generate_bootstrap_plots 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
generate_bootstrap_plots( sct_data = NULL, hits = NULL, bg = NULL, genelistSpecies = NULL, sctSpecies = NULL, output_species = "human", reps = 100, annotLevel = 1, full_results = NA, listFileName = "", savePath = tempdir(), verbose = TRUE )
| sct_data | List generated using generate_celltype_data. |
|---|---|
| hits | List of gene symbols containing the target gene list.
Will automatically be converted to human gene symbols
if |
| bg | List of gene symbols containing the background gene list
(including hit genes). If |
| genelistSpecies | Species that |
| sctSpecies | Species that |
| output_species | Species to convert |
| reps | Number of random gene lists to generate (Default: 100, but should be >=10,000 for publication-quality results). |
| annotLevel | An integer indicating which level of |
| full_results | The full output of bootstrap_enrichment_test for the same gene list. |
| listFileName | String used as the root for files saved using this function. |
| savePath | Directory where the BootstrapPlots folder should be saved, default is a temp directory. |
| verbose | Print messages. |
Saves a set of pdf files containing graphs and returns the file where they are saved. These will be saved with the filename adjusted using the value of listFileName. The files are saved into the 'BootstrapPlot' folder. 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
#>#>## Set the parameters for the analysis ## Use 5 bootstrap lists for speed, for publishable analysis use >10000 reps <- 5 ## Load the gene list and get human orthologs hits <- ewceData::example_genelist()[1:100]#>#>## Bootstrap significance test, ## no control for transcript length or GC content ## Use pre-computed results to speed up example full_results <- EWCE::example_bootstrap_results() ### Skip this for example purposes # full_results <- EWCE::bootstrap_enrichment_test( # sct_data = ctd, # hits = hits, # reps = reps, # annotLevel = 1, # sctSpecies = "mouse", # genelistSpecies = "human" # ) plot_file_path <- EWCE::generate_bootstrap_plots( sct_data = ctd, hits = hits, reps = reps, full_results = full_results, listFileName = "Example", sctSpecies = "mouse", genelistSpecies = "human", annotLevel = 1, savePath = tempdir() )#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#> #>#> #>#> #>#> #>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#> #>#>#>#>#>#>#>#>#>