Survival Analysis

Colorectal Cancer | Pyroptosis | Unique Genes per RCD Type | Gene Expression of Normal Samples

I. Preliminaries

Loading libraries

library("tidyverse")
library("tibble")
library("msigdbr")
library("ggplot2")
library("TCGAbiolinks")
library("RNAseqQC")
library("DESeq2")
library("ensembldb")
library("purrr")
library("magrittr")
library("vsn")
library("matrixStats")
library("dplyr")
library("grex")
library("survminer")
library("survival")

II. Downloading the TCGA gene expression data

Create a function for downloading TCGA gene expression data.

For more detailed documentation, refer to 2. Differential Gene Expression Analysis - TCGA.Rmd.

query_and_filter_samples <- function(project) {
  query_tumor <- GDCquery(
    project = project,
    data.category = "Transcriptome Profiling",
    data.type = "Gene Expression Quantification",
    experimental.strategy = "RNA-Seq",
    workflow.type = "STAR - Counts",
    access = "open",
    sample.type = "Primary Tumor"
  )
  tumor <- getResults(query_tumor)

  query_normal <- GDCquery(
    project = project,
    data.category = "Transcriptome Profiling",
    data.type = "Gene Expression Quantification",
    experimental.strategy = "RNA-Seq",
    workflow.type = "STAR - Counts",
    access = "open",
    sample.type = "Solid Tissue Normal"
  )
  normal <- getResults(query_normal)

  submitter_ids <- inner_join(tumor, normal, by = "cases.submitter_id") %>%
    dplyr::select(cases.submitter_id)
  tumor <- tumor %>%
    dplyr::filter(cases.submitter_id %in% submitter_ids$cases.submitter_id)
  normal <- normal %>%
    dplyr::filter(cases.submitter_id %in% submitter_ids$cases.submitter_id)

  samples <- rbind(tumor, normal)
  unique(samples$sample_type)

  query_project <- GDCquery(
    project = project,
    data.category = "Transcriptome Profiling",
    data.type = "Gene Expression Quantification",
    experimental.strategy = "RNA-Seq",
    workflow.type = "STAR - Counts",
    access = "open",
    sample.type = c("Solid Tissue Normal", "Primary Tumor"),
    barcode = as.list(samples$sample.submitter_id)
  )

  # If this is your first time running this notebook (i.e., you have not yet downloaded the results of the query in the previous block),
  # uncomment the line below

  # GDCdownload(query_project)

  return(list(samples = samples, query_project = query_project))
}

Download the TCGA gene expression data for colorectal cancer (TCGA-COAD).

projects <- c("TCGA-COAD")

with_results_projects <- c()

samples <- list()
project_data <- list()

for (project in projects) {
  result <- tryCatch(
    {
      result <- query_and_filter_samples(project)
      samples[[project]] <- result$samples
      project_data[[project]] <- result$query_project

      with_results_projects <- c(with_results_projects, project)
    },
    error = function(e) {

    }
  )
}

Running the code block above should generate and populate a directory named GDCdata.

III. Data preprocessing

Construct the RNA-seq count matrix for each cancer type.

tcga_data <- list()
tcga_matrix <- list()

projects <- with_results_projects
for (project in projects) {
  tcga_data[[project]] <- GDCprepare(project_data[[project]], summarizedExperiment = TRUE)
}

for (project in projects) {
  count_matrix <- assay(tcga_data[[project]], "unstranded")

  # Remove duplicate entries
  count_matrix_df <- data.frame(count_matrix)
  count_matrix_df <- count_matrix_df[!duplicated(count_matrix_df), ]
  count_matrix <- data.matrix(count_matrix_df)
  rownames(count_matrix) <- cleanid(rownames(count_matrix))
  count_matrix <- count_matrix[!(duplicated(rownames(count_matrix)) | duplicated(rownames(count_matrix), fromLast = TRUE)), ]

  tcga_matrix[[project]] <- count_matrix
}

Format the samples table so that it can be fed as input to DESeq2.

