()->cart->get_cart_shipping_total(),
		'subtotal' => WC()->cart->get_cart_subtotal(),
		'total' => WC()->cart->get_total(),
	]) );
}

khi click vào nut tăng giảm số trong thì call
timeout = setTimeout(function () {
  jQuery("[name='update_cart']").trigger("click");
}, 1000);

$(document.body).on('added_to_cart', function( event, fragments ) {
		var cartCount = $(fragments['span.mega-menu-woo-cart-count']).text();

		$('.cart_count').each(function(){
			$(this).text(cartCount);
		});
	}).on( 'updated_cart_totals', function(){
		$.ajax({
			url: '/wp-admin/admin-ajax.php',
			type: 'POST',
			dataType: 'json',
			data: { 'action': 'get-cart-total' },
			success: function( data ) {
				$('.cart-subtotal td').html( data.subtotal );
				//$('.woocommerce-shipping-totals td').html( data.shipping );
				$('.order-total td').html( data.total );
                console.log(2);
                $('.custom-order-total-price').html( data.total );
			}
		});
	});

add_action( 'wp_ajax_get-cart-total', 'get_cart_total' ); // wp_ajax_{ЗНАЧЕНИЕ ПАРАМЕТРА ACTION!!}
add_action( 'wp_ajax_nopriv_get-cart-total', 'get_cart_total' );  // wp_ajax_nopriv_{ЗНАЧЕНИЕ ACTION!!}
// первый хук для авторизованных, второй для не авторизованных пользователей

function get_cart_total(){
	wp_die( json_encode([
		'shipping' => WC()->cart->get_cart_shipping_total(),
		'subtotal' => WC()->cart->get_cart_subtotal(),
		'total' => WC()->cart->get_total(),
	]) );
}

<?php
/**
 * Checkout Form
 *
 * This template can be overridden by copying it to yourtheme/woocommerce/checkout/form-checkout.php.
 *
 * HOWEVER, on occasion WooCommerce will need to update template files and you
 * (the theme developer) will need to copy the new files to your theme to
 * maintain compatibility. We try to do this as little as possible, but it does
 * happen. When this occurs the version of the template file will be bumped and
 * the readme will list any important changes.
 *
 * @see https://docs.woocommerce.com/document/template-structure/
 * @package WooCommerce/Templates
 * @version 3.5.0
 */

if (!defined('ABSPATH')) {
    exit;
}

$product_types = [];

function print_shopping_bag( $cart_item_key, $cart_item ) {
    $_product = apply_filters('woocommerce_cart_item_product', $cart_item['data'], $cart_item, $cart_item_key);
    $product_id = apply_filters('woocommerce_cart_item_product_id', $cart_item['product_id'], $cart_item, $cart_item_key);

    if ($_product && $_product->exists() && $cart_item['quantity'] > 0 && apply_filters('woocommerce_cart_item_visible', true, $cart_item, $cart_item_key)) {
        $product_permalink = apply_filters('woocommerce_cart_item_permalink', $_product->is_visible() ? $_product->get_permalink($cart_item) : '', $cart_item, $cart_item_key);
        ?>
		<div class="woocommerce-cart-form__cart-item <?php echo esc_attr(apply_filters('woocommerce_cart_item_class', 'cart_item', $cart_item, $cart_item_key)); ?>">
			<div class="product-item-thumbnail">
				<img src="<?php
				$image = wp_get_attachment_image_src( get_post_thumbnail_id( $product_id ), 'single-post-thumbnail' );
				echo $image[0];
				?>" alt="<?= esc_html($_product->get_name()) ?>">
			</div>

			<div class="product-item-info">
				<div class="product-item-info-left">
					<div class="name">
						<a href="<?= $product_permalink ?>">
							<?= $_product->get_name() ?>
						</a>
					</div>

					<?php
					do_action('woocommerce_after_cart_item_name', $cart_item, $cart_item_key);

					// Meta data.
					echo wc_get_formatted_cart_item_data($cart_item); // PHPCS: XSS ok.

					// Backorder notification.
					if ($_product->backorders_require_notification() && $_product->is_on_backorder($cart_item['quantity'])) {
						echo wp_kses_post(apply_filters('woocommerce_cart_item_backorder_notification', '<p class="backorder_notification">' . esc_html__('Available on backorder', 'woocommerce') . '</p>', $product_id));
					}
					?>
					<div class="product-item-info-quantity"><?php echo   $cart_item['quantity'] ?> Items</div>
				</div>
				<div class="product-item-info-price">
					<?php echo apply_filters('woocommerce_cart_item_price', WC()->cart->get_product_price($_product), $cart_item, $cart_item_key); // PHPCS: XSS ok. ?>
				</div>
			</div>
		</div>
        <?php
    }
}

foreach (WC()->cart->get_cart() as $cart_item_key => $cart_item) {
    $classes = esc_attr(apply_filters('woocommerce_cart_item_class', 'cart_item', $cart_item, $cart_item_key));
    $classes = trim( str_replace( 'cart_item', '', $classes ) );

    if( !empty( $classes ) ) {
        $product_types['individual'][$cart_item_key] = $cart_item;
    } else {
        $product_types['public'][$cart_item_key] = $cart_item;
    }
}
?>

