generate_bootstrap_plots_for_transcriptome: Generate bootstrap plots

In NathanSkene/EWCE: Expression Weighted Celltype Enrichment

Generate bootstrap plots

Description

Takes a gene list and a single cell type transcriptome dataset and generates plots which show how the expression of the genes in the list compares to those in randomly generated gene lists.

Usage

generate_bootstrap_plots_for_transcriptome(
  sct_data,
  tt,
  bg = NULL,
  thresh = 250,
  annotLevel = 1,
  reps = 100,
  full_results = NA,
  listFileName = "",
  showGNameThresh = 25,
  ttSpecies = NULL,
  sctSpecies = NULL,
  output_species = NULL,
  sortBy = "t",
  sig_only = TRUE,
  sig_col = "q",
  sig_thresh = 0.05,
  celltype_col = "CellType",
  plot_types = c("bootstrap", "bootstrap_distributions", "log_bootstrap_distributions"),
  save_dir = file.path(tempdir(), "BootstrapPlots"),
  method = "homologene",
  verbose = TRUE
)

Arguments

sct_data	List generated using generate_celltype_data.
tt	Differential expression table. Can be output of topTable function. Minimum requirement is that one column stores a metric of increased/decreased expression (i.e. log fold change, t-statistic for differential expression etc) and another contains gene symbols.
bg	List of gene symbols containing the background gene list (including hit genes). If bg=NULL, an appropriate gene background will be created automatically.
thresh	The number of up- and down- regulated genes to be included in each analysis (Default: 250).
annotLevel	An integer indicating which level of sct_data to analyse (Default: 1).
reps	Number of random gene lists to generate (Default: 100, but should be >=10,000 for publication-quality results).
full_results	The full output of ewce_expression_data for the same gene list.
listFileName	String used as the root for files saved using this function.
showGNameThresh	Integer. If a gene has over X percent of it's expression proportion in a cell type, then list the gene name.
ttSpecies	The species the differential expression table was generated from.
sctSpecies	Species that sct_data is currently formatted as (no longer limited to just "mouse" and "human"). See list_species for all available species.
output_species	Species to convert sct_data and hits to (Default: "human"). See list_species for all available species.
sortBy	Column name of metric in tt which should be used to sort up- from down- regulated genes (Default: "t").
sig_only	Should plots only be generated for cells which have significant changes?
sig_col	Column name in tt that contains the significance values.
sig_thresh	Threshold by which to filter tt by sig_col.
celltype_col	Column within tt that contains celltype names.
plot_types	Plot types to generate.
save_dir	Directory where the BootstrapPlots folder should be saved, default is a temp directory.
method	R package to use for gene mapping: "gprofiler" : Slower but more species and genes. "homologene" : Faster but fewer species and genes. "babelgene" : Faster but fewer species and genes. Also gives consensus scores for each gene mapping based on a several different data sources.
verbose	Print messages.

Value

Saves a set of PDF files containing graphs. Then returns a nested list with each plot and the path where it was saved to. Files start with one of the following:

• qqplot_noText: sorts the gene list according to how enriched it is in the relevant cell type. Plots the value in the target list against the mean value in the bootstrapped lists.

• qqplot_wtGSym: as above but labels the gene symbols for the highest expressed genes.

• bootDists: rather than just showing the mean of the bootstrapped lists, a boxplot shows the distribution of values

• bootDists_LOG: shows the bootstrapped distributions with the y-axis shown on a log scale

Examples

## Load the single cell data
ctd <- ewceData::ctd()

## Set the parameters for the analysis
## Use 3 bootstrap lists for speed, for publishable analysis use >10,000
reps <- 3
annotLevel <- 1 # <- Use cell level annotations (i.e. Interneurons)
## Use 5 up/down regulated genes (thresh) for speed, default is 250
thresh <- 5

## Load the top table
tt_alzh <- ewceData::tt_alzh()

## See ?example_transcriptome_results for full code to produce tt_results
tt_results <- EWCE::example_transcriptome_results()

## Bootstrap significance test,
## no control for transcript length or GC content
savePath <- EWCE::generate_bootstrap_plots_for_transcriptome(
    sct_data = ctd,
    tt = tt_alzh,
    thresh = thresh,
    annotLevel = 1,
    full_results = tt_results,
    listFileName = "examples",
    reps = reps,
    ttSpecies = "human",
    sctSpecies = "mouse", 
    # Only do one plot type for demo purposes
    plot_types = "bootstrap" 
) 

NathanSkene/EWCE documentation built on Nov. 3, 2024, 8:16 a.m.