show_sig_bootstrap: Show Signature Bootstrap Analysis Results

In sigminer: Extract, Analyze and Visualize Mutational Signatures for Genomic Variations

Show Signature Bootstrap Analysis Results

Description

See details for description.

Usage

show_sig_bootstrap_exposure(
  bt_result,
  sample = NULL,
  signatures = NULL,
  methods = "QP",
  plot_fun = c("boxplot", "violin"),
  agg_fun = c("mean", "median", "min", "max"),
  highlight = "auto",
  highlight_size = 4,
  palette = "aaas",
  title = NULL,
  xlab = FALSE,
  ylab = "Signature exposure",
  width = 0.3,
  dodge_width = 0.8,
  outlier.shape = NA,
  add = "jitter",
  add.params = list(alpha = 0.3),
  ...
)

show_sig_bootstrap_error(
  bt_result,
  sample = NULL,
  methods = "QP",
  plot_fun = c("boxplot", "violin"),
  agg_fun = c("mean", "median"),
  highlight = "auto",
  highlight_size = 4,
  palette = "aaas",
  title = NULL,
  xlab = FALSE,
  ylab = "Reconstruction error (L2 norm)",
  width = 0.3,
  dodge_width = 0.8,
  outlier.shape = NA,
  add = "jitter",
  add.params = list(alpha = 0.3),
  legend = "none",
  ...
)

show_sig_bootstrap_stability(
  bt_result,
  signatures = NULL,
  measure = c("RMSE", "CV", "MAE", "AbsDiff"),
  methods = "QP",
  plot_fun = c("boxplot", "violin"),
  palette = "aaas",
  title = NULL,
  xlab = FALSE,
  ylab = "Signature instability",
  width = 0.3,
  outlier.shape = NA,
  add = "jitter",
  add.params = list(alpha = 0.3),
  ...
)

Arguments

bt_result	result object from sig_fit_bootstrap_batch.
sample	a sample id.
signatures	signatures to show.
methods	a subset of c("NNLS", "QP", "SA").
plot_fun	set the plot function.
agg_fun	set the aggregation function when sample is NULL.
highlight	set the color for optimal solution. Default is "auto", which use the same color as bootstrap results, you can set it to color like "red", "gold", etc.
highlight_size	size for highlighting triangle, default is 4.
palette	the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
title	plot main title.
xlab	character vector specifying x axis labels. Use xlab = FALSE to hide xlab.
ylab	character vector specifying y axis labels. Use ylab = FALSE to hide ylab.
width	numeric value between 0 and 1 specifying box width.
dodge_width	dodge width.
outlier.shape	point shape of outlier. Default is 19. To hide outlier, specify outlier.shape = NA. When jitter is added, then outliers will be automatically hidden.
add	character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.
add.params	parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").
...	other parameters passing to ggpubr::ggboxplot or ggpubr::ggviolin.
legend	character specifying legend position. Allowed values are one of c("top", "bottom", "left", "right", "none"). To remove the legend use legend = "none". Legend position can be also specified using a numeric vector c(x, y); see details section.
measure	measure to estimate the exposure instability, can be one of 'RMSE', 'CV', 'MAE' and 'AbsDiff'.

Details

Functions:

• show_sig_bootstrap_exposure - this function plots exposures from bootstrap samples with both dotted boxplot. The optimal exposure (the exposure from original input) is shown as triangle point. Only one sample can be plotted.

• show_sig_bootstrap_error - this function plots decomposition errors from bootstrap samples with both dotted boxplot. The error from optimal solution (the decomposition error from original input) is shown as triangle point. Only one sample can be plotted.

• show_sig_bootstrap_stability - this function plots the signature exposure instability for specified signatures. Currently, the instability measure supports 3 types:

  • 'RMSE' for Mean Root Squared Error (default) of bootstrap exposures and original exposures for each sample.

  • 'CV' for Coefficient of Variation (CV) based on RMSE (i.e. RMSE / btExposure_mean).

  • 'MAE' for Mean Absolute Error of bootstrap exposures and original exposures for each sample.

  • 'AbsDiff' for Absolute Difference between mean bootstram exposure and original exposure.

Value

a ggplot object

References

Huang X, Wojtowicz D, Przytycka TM. Detecting presence of mutational signatures in cancer with confidence. Bioinformatics. 2018;34(2):330–337. doi:10.1093/bioinformatics/btx604

See Also

sig_fit_bootstrap_batch, sig_fit, sig_fit_bootstrap

Examples

if (require("BSgenome.Hsapiens.UCSC.hg19")) {
  laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
  laml <- read_maf(maf = laml.maf)
  mt_tally <- sig_tally(
    laml,
    ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
    use_syn = TRUE
  )

  library(NMF)
  mt_sig <- sig_extract(mt_tally$nmf_matrix,
    n_sig = 3,
    nrun = 2,
    cores = 1
  )

  mat <- t(mt_tally$nmf_matrix)
  mat <- mat[, colSums(mat) > 0]
  bt_result <- sig_fit_bootstrap_batch(mat, sig = mt_sig, n = 10)
  ## Parallel computation
  ## bt_result = sig_fit_bootstrap_batch(mat, sig = mt_sig, n = 10, use_parallel = TRUE)

  ## At default, mean bootstrap exposure for each sample has been calculated
  p <- show_sig_bootstrap_exposure(bt_result, methods = c("QP"))
  ## Show bootstrap exposure (optimal exposure is shown as triangle)
  p1 <- show_sig_bootstrap_exposure(bt_result, methods = c("QP"), sample = "TCGA-AB-2802")
  p1
  p2 <- show_sig_bootstrap_exposure(bt_result,
    methods = c("QP"),
    sample = "TCGA-AB-3012",
    signatures = c("Sig1", "Sig2")
  )
  p2

  ## Show bootstrap error
  ## Similar to exposure above
  p <- show_sig_bootstrap_error(bt_result, methods = c("QP"))
  p
  p3 <- show_sig_bootstrap_error(bt_result, methods = c("QP"), sample = "TCGA-AB-2802")
  p3

  ## Show exposure (in)stability
  p4 <- show_sig_bootstrap_stability(bt_result, methods = c("QP"))
  p4
  p5 <- show_sig_bootstrap_stability(bt_result, methods = c("QP"), measure = "MAE")
  p5
  p6 <- show_sig_bootstrap_stability(bt_result, methods = c("QP"), measure = "AbsDiff")
  p6
  p7 <- show_sig_bootstrap_stability(bt_result, methods = c("QP"), measure = "CV")
  p7
} else {
  message("Please install package 'BSgenome.Hsapiens.UCSC.hg19' firstly!")
}

sigminer documentation built on May 29, 2024, 3:11 a.m.