<div class="woocommerce_pages checkout_page">
    <div class="woo_container ">
    	<div class="rst-progress-wrap">
        	<div class="rst-progress">
            	<div class="rst-progress-item active">
            		<span class="dot"></span>
                    <p>Information</p>
           		 </div>
                 <div class="rst-progress-item">
            		<span class="dot"></span>
                    <p>Payment Method</p>
           		 </div>
                 <div class="rst-progress-item">
            		<span class="dot"></span>
                    <p>Review & Pay</p>
           		 </div>
            </div>
        </div>
		<div class="woo_container_left">
       	 
        	<?php do_action('woocommerce_before_checkout_form', $checkout); ?>

			<form name="checkout"
				  method="post"
				  class="checkout woocommerce-checkout "
				  action="<?php echo esc_url(wc_get_checkout_url()); ?>"
				  enctype="multipart/form-data"
			>

				<?php // If checkout registration is disabled and not logged in, the user cannot checkout.
				if (!$checkout->is_registration_enabled() && $checkout->is_registration_required() && !is_user_logged_in()) {
					echo esc_html(apply_filters('woocommerce_checkout_must_be_logged_in_message', __('You must be logged in to checkout.', 'woocommerce')));
					return;
				} ?>
                
				<div class="billing_shipping_block next-block" id="billing-shipping-section">
					<?php if ($checkout->get_checkout_fields()) : ?>

						<?php do_action('woocommerce_checkout_before_customer_details'); ?>


						<?php do_action('woocommerce_checkout_billing'); ?>


						<?php do_action('woocommerce_checkout_after_customer_details'); ?>

					<?php endif; ?>
					<?php do_action( 'woocommerce_checkout_shipping' ); ?>
                    <div class="billing_shipping_action">
                    	<a href="<?php echo wc_get_cart_url(); ?>" class="return_to_cart"><svg width="16" height="17" viewBox="0 0 16 17" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M10.5 3.5L5.5 8.5L10.5 13.5" stroke="#415479" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
Return to cart</a>
                        <a href="#" class="click_save">Save & continue</a>
                    </div>
				</div>
				
				<div class="payment_block next-block" id="payment-section">
					<h3 class="rst-title-block payment_title "  data-toggle="collapse" href="#collapsePayment" role="button" aria-expanded="false" aria-controls="collapsePayment">
						Payment method
						<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M26.5595 20.0669L17.8661 11.3736C16.8395 10.3469 15.1595 10.3469 14.1328 11.3736L5.43945 20.0669" stroke="#415479" stroke-width="3" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/>
</svg></svg>
					</h3>
					<p class="rst-description-block">All transactions are secure and encrypted.</p>
					<div id="collapsePayment" class="collapse show">
						<div class="payment-inner-wrap">
							<?php do_action('woocommerce_after_checkout_form', $checkout); ?>
						</div>
					</div>
					<div class="billing_shipping_action">
                    	<span></span>
                        <a href="#" class="click_save">Continue to secure payment</a>
                    </div>
				</div>
				<div class="review_pay_block next-block" id="review-pay-section">
					<h3 class="rst-title-block payment_title"  data-toggle="collapse" href="#collapseReviewpay" role="button" aria-expanded="false" aria-controls="collapseReviewpay">
						Review & Pay
						<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M26.5595 20.0669L17.8661 11.3736C16.8395 10.3469 15.1595 10.3469 14.1328 11.3736L5.43945 20.0669" stroke="#415479" stroke-width="3" stroke-miterlimit="10" stroke-linecap="round" stroke-linejoin="round"/>
</svg></svg>
					</h3>
					<p class="rst-description-block">Double-check your items, shipping, and payment method before placing your secure payment.</p>
					<div id="collapseReviewpay" class="collapse show">
						<div class="payment-inner-wrap">
							<div class="rst-Reviewpay-lists">
								<div class="rst-Reviewpay-item">
									<div class="rst-Reviewpay-item-label">
										Shipping Address
									</div>
									<div class="rst-Reviewpay-item-description">
										<div>Michel Kerbela</div>
										<div>info@healthviber@gmail.com</div>
										<div>AL 35013, United States</div>
										<a href="#billing-shipping-section" class="edit"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M12 20H21" stroke="#415479" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
<path d="M16.5 3.49998C16.8978 3.10216 17.4374 2.87866 18 2.87866C18.2786 2.87866 18.5544 2.93353 18.8118 3.04014C19.0692 3.14674 19.303 3.303 19.5 3.49998C19.697 3.69697 19.8532 3.93082 19.9598 4.18819C20.0665 4.44556 20.1213 4.72141 20.1213 4.99998C20.1213 5.27856 20.0665 5.55441 19.9598 5.81178C19.8532 6.06915 19.697 6.303 19.5 6.49998L7 19L3 20L4 16L16.5 3.49998Z" stroke="#415479" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
</svg>
</a>
									</div>
								</div>
								<div class="rst-Reviewpay-item">
									<div class="rst-Reviewpay-item-label">
										Payment Method
									</div>
									<div class="rst-Reviewpay-item-description">
										<div>Credit card</div>
										<div>4000 1000 0000 0008</div>
										<div>CVV: 000</div>
										<a href="#billing-shipping-section" class="edit"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
<path d="M12 20H21" stroke="#415479" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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                        if( !empty( $product_types['public'] ) ) {
                            foreach ( $product_types['public'] as $cart_item_key => $cart_item ) {
                                print_shopping_bag( $cart_item_key, $cart_item );
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                            foreach ( $product_types['individual'] as $cart_item_key => $cart_item ) {
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                            }
                        } ?>
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						<form class="woocommerce-coupon-form" action="<?php echo esc_url(wc_get_checkout_url()); ?>" method="post">
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						role="button"
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					<div id="collapseShippingBag" class="collapse inner">
                        <?php
                        if( !empty( $product_types['public'] ) ) {
                            foreach ( $product_types['public'] as $cart_item_key => $cart_item ) {
                                print_shopping_bag( $cart_item_key, $cart_item );
                            }
                        } if( !empty( $product_types['individual'] ) ) {
                            foreach ( $product_types['individual'] as $cart_item_key => $cart_item ) {
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    </div>




