Identify EIF4F correlating genes

This function

• takes the specific data frames .TCGA_GTEX_RNAseq_sampletype_subset and sample_type that are generated inside .plot_Corr_RNAseq_TCGA_GTEX()

• calculates the correlation coefficiency between each EIF4F gene and the rest of cellular mRNAs with .correlation_coefficient()

• combines the correlation coefficiency data from EIF4E, EIF4A1, EIF4G1, and EIF4EBP1

• selects positive correlating genes with .is_significant_poscor() and negative correlating genes with .is_significant_negcor()

• summarizes the total number of posCORs or negCORs identified for each EIF4F gene with .summarize_counts()

It should not be used directly, only inside .plot_Corr_RNAseq_TCGA_GTEX() function.

.EIF_correlation(df, sample_type)

Arguments

df

the data frame .TCGA_GTEX_RNAseq_sampletype_subset generated inside .plot_Corr_RNAseq_TCGA_GTEX()

sample_type

sample types, either all.tumor.type or c("Normal Tissue") generated inside .plot_Corr_RNAseq_TCGA_GTEX()

Value

a list output with four elements:

• cor_value_combined for the heatmap

• CORs_summary_tbl for bargraph

• posCOR_EIF4F for Venn plots

• negCOR_EIF4F for Venn plots

See also

Other helper function to identify correlating genes for EIF4F genes: .correlation_coefficient(), .is_significant_negcor(), .is_significant_poscor(), .summarize_counts()

Examples

if (FALSE) {
.EIF_correlation(
  df = .TCGA_GTEX_RNAseq_sampletype_subset,
  sample_type = all.tumor.type
)
}