bootstrap_enrichment_test takes a genelist and a single cell type transcriptome dataset and determines the probability of enrichment and fold changes for each cell type.

bootstrap_enrichment_test(
  sct_data = NULL,
  hits = NULL,
  bg = NULL,
  genelistSpecies = NULL,
  sctSpecies = NULL,
  sctSpecies_origin = sctSpecies,
  output_species = "human",
  method = "homologene",
  reps = 100,
  no_cores = 1,
  annotLevel = 1,
  geneSizeControl = FALSE,
  controlledCT = NULL,
  mtc_method = "BH",
  sort_results = TRUE,
  standardise_sct_data = TRUE,
  standardise_hits = FALSE,
  verbose = TRUE,
  localHub = FALSE,
  store_gene_data = TRUE
)

Arguments

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 geneSizeControl=TRUE.

bg

List of gene symbols containing the background gene list (including hit genes). If bg=NULL, an appropriate gene background will be created automatically.

genelistSpecies

Species that hits genes came from (no longer limited to just "mouse" and "human"). See list_species for all available species.

sctSpecies

Species that sct_data is currently formatted as (no longer limited to just "mouse" and "human"). See list_species for all available species.

sctSpecies_origin

Species that the sct_data originally came from, regardless of its current gene format (e.g. it was previously converted from mouse to human gene orthologs). This is used for computing an appropriate backgrund.

output_species

Species to convert sct_data and hits to (Default: "human"). See list_species for all available species.

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.

reps

Number of random gene lists to generate (Default: 100, but should be >=10,000 for publication-quality results).

no_cores

Number of cores to parallelise bootstrapping reps over.

annotLevel

An integer indicating which level of sct_data to analyse (Default: 1).

geneSizeControl

Whether you want to control for GC content and transcript length. Recommended if the gene list originates from genetic studies (Default: FALSE). If set to TRUE, then hits must be from humans.

controlledCT

[Optional] If not NULL, and instead is the name of a cell type, then the bootstrapping controls for expression within that cell type.

mtc_method

Multiple-testing correction method (passed to p.adjust).

sort_results

Sort enrichment results from smallest to largest p-values.

standardise_sct_data

Should sct_data be standardised? if TRUE:

  • When sctSpecies!=output_species the sct_data will be checked for object formatting and the genes will be converted to the orthologs of the output_species with standardise_ctd (which calls map_genes internally).

  • When sctSpecies==output_species, the sct_data will be checked for object formatting with standardise_ctd, but the gene names will remain untouched.

standardise_hits

Should hits be standardised? If TRUE:

  • When genelistSpecies!=output_species, the genes will be converted to the orthologs of the output_species with convert_orthologs.

  • When genelistSpecies==output_species, the genes will be standardised with map_genes.

If FALSE, hits will be passed on to subsequent steps as-is.

verbose

Print messages.

localHub

If working offline, add argument localHub=TRUE to work with a local, non-updated hub; It will only have resources available that have previously been downloaded. If offline, Please also see BiocManager vignette section on offline use to ensure proper functionality.

store_gene_data

Store sampled gene data for every bootstrap iteration. When the number of bootstrap reps is very high (>=100k) and/or the number of genes in hits is very high, you may want to set store_gene_data=FALSE to avoid using excessive amounts of CPU memory.

Value

A list containing three elements:

  • hit.cells: vector containing the summed proportion of expression in each cell type for the target list.

  • gene_data: data.table showing the number of time each gene appeared in the bootstrap sample.

  • bootstrap_data: matrix in which each row represents the summed proportion of expression in each cell type for one of the random lists

  • controlledCT: the controlled cell type (if applicable)

Examples

# Load the single cell data
sct_data <- ewceData::ctd()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
# Set the parameters for the analysis
# Use 3 bootstrap lists for speed, for publishable analysis use >=10,000
reps <- 3
# Load gene list from Alzheimer's disease GWAS
hits <- ewceData::example_genelist()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache

# Bootstrap significance test, no control for transcript length or GC content
full_results <- EWCE::bootstrap_enrichment_test(
    sct_data = sct_data,
    hits = hits,
    reps = reps,
    annotLevel = 1,
    sctSpecies = "mouse",
    genelistSpecies = "human")
#> 1 core(s) assigned as workers (3 reserved).
#> Generating gene background for mouse x human ==> human
#> Gathering ortholog reports.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Gene table with 19,129 rows retrieved.
#> Returning all 19,129 genes from human.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: mouse
#> Common name mapping found for mouse
#> 1 organism identified from search: 10090
#> Gene table with 21,207 rows retrieved.
#> Returning all 21,207 genes from mouse.
#> --
#> --
#> Preparing gene_df.
#> data.frame format detected.
#> Extracting genes from Gene.Symbol.
#> 21,207 genes extracted.
#> Converting mouse ==> human orthologs using: homologene
#> Retrieving all organisms available in homologene.
#> Mapping species name: mouse
#> Common name mapping found for mouse
#> 1 organism identified from search: 10090
#> Retrieving all organisms available in homologene.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Checking for genes without orthologs in human.
#> Extracting genes from input_gene.
#> 17,355 genes extracted.
#> Extracting genes from ortholog_gene.
#> 17,355 genes extracted.
#> Checking for genes without 1:1 orthologs.
#> Dropping 131 genes that have multiple input_gene per ortholog_gene (many:1).
#> Dropping 498 genes that have multiple ortholog_gene per input_gene (1:many).
#> Filtering gene_df with gene_map
#> Adding input_gene col to gene_df.
#> Adding ortholog_gene col to gene_df.
#> 
#> =========== REPORT SUMMARY ===========
#> Total genes dropped after convert_orthologs :
#>    4,725 / 21,207 (22%)
#> Total genes remaining after convert_orthologs :
#>    16,482 / 21,207 (78%)
#> --
#> 
#> =========== REPORT SUMMARY ===========
#> 16,482 / 21,207 (77.72%) target_species genes remain after ortholog conversion.
#> 16,482 / 19,129 (86.16%) reference_species genes remain after ortholog conversion.
#> Gathering ortholog reports.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Gene table with 19,129 rows retrieved.
#> Returning all 19,129 genes from human.
#> Retrieving all genes using: homologene.
#> Retrieving all organisms available in homologene.
#> Mapping species name: human
#> Common name mapping found for human
#> 1 organism identified from search: 9606
#> Gene table with 19,129 rows retrieved.
#> Returning all 19,129 genes from human.
#> --
#> 
#> =========== REPORT SUMMARY ===========
#> 19,129 / 19,129 (100%) target_species genes remain after ortholog conversion.
#> 19,129 / 19,129 (100%) reference_species genes remain after ortholog conversion.
#> 16,482 intersect background genes used.
#> Standardising CellTypeDataset
#> Checking gene list inputs.
#> Running without gene size control.
#> 17 hit gene(s) remain after filtering.
#> Computing gene scores.
#> Using previously sampled genes.
#> Computing gene counts.
#> Testing for enrichment in 7 cell types...
#> Sorting results by p-value.
#> Computing BH-corrected q-values.
#> 2 significant cell type enrichment results @ q<0.05 : 
#>               CellType annotLevel p fold_change sd_from_mean q
#> 1            microglia          1 0    2.308812     7.094535 0
#> 2 astrocytes_ependymal          1 0    1.484770     1.821994 0