    <?php do_action('woocommerce_checkout_after_order_review'); ?>

</div>

# urls.py
from django.urls import path
from . import views

urlpatterns = [
    path("patients/", views.patient_list, name="patient_list"),
]

<!-- patients.html -->
<h1>Hospital Patient Records</h1>

<ul>
    {% for patient in patients %}
        <li>
            <strong>{{ patient.first_name }} {{ patient.last_name }}</strong><br>
            Diagnosis: {{ patient.diagnosis }}
        </li>
    {% endfor %}
</ul>

# views.py
from django.shortcuts import render
from .models import Patient

def patient_list(request):
    patients = Patient.objects.all()
    return render(request, "patients.html", {"patients": patients})

from django.db import models

class Patient(models.Model):
    first_name = models.CharField(max_length=50)
    last_name = models.CharField(max_length=50)
    age = models.IntegerField()
    diagnosis = models.TextField()
    admitted_on = models.DateField(auto_now_add=True)

    def __str__(self):
        return f"{self.first_name} {self.last_name}"

setwd("//files.wustl.edu/Shares/DOM/ONC/Hirbe_Lab/Diana/UBR5 KO RNASeq/analysis/HOM VS WT_2")
Counts <- read.csv("Counts.csv")
# Remove duplicate rows from Counts
Counts <- Counts[!duplicated(Counts[, 1]), ]
rownames(Counts) <- Counts[, 1]
Counts<- Counts [, -1]

# Calculate row means
row_means <- rowMeans(Counts)

# Order genes by row means in descending order
ordered_counts <- Counts[rev(order(row_means)), ]
#alternative code to the above is (ordered_counts <- Counts[order(row_means, decreasing = TRUE), ])
# Filter out rows with row means less than 10
filtered_counts <- ordered_counts[rowMeans(ordered_counts) >= 10, ]
#save filtered data frame
write.csv(filtered_counts, "filtered_counts.csv")

#prepare metadata telling R the conditions (columns) 
metadata <- data.frame( 
  
  sample_id = colnames(filtered_counts),  # Assuming you have loaded the filtered expression data 
  
  condition = c(rep("UBR5 WT", 3), rep("UBR5 HOM", 3)),  # Treatment conditions 
  
  replicate = c(1, 2, 3, 1, 2, 3)  # Sample replicates 
  
) 

metadata$condition <- factor(metadata$condition, levels = c("UBR5 WT", "UBR5 HOM"))

#Load DESEQ2 for normalization
library(DESeq2) 

#Use the DESeqDataSetFromMatrix function from DESeq2 to create a DESeqDataSet object
dds <- DESeqDataSetFromMatrix(countData = filtered_counts,
                              colData = metadata,
                              design = ~ condition)



#Perform normalization and estimation of dispersions: Use the DESeq() function to perform normalization and estimation of dispersions.

dds <- DESeq(dds)
results <- results(dds, alpha = 0.05)
DEGs <- subset(results, abs(log2FoldChange) > 1 & padj < 0.05)


#save the de_genes data frame
write.csv(DEGs, file = "DEG_HOM_VS_WT.csv",)
write.csv(results, file = "DeseqResults_HOM_VS_WT.csv",)



#create volcano plot
library(ggplot2)

# Add column to classify genes as DEG or not
results_df <- as.data.frame(results)
results_df$gene <- rownames(results_df)
results_df$threshold <- "Unchanged"
results_df$threshold[results_df$padj < 0.05 & abs(results_df$log2FoldChange) > 1] <- "DEG"


library(ggrepel)


# Volcano plot
ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HOM vs WT",
       x = "log2 Fold Change (HOM vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black")


# Select top 50 significant genes by padj
top100 <- results_df[order(results_df$padj), ][1:100, ]

# Volcano plot
ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HOM vs WT",
       x = "log2 Fold Change (HOM vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  geom_text_repel(data = top100,
                  aes(label = gene),
                  size = 3,
                  max.overlaps = Inf,
                  box.padding = 0.3,
                  point.padding = 0.2,
                  segment.color = "grey50")

write.csv(top100, file = "Top100_HOM_vs_WT.csv", row.names = FALSE)


