sigminer (version 2.3.1)

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"),
  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),
  ...
)

Value

a ggplot object

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.

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

# \donttest{
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!")
}
# }