for (project in projects) {
  rownames(samples[[project]]) <- samples[[project]]$cases
  samples[[project]] <- samples[[project]] %>%
    dplyr::select(case = "cases.submitter_id", type = "sample_type")
  samples[[project]]$type <- str_replace(samples[[project]]$type, "Solid Tissue Normal", "normal")
  samples[[project]]$type <- str_replace(samples[[project]]$type, "Primary Tumor", "tumor")
}

DESeq2 requires the row names of samples should be identical to the column names of count_matrix.

for (project in projects) {
  colnames(tcga_matrix[[project]]) <- gsub(x = colnames(tcga_matrix[[project]]), pattern = "\\.", replacement = "-")
  tcga_matrix[[project]] <- tcga_matrix[[project]][, rownames(samples[[project]])]

  # Sanity check
  print(all(colnames(tcga_matrix[[project]]) == rownames(samples[[project]])))
}

IV. Differential gene expression analysis

For more detailed documentation on obtaining the gene set, refer to 7. Differential Gene Expression Analysis - TCGA - Pan-cancer - Unique Genes.Rmd.

RCDdb <- "temp/unique_genes/necroptosis_ferroptosis_pyroptosis/"

Write utility functions for filtering the gene sets, performing differential gene expression analysis, plotting the results, and performing variance-stabilizing transformation.

filter_gene_set_and_perform_dgea <- function(genes) {
  tcga_rcd <- list()

  for (project in projects) {
    rownames(genes) <- genes$gene_id
    tcga_rcd[[project]] <- tcga_matrix[[project]][rownames(tcga_matrix[[project]]) %in% genes$gene_id, ]
    tcga_rcd[[project]] <- tcga_rcd[[project]][, rownames(samples[[project]])]
  }

  dds_rcd <- list()
  res_rcd <- list()

  for (project in projects) {
    print(project)
    print("=============")
    dds <- DESeqDataSetFromMatrix(
      countData = tcga_rcd[[project]],
      colData = samples[[project]],
      design = ~type
    )
    dds <- filter_genes(dds, min_count = 10)
    dds$type <- relevel(dds$type, ref = "normal")
    dds_rcd[[project]] <- DESeq(dds)
    res_rcd[[project]] <- results(dds_rcd[[project]])
  }

  deseq.bbl.data <- list()

  for (project in projects) {
    deseq.results <- res_rcd[[project]]
    deseq.bbl.data[[project]] <- data.frame(
      row.names = rownames(deseq.results),
      baseMean = deseq.results$baseMean,
      log2FoldChange = deseq.results$log2FoldChange,
      lfcSE = deseq.results$lfcSE,
      stat = deseq.results$stat,
      pvalue = deseq.results$pvalue,
      padj = deseq.results$padj,
      cancer_type = project,
      gene_symbol = genes[rownames(deseq.results), "gene"]
    )
  }

  deseq.bbl.data.combined <- bind_rows(deseq.bbl.data)
  deseq.bbl.data.combined <- dplyr::filter(deseq.bbl.data.combined, abs(log2FoldChange) >= 1.5 & padj < 0.05)

  return(deseq.bbl.data.combined)
}

plot_dgea <- function(deseq.bbl.data.combined) {
  sizes <- c("<10^-15" = 4, "10^-10" = 3, "10^-5" = 2, "0.05" = 1)

  deseq.bbl.data.combined <- deseq.bbl.data.combined %>%
    mutate(fdr_category = cut(padj,
      breaks = c(-Inf, 1e-15, 1e-10, 1e-5, 0.05),
      labels = c("<10^-15", "10^-10", "10^-5", "0.05"),
      right = FALSE
    ))

  top_genes <- deseq.bbl.data.combined %>%
    group_by(cancer_type) %>%
    mutate(rank = rank(-abs(log2FoldChange))) %>%
    dplyr::filter(rank <= 10) %>%
    ungroup()

