variateExp: Wrapper to compute confidence intervals for a cohort

In YAPSA: Yet Another Package for Signature Analysis

Description

Wrapper function around confIntExp, which is applied to every signature/sample pair in a cohort. The extracted upper and lower bounds of the confidence intervals are added to the input data which is reordered and melted in order to prepare for visualization with ggplot2.

Usage

variateExp(
  in_catalogue_df,
  in_sig_df,
  in_exposures_df,
  in_sigLevel = 0.05,
  in_delta = 0.4,
  in_pdf = NULL
)

Arguments

in_catalogue_df	Input numerical data frame of the mutational catalog of the cohort to be analyzed.
in_sig_df	Numerical data frame of the signatures used for analysis.
in_exposures_df	Input numerical data frame of the exposures computed for the cohort to be analyzed.
in_sigLevel	Significance level, parameter passed to confIntExp.
in_delta	Inflation parameter for the alternative model, parameter passed on to confIntExp
in_pdf	Probability distribution function, parameter passed on to confIntExp, if NULL assumed to be normal distribution.

Value

A melted data frame.

Examples

 library(BSgenome.Hsapiens.UCSC.hg19)
 data(lymphoma_test)
 data(lymphoma_cohort_LCD_results)
 data(sigs)
 word_length <- 3
 temp_list <- create_mutation_catalogue_from_df(
   lymphoma_test_df,this_seqnames.field = "CHROM",
   this_start.field = "POS",this_end.field = "POS",
   this_PID.field = "PID",this_subgroup.field = "SUBGROUP",
   this_refGenome = BSgenome.Hsapiens.UCSC.hg19,
   this_wordLength = word_length)
 lymphoma_catalogue_df <- temp_list$matrix
 lymphoma_PIDs <- colnames(lymphoma_catalogue_df)
 data("lymphoma_cohort_LCD_results")
 lymphoma_exposures_df <-
   lymphoma_Nature2013_COSMIC_cutoff_exposures_df[,lymphoma_PIDs]
 lymphoma_sigs <- rownames(lymphoma_exposures_df)
 lymphoma_sig_df <- AlexCosmicValid_sig_df[,lymphoma_sigs]
 lymphoma_complete_df <- variateExp(in_catalogue_df = lymphoma_catalogue_df,
                                    in_sig_df = lymphoma_sig_df,
                                    in_exposures_df = lymphoma_exposures_df,
                                    in_sigLevel = 0.025, in_delta = 0.4)
 head(lymphoma_complete_df)
 lymphoma_complete_df$sample <-
   factor(lymphoma_complete_df$sample,
          levels = colnames(lymphoma_exposures_df)[
            order(colSums(lymphoma_exposures_df), decreasing = TRUE)])
 sig_colour_vector <- c("black", AlexCosmicValid_sigInd_df$colour)
 names(sig_colour_vector) <-
   c("total", as.character(AlexCosmicValid_sigInd_df$sig))
 ggplot(data = lymphoma_complete_df,
        aes(x = sample, y = exposure, fill = sig)) +
   geom_bar(stat = "identity") +
   geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
   facet_wrap(~sig, nrow = nrow(lymphoma_exposures_df) + 1) +
   theme_grey() +
   theme(panel.border = element_rect(fill = NA, colour = "black"),
         strip.background = element_rect(colour = "black"),
         legend.position = "none") +
   scale_fill_manual(values = sig_colour_vector)
 

YAPSA documentation built on Nov. 8, 2020, 4:59 p.m.