# Define genes of interest
genes_of_interest <- c("Egfr", "Hsp90ab1", "Map2k2", "Cerk", "Pdgfra", "Tyk2", "Jak1",
                       "Yap1", "Taz", "Kdr", "Aurka", "Pten", "Csf1r","Ptch1", "Smo", "Gli2", "Gli3", "Wnt10a", "Rac2", "Rspo2", "Apc",
                       "Cd274", "Pdcd1", "Id1", "Id3", "Cdh1", "Cdc73", "Hrpt2","Csf1","Golph3", "Cdk1", "Acsl4", "Ptk2b", "Akt1", "Akt2", "Akt3", "Pik3ca", "Pik3c2a", "Pik3cb" , "Pik3c3", "Pik3c2b", "Pik3cd", "Atmin", "Cdkn1a", "Cdk9", "Rela", "Nfkb1", "Nfkb2", "Capza1", "Stat1", "Stat3", "Irf1", "Irf3")

# Subset DEGs for these genes (case-sensitive match!)
genes_subset <- results_df[rownames(results_df) %in% genes_of_interest, ]

# Save genes of interest with stats
write.csv(genes_subset, file = "GenesOfInterest_HOM_vs_WT.csv", row.names = TRUE)

ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HET vs WT",
       x = "log2 Fold Change (HET vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  geom_text_repel(
    data = genes_subset,
    aes(label = gene),
    color = "black",       # <-- force label text to black
    size = 3,
    max.overlaps = Inf,
    box.padding = 0.3,
    point.padding = 0.2,
    segment.color = "grey50"
  )


# Subset DEGs only
DEGs_df <- as.data.frame(DEGs)
DEGs_df$gene <- rownames(DEGs_df)

# Find overlap between DEGs and genes of interest
genes_subset <- DEGs_df[rownames(DEGs_df) %in% genes_of_interest, ]

# Save overlapping genes with stats
write.csv(genes_subset, file = "GenesOfInterest_DEGs_HOM_vs_WT.csv", row.names = TRUE)

# Volcano plot with labels ONLY for genes of interest that are DEGs
ggplot(results_df, aes(x = log2FoldChange, y = -log10(padj), color = threshold)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("Unchanged" = "grey", "DEG" = "red")) +
  theme_minimal(base_size = 14) +
  labs(title = "Volcano Plot: HET vs WT",
       x = "log2 Fold Change (HET vs WT)",
       y = "-log10 Adjusted p-value",
       color = "Gene status") +
  geom_vline(xintercept = c(-1, 1), linetype = "dashed", color = "black") +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "black") +
  geom_text_repel(
    data = genes_subset,
    aes(label = gene),
    color = "black",       # labels in black
    size = 5,
    max.overlaps = Inf,
    box.padding = 0.3,
    point.padding = 0.2,
    segment.color = "grey50"
  )





if (!requireNamespace("clusterProfiler", quietly = TRUE)) {
  BiocManager::install("clusterProfiler")
}
if (!requireNamespace("msigdbr", quietly = TRUE)) {
  install.packages("msigdbr")
}
library(clusterProfiler)
library(msigdbr)


# Convert results to dataframe
res_df <- as.data.frame(results)

# Remove NA log2FC
res_df <- res_df[!is.na(res_df$log2FoldChange), ]

# Create named vector: names = gene symbols, values = log2FC
gene_list <- res_df$log2FoldChange
names(gene_list) <- rownames(res_df)

# Sort decreasing for clusterProfiler
gene_list <- sort(gene_list, decreasing = TRUE)


# Mouse Hallmark gene sets
hallmark_sets <- msigdbr(species = "Mus musculus", category = "H")  # H = Hallmark

# Use as two-column dataframe: gs_name (pathway), gene_symbol
term2gene <- hallmark_sets[, c("gs_name", "gene_symbol")]

# Make sure your DESeq2 results have no NA log2FC
res_df <- as.data.frame(results)
res_df <- res_df[!is.na(res_df$log2FoldChange), ]

# Named vector: names = gene symbols, values = log2FC
gene_list <- res_df$log2FoldChange
names(gene_list) <- rownames(res_df)
gene_list <- sort(gene_list, decreasing = TRUE)


gsea_res <- GSEA(
  geneList = gene_list,
  TERM2GENE = term2gene,  # <- must be dataframe, not list
  pvalueCutoff = 0.1,
  verbose = FALSE
)

# View top pathways
head(as.data.frame(gsea_res))

# Save results
write.csv(as.data.frame(gsea_res), "GSEA_Hallmark_Mouse_HOM_vs_WT.csv", row.names = FALSE)

library(enrichplot)

# Convert GSEA results to dataframe
gsea_df <- as.data.frame(gsea_res)

# Suppose the top (and only) enriched pathway:
top_pathway <- gsea_df$ID[5]  # or use $Description if you prefer

# Classic GSEA plot for the top pathway
gseaplot2(
  gsea_res,
  geneSetID = top_pathway,   # pathway ID
  title = gsea_df$Description[5],  # nice descriptive title
  color = "red"
)
#PLOT HALLMARK PATHWAYS
library(ggplot2)