  ggplot(top_genes, aes(y = cancer_type, x = gene_symbol, size = fdr_category, fill = log2FoldChange)) +
    geom_point(alpha = 0.5, shape = 21, color = "black") +
    scale_size_manual(values = sizes) +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red", limits = c(min(deseq.bbl.data.combined$log2FoldChange), max(deseq.bbl.data.combined$log2FoldChange))) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 9, angle = 90, hjust = 1)
    ) +
    theme(legend.position = "bottom") +
    theme(legend.position = "bottom") +
    labs(size = "Adjusted p-value", fill = "log2 FC", y = "Cancer type", x = "Gene")
}

perform_vsd <- function(genes) {
  tcga_rcd <- list()

  for (project in projects) {
    rownames(genes) <- genes$gene_id
    tcga_rcd[[project]] <- tcga_matrix[[project]][rownames(tcga_matrix[[project]]) %in% genes$gene_id, ]
    tcga_rcd[[project]] <- tcga_rcd[[project]][, rownames(samples[[project]])]
  }

  vsd_rcd <- list()

  for (project in projects) {
    print(project)
    print("=============")
    dds <- DESeqDataSetFromMatrix(
      countData = tcga_rcd[[project]],
      colData = samples[[project]],
      design = ~type
    )
    dds <- filter_genes(dds, min_count = 10)

    # Perform variance stabilization
    dds <- estimateSizeFactors(dds)
    nsub <- sum(rowMeans(counts(dds, normalized = TRUE)) > 10)
    vsd <- vst(dds, nsub = nsub)
    vsd_rcd[[project]] <- assay(vsd)
  }

  return(vsd_rcd)
}

Pyroptosis

Fetch the gene set of interest.

genes <- read.csv(paste0(RCDdb, "Pyroptosis.csv"))
print(genes)

genes$gene_id <- cleanid(genes$gene_id)
genes <- distinct(genes, gene_id, .keep_all = TRUE)
genes <- subset(genes, gene_id != "")
genes

Filter the genes to include only those in the gene set of interest, and then perform differential gene expression analysis.

deseq.bbl.data.combined <- filter_gene_set_and_perform_dgea(genes)

[1] "TCGA-COAD"
[1] "============="

Warning: some variables in design formula are characters, converting to factorsestimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 3 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing

deseq.bbl.data.combined

Plot the results.

plot_dgea(deseq.bbl.data.combined)

Perform variance-stabilizing transformation for further downstream analysis (i.e., for survival analysis).

vsd <- perform_vsd(genes)

[1] "TCGA-COAD"
[1] "============="

V. Downloading the clinical data

Download clinical data from TCGA, and perform some preprocessing: - The deceased column should be FALSE if the patient is alive and TRUE otherwise - The overall_survival column should reflect the follow-up time if the patient is alive and the days to death otherwise

download_clinical_data <- function(project) {
  clinical_data <- GDCquery_clinic(project)
  clinical_data$deceased <- ifelse(clinical_data$vital_status == "Alive", FALSE, TRUE)
  clinical_data$overall_survival <- ifelse(clinical_data$vital_status == "Alive",
    clinical_data$days_to_last_follow_up,
    clinical_data$days_to_death
  )

  return(clinical_data)
}

tcga_clinical <- list()
for (project in projects) {
  tcga_clinical[[project]] <- download_clinical_data(project)
}

VI. Performing survival analysis

Write utility functions for performing survival analysis.

construct_gene_df <- function(gene_of_interest, project) {
  gene_df <- vsd[[project]] %>%
    as.data.frame() %>%
    rownames_to_column(var = "gene_id") %>%
    gather(key = "case_id", value = "counts", -gene_id) %>%
    left_join(., genes, by = "gene_id") %>%
    dplyr::filter(gene == gene_of_interest) %>%
    dplyr::filter(case_id %in% rownames(samples[[project]] %>% dplyr::filter(type == "normal")))

