sigminer (version 1.0.6)

show_sig_bootstrap: 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"),
  highlight = "auto",
  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"),
  highlight = "auto",
  palette = "aaas",
  title = NULL,
  xlab = FALSE,
  ylab = "Reconstruction error (F2 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("MRSE", "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("LS", "QP", "SA").

plot_fun

set the plot function.

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.

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

Default aesthetics for outliers. Set to NULL to inherit from the aesthetics used for the box.

In the unlikely event you specify both US and UK spellings of colour, the US spelling will take precedence.

Sometimes it can be useful to hide the outliers, for example when overlaying the raw data points on top of the boxplot. Hiding the outliers can be achieved by setting outlier.shape = NA. Importantly, this does not remove the outliers, it only hides them, so the range calculated for the y-axis will be the same with outliers shown and outliers 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_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 'MRSE', 'MAE' and 'AbsDiff'.

Value

a ggplot object

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:

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

  • '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.

References

Huang X, Wojtowicz D, Przytycka TM. Detecting presence of mutational signatures in cancer with confidence. Bioinformatics. 2018;34(2):330<U+2013>337. doi:10.1093/bioinformatics/btx604

See Also

sig_fit_bootstrap_batch, sig_fit, sig_fit_bootstrap

Examples

# NOT RUN {
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,
    pConstant = 1e-13
  )

  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)

  ## Show bootstrap exposure (optimal exposure is shown as triangle)
  p1 <- show_sig_bootstrap_exposure(bt_result, methods = c("QP"))
  p1
  p2 <- show_sig_bootstrap_exposure(bt_result,
    methods = c("QP"),
    sample = "TCGA-AB-3012",
    signatures = c("Sig1", "Sig2")
  )
  p2

  ## Show bootstrap error
  p3 <- show_sig_bootstrap_error(bt_result, methods = c("QP"))
  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
} else {
  message("Please install package 'BSgenome.Hsapiens.UCSC.hg19' firstly!")
}
# }