# Convert GSEA results to dataframe
gsea_df <- as.data.frame(gsea_res)

# Order pathways by NES (normalized enrichment score)
gsea_df <- gsea_df[order(gsea_df$NES, decreasing = TRUE), ]

# Plot ALL enriched pathways
ggplot(gsea_df, aes(x = reorder(Description, NES), y = NES, fill = -log10(p.adjust))) +
  geom_col() +
  coord_flip() +
  labs(
    title = "GSEA: All Enriched Hallmark Pathways",
    x = "Pathway",
    y = "Normalized Enrichment Score (NES)",
    fill = "-log10 adj p-value"
  ) +
  theme_minimal(base_size = 14)


#plot GSEA KEGG, GO, REACTOME --------------------------
# Prepare ranked gene list with Entrez IDs for GSEA
# --------------------------
library(clusterProfiler)
library(org.Mm.eg.db)
library(ReactomePA)

# Convert gene symbols to Entrez IDs
entrez_map <- bitr(names(gene_list), fromType="SYMBOL", toType="ENTREZID", OrgDb=org.Mm.eg.db)
gene_list_df <- merge(entrez_map, data.frame(log2FC = gene_list), by.x="SYMBOL", by.y="row.names")
gene_list_df <- gene_list_df[!duplicated(gene_list_df$ENTREZID), ]
gene_list_named <- gene_list_df$log2FC
names(gene_list_named) <- gene_list_df$ENTREZID

# Sort decreasing for GSEA
gene_list_named <- sort(gene_list_named, decreasing = TRUE)

# --------------------------
# 1) GSEA: KEGG Pathways
# --------------------------
gsea_kegg <- gseKEGG(
  geneList = gene_list_named,
  organism = "mmu",
  minGSSize = 10,
  pvalueCutoff = 0.1,
  verbose = TRUE
)

# Save KEGG GSEA results
write.csv(as.data.frame(gsea_kegg), "GSEA_KEGG_HOM_vs_WT.csv", row.names = FALSE)

# Top 30 KEGG pathways barplot
library(enrichplot)
library(ggplot2)

# Convert gseaResult to dataframe to see top pathways
gsea_df <- as.data.frame(gsea_kegg)

# Select top 30 pathways by NES or pvalue
top30 <- gsea_df[order(gsea_df$NES, decreasing = TRUE)[1:30], ]

# Ridgeplot (shows enrichment distribution for multiple pathways)
ridgeplot(gsea_kegg, showCategory = 30) +
  ggtitle("GSEA: KEGG Top 30 Pathways") +
  theme_minimal(base_size = 14)

# Optional: classic GSEA plot for the top pathway
top_pathway <- top30$ID[1]
gseaplot2(gsea_kegg, geneSetID = top_pathway,
          title = top30$Description[1], color = "red")


# --------------------------
# 2) GSEA: GO Biological Process (BP)
# --------------------------
gsea_go_bp <- gseGO(
  geneList = gene_list_named,
  OrgDb = org.Mm.eg.db,
  ont = "ALL",
  keyType = "ENTREZID",
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.1,
  verbose = TRUE
)

# Save GO BP GSEA results
write.csv(as.data.frame(gsea_go_bp), "GSEA_GO_BP_HOM_vs_WT.csv", row.names = FALSE)

# Top 30 GO BP pathways barplot
barplot(gsea_go_bp, showCategory = 30, title = "GSEA: GO BP Top 30 Pathways")

# --------------------------
# 3) GSEA: Reactome Pathways
# --------------------------
gsea_reactome <- gsePathway(
  geneList = gene_list_named,
  organism = "mouse",
  minGSSize = 10,
  pvalueCutoff = 0.1,
  verbose = TRUE
)

# Save Reactome GSEA results
write.csv(as.data.frame(gsea_reactome), "GSEA_Reactome_HOM_vs_WT.csv", row.names = FALSE)

# Top 30 Reactome pathways barplot
barplot(gsea_reactome, showCategory = 30, title = "GSEA: Reactome Top 30 Pathways")

# --------------------------


library(ggplot2)
library(dplyr)

# Convert GSEA Reactome results to dataframe
gsea_reactome_df <- as.data.frame(gsea_reactome)

# Select top 30 pathways by NES magnitude
top30_reactome <- gsea_reactome_df %>%
  arrange(desc(abs(NES))) %>%
  slice(1:30)

# Reorder for plotting (highest NES on top)
top30_reactome$Description <- factor(top30_reactome$Description, levels = rev(top30_reactome$Description))

# Plot barplot: NES on x-axis, pathways on y-axis, fill by -log10(padj)
ggplot(top30_reactome, aes(x = NES, y = Description, fill = -log10(p.adjust))) +
  geom_bar(stat = "identity") +
  scale_fill_gradient(low = "red", high = "darkred") +
  theme_minimal(base_size = 14) +
  labs(title = "GSEA: Top 30 Reactome Pathways",
       x = "Normalized Enrichment Score (NES)",
       y = "",
       fill = "-log10(adj.p)")