  q1 <- quantile(gene_df$counts, probs = 0.25)
  q3 <- quantile(gene_df$counts, probs = 0.75)
  gene_df$strata <- ifelse(gene_df$counts >= q3, "HIGH", ifelse(gene_df$counts <= q1, "LOW", "MIDDLE"))
  gene_df <- gene_df %>% dplyr::filter(strata %in% c("LOW", "HIGH"))
  gene_df$case_id <- paste0(sapply(strsplit(as.character(gene_df$case_id), "-"), `[`, 1), '-',
                          sapply(strsplit(as.character(gene_df$case_id), "-"), `[`, 2), '-', 
                          sapply(strsplit(as.character(gene_df$case_id), "-"), `[`, 3))
  gene_df <- merge(gene_df, tcga_clinical[[project]], by.x = "case_id", by.y = "submitter_id")
  
  return(gene_df)
}

compute_surival_fit <- function(gene_df) {
  return (survfit(Surv(overall_survival, deceased) ~ strata, data = gene_df))
}

compute_cox <- function(gene_df) {
  return (coxph(Surv(overall_survival, deceased) ~ strata, data=gene_df))
}

plot_survival <- function(fit) {
  return(ggsurvplot(fit,
    data = gene_df,
    pval = T,
    risk.table = T,
    risk.table.height = 0.3
  ))
}

compute_survival_diff <- function(gene_df) {
  return(survdiff(Surv(overall_survival, deceased) ~ strata, data = gene_df))
}

Perform survival analysis by testing for the difference in the Kaplan-Meier curves using the G-rho family of Harrington and Fleming tests: https://rdrr.io/cran/survival/man/survdiff.html

Our genes of interest are GSDMD (the primary executor of pyroptosis) and the differentially expressed genes.

significant_projects <- c()
significant_genes <- c()

ctr <- 1
for (project in projects) {
  for (gene in c("GSDMD", genes$gene)) {
    cat(project, gene, "\n\n")
    error <- tryCatch (
      {
        gene_df <- construct_gene_df(gene, project)
      },
      error = function(e) {
        cat("\n\n============================\n\n")
        e
      }
    )
    
    if(inherits(error, "error")) next

    if (nrow(gene_df) > 0) {
      fit <- compute_surival_fit(gene_df)
      tryCatch (
        {
          survival <- compute_survival_diff(gene_df)
          cox <- compute_cox(gene_df)
          print(ctr)
          ctr <- ctr + 1
          print(survival)
          cat("\n")
          print(cox)
          print(plot_survival(fit))
          if (pchisq(survival$chisq, length(survival$n)-1, lower.tail = FALSE) < 0.05) {
            significant_projects <- c(significant_projects, project)
            significant_genes <- c(significant_genes, gene)
          }
        },
        error = function(e) {
        }
      )
      
    }
    
    cat("\n\n============================\n\n")
  }
}

TCGA-COAD GSDMD 

[1] 1
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3      2.4     0.152     0.403
strata=LOW  11        2      2.6     0.140     0.403

 Chisq= 0.4  on 1 degrees of freedom, p= 0.5 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.7327    0.4806   1.1773 -0.622 0.534

Likelihood ratio test=0.43  on 1 df, p=0.5139
n= 22, number of events= 5 


============================

TCGA-COAD CHMP7 

[1] 2
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     2.73     0.596     0.984
strata=LOW  16        3     4.27     0.380     0.984

 Chisq= 1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW -0.7437    0.4754   0.7668 -0.97 0.332

Likelihood ratio test=0.95  on 1 df, p=0.3291
n= 27, number of events= 7 


============================

TCGA-COAD GSDMC 

[1] 3
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     4.04  0.000366   0.00148
strata=LOW  11        2     1.96  0.000754   0.00148

 Chisq= 0  on 1 degrees of freedom, p= 1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW 0.03851   1.03926  1.00056 0.038 0.969

Likelihood ratio test=0  on 1 df, p=0.9693
n= 22, number of events= 6 


============================

TCGA-COAD ELANE 

[1] 4
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.34     0.824      1.59
strata=LOW  11        3     4.66     0.591      1.59

 Chisq= 1.6  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -1.0177    0.3614   0.8404 -1.211 0.226

Likelihood ratio test=1.63  on 1 df, p=0.2013
n= 22, number of events= 8 


============================

TCGA-COAD IRF1 

[1] 5
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     3.14   0.00627     0.018
strata=LOW  11        2     1.86   0.01058     0.018

