variateExp {YAPSA} | R Documentation

## Wrapper to compute confidence intervals for a cohort

### 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)

[Package YAPSA version 1.18.0 Index]