#ORA Enrichment analysis
### --- DEG-based Over-Representation Analysis (ORA) ---

library(clusterProfiler)
library(org.Mm.eg.db)
library(msigdbr)
library(ReactomePA)
library(enrichplot)
library(ggplot2)
library(dplyr)

# Subset DEGs you already defined
DEGs_df <- as.data.frame(DEGs)
DEGs_df$gene <- rownames(DEGs_df)

# Convert SYMBOL -> ENTREZ
deg_entrez <- bitr(
  DEGs_df$gene,
  fromType = "SYMBOL",
  toType   = "ENTREZID",
  OrgDb    = org.Mm.eg.db
)

# Remove duplicates
deg_entrez <- deg_entrez[!duplicated(deg_entrez$ENTREZID), ]


### --- ORA: GO Biological Process ---
ego_bp <- enrichGO(
  gene          = deg_entrez$ENTREZID,
  OrgDb         = org.Mm.eg.db,
  keyType       = "ENTREZID",
  ont           = "BP",   # Biological Process
  pAdjustMethod = "BH",
  pvalueCutoff  = 0.05,
  qvalueCutoff  = 0.2,
  readable      = TRUE    # back to SYMBOL
)

# Save results
write.csv(as.data.frame(ego_bp), "ORA_GO_BP_HET_vs_WT.csv", row.names = FALSE)

# Plot top 20
dotplot(ego_bp, showCategory = 20) + ggtitle("ORA: GO Biological Process (DEGs)")


### --- ORA: KEGG Pathways ---
ekegg <- enrichKEGG(
  gene          = deg_entrez$ENTREZID,
  organism      = "mmu",
  pvalueCutoff  = 0.05
)

# Save results
write.csv(as.data.frame(ekegg), "ORA_KEGG_HET_vs_WT.csv", row.names = FALSE)

# Plot top 20
dotplot(ekegg, showCategory = 20) + ggtitle("ORA: KEGG Pathways (DEGs)")


### --- ORA: Reactome Pathways ---
ereact <- enrichPathway(
  gene          = deg_entrez$ENTREZID,
  organism      = "mouse",
  pvalueCutoff  = 0.05,
  readable      = TRUE
)

# Save results
write.csv(as.data.frame(ereact), "ORA_Reactome_HET_vs_WT.csv", row.names = FALSE)

# Plot top 20
dotplot(ereact, showCategory = 20) + ggtitle("ORA: Reactome Pathways (DEGs)")


### --- ORA: Hallmark Gene Sets ---
hallmark_sets <- msigdbr(species = "Mus musculus", category = "H")
term2gene <- hallmark_sets[, c("gs_name", "entrez_gene")]
term2gene$entrez_gene <- as.character(term2gene$entrez_gene)

ora_hallmark <- enricher(
  gene          = deg_entrez$ENTREZID,
  TERM2GENE     = term2gene,
  pvalueCutoff  = 0.05
)

# Save results
write.csv(as.data.frame(ora_hallmark), "ORA_Hallmark_HET_vs_WT.csv", row.names = FALSE)

# Plot top 20
dotplot(ora_hallmark, showCategory = 20) + ggtitle("ORA: Hallmark Pathways (DEGs)")


### --- DEG-based ORA: Up vs Downregulated DEGs ---


# Split DEGs
up_DEGs   <- rownames(DEGs[DEGs$log2FoldChange > 1, ])
down_DEGs <- rownames(DEGs[DEGs$log2FoldChange < -1, ])

# Convert SYMBOL -> ENTREZ for both
up_entrez <- bitr(up_DEGs, fromType="SYMBOL", toType="ENTREZID", OrgDb=org.Mm.eg.db)
up_entrez <- up_entrez[!duplicated(up_entrez$ENTREZID), ]

down_entrez <- bitr(down_DEGs, fromType="SYMBOL", toType="ENTREZID", OrgDb=org.Mm.eg.db)
down_entrez <- down_entrez[!duplicated(down_entrez$ENTREZID), ]


### --- ORA: GO Biological Process ---
ego_up <- enrichGO(gene=up_entrez$ENTREZID, OrgDb=org.Mm.eg.db, keyType="ENTREZID",
                   ont="BP", pAdjustMethod="BH", pvalueCutoff=0.05, qvalueCutoff=0.2, readable=TRUE)
ego_down <- enrichGO(gene=down_entrez$ENTREZID, OrgDb=org.Mm.eg.db, keyType="ENTREZID",
                     ont="BP", pAdjustMethod="BH", pvalueCutoff=0.05, qvalueCutoff=0.2, readable=TRUE)

write.csv(as.data.frame(ego_up), "ORA_GO_BP_Upregulated.csv", row.names=FALSE)
write.csv(as.data.frame(ego_down), "ORA_GO_BP_Downregulated.csv", row.names=FALSE)

dotplot(ego_up, showCategory=20) + ggtitle("ORA: GO BP (Upregulated)")
dotplot(ego_down, showCategory=20) + ggtitle("ORA: GO BP (Downregulated)")
                     