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.1270    1.1355   0.9463 0.134 0.893

Likelihood ratio test=0.02  on 1 df, p=0.8936
n= 22, number of events= 5 


============================

TCGA-COAD CYCS 

[1] 6
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        1      3.7      1.97       5.2
strata=LOW  11        5      2.3      3.15       5.2

 Chisq= 5.2  on 1 degrees of freedom, p= 0.02 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

           coef exp(coef) se(coef)     z     p
strataLOW 2.112     8.267    1.101 1.919 0.055

Likelihood ratio test=5.21  on 1 df, p=0.02242
n= 22, number of events= 6 


============================

TCGA-COAD GSDMA 

[1] 7
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3      3.1   0.00327   0.00685
strata=LOW  11        5      4.9   0.00207   0.00685

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW 0.06796   1.07032  0.82132 0.083 0.934

Likelihood ratio test=0.01  on 1 df, p=0.9341
n= 22, number of events= 8 


============================

TCGA-COAD CASP4 

[1] 8
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2      2.7     0.183     0.336
strata=LOW  11        5      4.3     0.115     0.336

 Chisq= 0.3  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.4982    1.6458   0.8683 0.574 0.566

Likelihood ratio test=0.34  on 1 df, p=0.5572
n= 22, number of events= 7 


============================

TCGA-COAD BAK1 

[1] 9
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     4.21      1.16      2.98
strata=LOW  11        5     2.79      1.75      2.98

 Chisq= 3  on 1 degrees of freedom, p= 0.08 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

           coef exp(coef) se(coef)     z     p
strataLOW 1.358     3.890    0.845 1.607 0.108

Likelihood ratio test=2.92  on 1 df, p=0.08733
n= 22, number of events= 7 


============================

TCGA-COAD NOD1 

[1] 10
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     2.94     0.382     0.762
strata=LOW  11        2     3.06     0.367     0.762

 Chisq= 0.8  on 1 degrees of freedom, p= 0.4 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.7457    0.4744   0.8730 -0.854 0.393

Likelihood ratio test=0.78  on 1 df, p=0.3786
n= 22, number of events= 6 


============================

TCGA-COAD NLRP7 

[1] 11
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     2.62     0.145      0.26
strata=LOW  11        4     3.38     0.112      0.26

 Chisq= 0.3  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.4409    1.5541   0.8713 0.506 0.613

Likelihood ratio test=0.27  on 1 df, p=0.6055
n= 22, number of events= 6 


============================

TCGA-COAD CASP3 

[1] 12
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2      3.7     0.779      1.66
strata=LOW  11        5      3.3     0.871      1.66

 Chisq= 1.7  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 1.0334    2.8105   0.8383 1.233 0.218

Likelihood ratio test=1.7  on 1 df, p=0.1929
n= 22, number of events= 7 


============================

TCGA-COAD GSDMB 

[1] 13
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2    2.071   0.00246    0.0103
strata=LOW  11        1    0.929   0.00549    0.0103

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.1438    1.1547   1.4179 0.101 0.919

Likelihood ratio test=0.01  on 1 df, p=0.9192
n= 22, number of events= 3 


============================

TCGA-COAD GZMB 

[1] 14
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     3.25    0.0193    0.0428
strata=LOW  11        4     3.75    0.0168    0.0428

 Chisq= 0  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.1698    1.1851   0.8220 0.207 0.836

Likelihood ratio test=0.04  on 1 df, p=0.8364
n= 22, number of events= 7 


============================

TCGA-COAD GSDME 

[1] 15
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4      3.7    0.0244    0.0764
strata=LOW  11        2      2.3    0.0392    0.0764

 Chisq= 0.1  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.2576    0.7729   0.9341 -0.276 0.783

Likelihood ratio test=0.08  on 1 df, p=0.7814
n= 22, number of events= 6 


============================

TCGA-COAD CHMP3 

[1] 16
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.79    0.0155    0.0259
strata=LOW  11        4     4.21    0.0103    0.0259

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.1233    0.8840   0.7663 -0.161 0.872

