dot-EIF_correlation: Identify EIF4F correlating genes

In a3609640/eIF4F.analysis: A R package to analyze eIF4F subunits in human cancers

Identify EIF4F correlating genes

Description

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.

Usage

.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

## Not run: 
.EIF_correlation(
  df = .TCGA_GTEX_RNAseq_sampletype_subset,
  sample_type = all.tumor.type
)

## End(Not run)

a3609640/eIF4F.analysis documentation built on Jan. 2, 2023, 11:19 p.m.