### --- ORA: Hallmark Gene Sets ---
hallmark_sets <- msigdbr(species="Mus musculus", category="H")
term2gene <- hallmark_sets[, c("gs_name", "entrez_gene")]
term2gene$entrez_gene <- as.character(term2gene$entrez_gene)

ora_hallmark_up <- enricher(gene=up_entrez$ENTREZID, TERM2GENE=term2gene, pvalueCutoff=0.05)
ora_hallmark_down <- enricher(gene=down_entrez$ENTREZID, TERM2GENE=term2gene, pvalueCutoff=0.05)

write.csv(as.data.frame(ora_hallmark_up), "ORA_Hallmark_Upregulated.csv", row.names=FALSE)
write.csv(as.data.frame(ora_hallmark_down), "ORA_Hallmark_Downregulated.csv", row.names=FALSE)

dotplot(ora_hallmark_up, showCategory=20) + ggtitle("ORA: Hallmark (Upregulated)")
dotplot(ora_hallmark_down, showCategory=20) + ggtitle("ORA: Hallmark (Downregulated)")


### --- ORA: Reactome ---
ereact_up <- enrichPathway(gene=up_entrez$ENTREZID, organism="mouse", pvalueCutoff=0.05, readable=TRUE)
ereact_down <- enrichPathway(gene=down_entrez$ENTREZID, organism="mouse", pvalueCutoff=0.05, readable=TRUE)

write.csv(as.data.frame(ereact_up), "ORA_Reactome_Upregulated.csv", row.names=FALSE)
write.csv(as.data.frame(ereact_down), "ORA_Reactome_Downregulated.csv", row.names=FALSE)

dotplot(ereact_up, showCategory=20) + ggtitle("ORA: Reactome (Upregulated)")
dotplot(ereact_down, showCategory=20) + ggtitle("ORA: Reactome (Downregulated)")



### --- ORA: KEGG ---
ekegg_up <- enrichKEGG(gene=up_entrez$ENTREZID, organism="mmu", pvalueCutoff=0.05)
ekegg_down <- enrichKEGG(gene=down_entrez$ENTREZID, organism="mmu", pvalueCutoff=0.05)

write.csv(as.data.frame(ekegg_up), "ORA_KEGG_Upregulated.csv", row.names=FALSE)
write.csv(as.data.frame(ekegg_down), "ORA_KEGG_Downregulated.csv", row.names=FALSE)

dotplot(ekegg_up, showCategory=20) + ggtitle("ORA: KEGG (Upregulated)")
dotplot(ekegg_down, showCategory=20) + ggtitle("ORA: KEGG (Downregulated)")



#Heatmap
ifna_genes <- c(
  "Serpine2","Sntb1","Mmp14","Tnc","Fstl1","Vcan","Il15","Scg2","Ecm1","Vegfa",
  "Cxcl12","Dst","Tgfbr3","Ptx3","Pcolce","Spock1","Adam12","Tagln","Loxl1","Cdh6",
  "Pvr","Gadd45b","Gadd45a","Rhob","Rgs4","Fzd8","Tfpi2","Vegfc","Oxtr","Dab2",
  "Lum","Col16a1","Tnfrsf11b","Fap","Matn2","Snai2","Cxcl5","Calu","Capg","Emp3",
  "Nnmt","Gpc1","Tnfaip3","Nid2","Mcm7","Slit3","Slit2","Matn3","Fmod","Edil3",
  "Dkk1","Qsox1","Copa","Cxcl15","Crlf1","Grem1","Fbln5","Sgcb","Sgcg","Sgcd",
  "Plod2","Plod3","Foxc2","Sdc1","Sdc4","Dpysl3","Tnfrsf12a","Pdlim4","Mfap5","Col5a3",
  "P3h1","Cadm1","Fstl3","Efemp2","Cap2","Gpx7","Cthrc1","Basp1","Glipr1","Lrrc15",
  "Pcolce2","Colgalt1","Postn","Htra1","Pmepa1","Myl9","Slc6a8","Magee1","Wipf1","Fermt2",
  "Abi3bp","Ntm","Tpm4","Ecm2","Anpep","Gm21451","Acta2","Aplp1","Areg","Bdnf",
  "Bgn","Bmp1","Cald1","Serpinh1","Cd44","Cdh2","Col11a1","Col12a1","Col3a1","Col4a1",
  "Col4a2","Col5a1","Col5a2","Col6a2","Col6a3","Col7a1","Col8a2","Col1a1","Col1a2","Comp",
  "Ccn1","Sfrp4","Sfrp1","Mylk","Dcn","Eln","Eno2","Fas","Fbln1","Fbln2",
  "Fbn1","Fbn2","Fgf2","Ccn2","Flna","Fn1","Fuca1","Gas1","Gja1","Id2",
  "Igfbp2","Igfbp3","Igfbp4","Il6","Inhba","Itga2","Itga5","Itgav","Itgb1","Itgb3",
  "Itgb5","Jun","Lgals1","Lox","Lrp1","Mest","Mgp","Mmp2","Mmp3","Msx1","Notch2",
  "Pdgfrb","Pfn2","Serpine1","Plaur","Pmp22","Prrx1","Ppib","Pthlh","Sat1","Sparc",
  "Spp1","Tgfb1","Tgm2","Thbs1","Thbs2","Thy1","Timp1","Timp3","Tpm1","Tpm2",
  "Vcam1","Vim","Wnt5a","Cdh11","Nt5e","Gem","Lama1","Plod1","Lama3","Lama2",
  "Lamc2","Lamc1","Tgfbi"
)