Likelihood ratio test=0.03  on 1 df, p=0.8726
n= 22, number of events= 7 


============================

TCGA-COAD DPP9 

[1] 17
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        6     4.61     0.418      1.11
strata=LOW  11        2     3.39     0.569      1.11

 Chisq= 1.1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.8568    0.4245   0.8384 -1.022 0.307

Likelihood ratio test=1.15  on 1 df, p=0.2835
n= 22, number of events= 8 


============================

TCGA-COAD NOD2 

[1] 18
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.52    0.0928     0.162
strata=LOW  11        3     3.48    0.0670     0.162

 Chisq= 0.2  on 1 degrees of freedom, p= 0.7 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.3296    0.7192   0.8223 -0.401 0.689

Likelihood ratio test=0.16  on 1 df, p=0.6892
n= 22, number of events= 6 


============================

TCGA-COAD NLRC4 

[1] 19
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.01     0.486     0.758
strata=LOW  11        4     4.99     0.196     0.758

 Chisq= 0.8  on 1 degrees of freedom, p= 0.4 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.7124    0.4905   0.8335 -0.855 0.393

Likelihood ratio test=0.72  on 1 df, p=0.397
n= 22, number of events= 7 


============================

TCGA-COAD GSDMD 

[1] 20
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3      2.4     0.152     0.403
strata=LOW  11        2      2.6     0.140     0.403

 Chisq= 0.4  on 1 degrees of freedom, p= 0.5 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.7327    0.4806   1.1773 -0.622 0.534

Likelihood ratio test=0.43  on 1 df, p=0.5139
n= 22, number of events= 5 


============================

TCGA-COAD TIRAP 

[1] 21
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     1.95  0.001517   0.00251
strata=LOW  11        3     3.05  0.000966   0.00251

 Chisq= 0  on 1 degrees of freedom, p= 1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z    p
strataLOW -0.0459    0.9551   0.9171 -0.05 0.96

Likelihood ratio test=0  on 1 df, p=0.9601
n= 22, number of events= 5 


============================

TCGA-COAD SCAF11 

[1] 22
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.29     0.151     0.377
strata=LOW  11        3     3.71     0.134     0.377

 Chisq= 0.4  on 1 degrees of freedom, p= 0.5 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.5182    0.5956   0.8517 -0.608 0.543

Likelihood ratio test=0.37  on 1 df, p=0.544
n= 22, number of events= 7 


============================

TCGA-COAD NLRP6 

[1] 23
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     3.19   0.01184    0.0225
strata=LOW  11        5     4.81   0.00787    0.0225

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)    z     p
strataLOW 0.1164    1.1234   0.7759 0.15 0.881

Likelihood ratio test=0.02  on 1 df, p=0.8805
n= 22, number of events= 8 


============================

TCGA-COAD AIM2 

[1] 24
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3      4.3     0.394         1
strata=LOW  11        5      3.7     0.458         1

 Chisq= 1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.8305    2.2945   0.8504 0.977 0.329

Likelihood ratio test=1.05  on 1 df, p=0.3066
n= 22, number of events= 8 


============================

TCGA-COAD CASP6 

[1] 25
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     4.06      1.04       2.5
strata=LOW  11        6     3.94      1.08       2.5

 Chisq= 2.5  on 1 degrees of freedom, p= 0.1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 1.2475    3.4817   0.8393 1.486 0.137

Likelihood ratio test=2.49  on 1 df, p=0.1143
n= 22, number of events= 8 


============================

TCGA-COAD NLRP2 

[1] 26
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     3.89     0.917      2.09
strata=LOW  11        5     3.11     1.145      2.09

 Chisq= 2.1  on 1 degrees of freedom, p= 0.1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z    p
strataLOW 1.1538    3.1704   0.8415 1.371 0.17

Likelihood ratio test=2.11  on 1 df, p=0.1463
n= 22, number of events= 7 


============================

TCGA-COAD IRF2 

[1] 27
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.33    0.1918     0.315
strata=LOW  11        5     5.67    0.0789     0.315