# Get normalized counts from DESeq2
norm_counts <- counts(dds, normalized=TRUE)

# Subset for IFN-α response genes (keep only genes present in your dataset)
ifna_counts <- norm_counts[rownames(norm_counts) %in% ifna_genes, ]

# Optionally, z-score normalize each gene for heatmap visualization
ifna_counts_z <- t(scale(t(ifna_counts)))


# Create annotation for columns
ann_col <- data.frame(
  Condition = metadata$condition
)
rownames(ann_col) <- metadata$sample_id


library(pheatmap)

# Make sure columns are in your desired order
desired_order <- c("sample.WT_1", "sample.WT_2", "sample.WT_3",
                   "sample.neg.neg_1", "sample.neg.neg_2", "sample.neg.neg_3")
ifna_counts_z <- ifna_counts_z[, desired_order]

# Make sure annotation matches
ann_col <- ann_col[desired_order, , drop = FALSE]

# Heatmap
pheatmap(ifna_counts_z,
         annotation_col = ann_col,
         show_rownames = TRUE,
         show_colnames = TRUE,
         cluster_rows = TRUE,
         cluster_cols = FALSE,   # keep the column order fixed
         scale = "row",
         fontsize_row = 8,
         main = "Interferon Alpha Response Genes")

class NW_PayslipGenerateReport
{
    public static System.IO.Stream getPayslipReport(date _from, date _to, hcmWorkerRecid worker)
    {
        NWPY_PayslipReportContract contract = new NWPY_PayslipReportContract();
        SRSPrintDestinationSettings settings;
        SrsReportEMailDataContract  emailContract;
        System.IO.MemoryStream      _strem;
        SysMailerMessageBuilder mailer = new SysMailerMessageBuilder();
        SysMailerSMTP smtp = new SysMailerSMTP();
        Filename filename;
        Array arrayfiles;
        System.Byte[] reportBytes = new System.Byte[0]();
        SRSProxy srsProxy;
        SrsReportRunService srsreportRunService = new SrsReportRunService();
        Microsoft.Dynamics.AX.Framework.Reporting.Shared.ReportingService.ParameterValue[] parameterValueArray;
        Map reportParametersMap;
        SRSReportExecutionInfo executionInfo = new SRSReportExecutionInfo();
        SrsReportRunController controller = new SrsReportRunController();

        filename = 'Payslip.PDF';
        contract.ParmFromDate(_from);
        contract.parmToDate(_to);
        contract.parmEmployee(worker);

        controller.parmReportName(ssrsReportStr(NWPY_PayslipReport, PrecisionDesign2));
        controller.parmShowDialog(false);
        controller.parmReportContract().parmRdpContract(contract);
        settings = controller.parmReportContract().parmPrintSettings();
        settings.printMediumType(SRSPrintMediumType::File);
        settings.fileFormat(SRSReportFileFormat::PDF);
        settings.overwriteFile(true);
        settings.fileName(filename);
        controller.parmReportContract().parmReportServerConfig(SRSConfiguration::getDefaultServerConfiguration());
        controller.parmReportContract().parmReportExecutionInfo(executionInfo);
        srsreportRunService.getReportDataContract(controller.parmReportContract().parmReportName());
        srsreportRunService.preRunReport(controller.parmReportContract());
        reportParametersMap =  srsreportRunService.createParamMapFromContract(controller.parmReportContract());
        parameterValueArray = SrsReportRunUtil::getParameterValueArray(reportParametersMap);
        srsProxy = SRSProxy::constructWithConfiguration(controller.parmReportContract().parmReportServerConfig());
        reportBytes = srsProxy.renderReportToByteArray(controller.parmReportContract().parmReportPath(),
                        parameterValueArray,
                        settings.fileFormat(),
                        settings.deviceInfo());
        if(reportBytes)
        {
            _strem = new System.IO.MemoryStream(reportBytes);
        }
        return _strem;
    }

}

textToTranslate = "Syed Ali Nayyar Nasir";

targetLang = "ar";

// Arabic

apiKey = "AIzaSyCtqtqWGfnZ1EAYXD9pGhQoZ6QtkflC270";

// replace with your actual key

url = "https://translation.googleapis.com/language/translate/v2?key=" + apiKey;

params = Map();

params.put("q",textToTranslate);

params.put("target",targetLang);

params.put("source","en");

// optional

headers = Map();

headers.put("Content-Type","application/x-www-form-urlencoded");

response = invokeurl

[

	url :url

	type :POST

	parameters:params

	headers:headers

];

info response;

translation = response.get("data").get("translations").get(0).get("translatedText");

info translation;
 

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