 Chisq= 0.3  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.4560    0.6338   0.8189 -0.557 0.578

Likelihood ratio test=0.31  on 1 df, p=0.5792
n= 22, number of events= 8 


============================

TCGA-COAD PJVK 

[1] 28
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     2.46    0.0868     0.149
strata=LOW  11        4     3.54    0.0604     0.149

 Chisq= 0.1  on 1 degrees of freedom, p= 0.7 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z   p
strataLOW 0.3355    1.3987   0.8719 0.385 0.7

Likelihood ratio test=0.15  on 1 df, p=0.6957
n= 22, number of events= 6 


============================

TCGA-COAD CASP5 

[1] 29
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.42     0.136     0.352
strata=LOW  11        1     1.58     0.210     0.352

 Chisq= 0.4  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z    p
strataLOW -0.6780    0.5076   1.1631 -0.583 0.56

Likelihood ratio test=0.37  on 1 df, p=0.5406
n= 22, number of events= 4 


============================

TCGA-COAD NLRP1 

[1] 30
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     3.69     0.131     0.329
strata=LOW  11        4     3.31     0.146     0.329

 Chisq= 0.3  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z    p
strataLOW 0.4944    1.6395   0.8707 0.568 0.57

Likelihood ratio test=0.34  on 1 df, p=0.5614
n= 22, number of events= 7 


============================

TCGA-COAD CASP9 

[1] 31
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.51     0.628      1.17
strata=LOW  11        4     5.49     0.402      1.17

 Chisq= 1.2  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z    p
strataLOW -0.7944    0.4519   0.7508 -1.058 0.29

Likelihood ratio test=1.17  on 1 df, p=0.2798
n= 22, number of events= 9 


============================

TCGA-COAD PLCG1 

[1] 32
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     4.63    0.0861     0.233
strata=LOW  11        4     3.37    0.1184     0.233

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)    z     p
strataLOW 0.3705    1.4484   0.7717 0.48 0.631

Likelihood ratio test=0.23  on 1 df, p=0.6291
n= 22, number of events= 8 


============================

TCGA-COAD IL18 

[1] 33
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.66    0.0432    0.0971
strata=LOW  11        3     3.34    0.0344    0.0971

 Chisq= 0.1  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.2899    0.7483   0.9335 -0.311 0.756

Likelihood ratio test=0.1  on 1 df, p=0.7546
n= 22, number of events= 6 


============================

TCGA-COAD DPP8 

[1] 34
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.76    0.0147    0.0372
strata=LOW  11        3     3.24    0.0171    0.0372

 Chisq= 0  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.1574    0.8543   0.8174 -0.193 0.847

Likelihood ratio test=0.04  on 1 df, p=0.8474
n= 22, number of events= 7 


============================

Display the results only for genes where a significant difference in survival has been reported.

significant_genes

[1] "CYCS"

num_significant_genes <- length(significant_genes)

if (num_significant_genes > 0) {
  for (i in 1 : num_significant_genes) {
    project <- significant_projects[[i]]
    gene <- significant_genes[[i]]
    
    cat(project, gene, "\n\n")
    gene_df <- construct_gene_df(gene, project)
    
    fit <- compute_surival_fit(gene_df)
    survival <- compute_survival_diff(gene_df)
    cox <- compute_cox(gene_df)
    print(survival)
    cat("\n")
    print(cox)
    print(plot_survival(fit))
    
    cat("\n\n============================\n\n")
  } 
}

TCGA-COAD CYCS 

Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        1      3.7      1.97       5.2
strata=LOW  11        5      2.3      3.15       5.2

 Chisq= 5.2  on 1 degrees of freedom, p= 0.02 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

           coef exp(coef) se(coef)     z     p
strataLOW 2.112     8.267    1.101 1.919 0.055

Likelihood ratio test=5.21  on 1 df, p=0.02242
n= 22, number of events= 6 


============================

---

1. De La Salle University, Manila, Philippines, gonzales.markedward@gmail.com↩︎

2. De La Salle University, Manila, Philippines, anish.shrestha@dlsu.edu.ph↩︎