RunDynamicEnrichment: RunDynamicEnrichment
In zh542370159/SCP: Single Cell Pipeline

RunDynamicEnrichment

R Documentation

RunDynamicEnrichment

Description

This function calculates gene-set scores from the specified database (db) for each lineage using the specified scoring method (score_method). It then treats these scores as expression values and uses them as input to the RunDynamicFeatures function to identify dynamically enriched terms along the lineage.

Usage

RunDynamicEnrichment(
  srt,
  lineages,
  score_method = "AUCell",
  slot = "data",
  assay = NULL,
  min_expcells = 20,
  r.sq = 0.2,
  dev.expl = 0.2,
  padjust = 0.05,
  IDtype = "symbol",
  species = "Homo_sapiens",
  db = "GO_BP",
  db_update = FALSE,
  db_version = "latest",
  convert_species = TRUE,
  Ensembl_version = 103,
  mirror = NULL,
  TERM2GENE = NULL,
  TERM2NAME = NULL,
  minGSSize = 10,
  maxGSSize = 500,
  BPPARAM = BiocParallel::bpparam(),
  seed = 11
)

Arguments

`srt`	A Seurat object containing the results of differential expression analysis (RunDEtest). If specified, the genes and groups will be extracted from the Seurat object automatically. If not specified, the `geneID` and `geneID_groups` arguments must be provided.
`lineages`	A character vector specifying the lineages to plot.
`score_method`	The method to use for scoring. Can be "Seurat", "AUCell", or "UCell". Defaults to "Seurat".
`slot`	A character vector specifying the slot in the Seurat object to use. Default is "counts".
`assay`	A character vector specifying the assay in the Seurat object to use. Default is NULL.
`min_expcells`	A numeric value specifying the minimum number of expected cells. Default is 20.
`r.sq`	A numeric value specifying the R-squared threshold. Default is 0.2.
`dev.expl`	A numeric value specifying the deviance explained threshold. Default is 0.2.
`padjust`	A numeric value specifying the p-value adjustment threshold. Default is 0.05.
`IDtype`	A character vector specifying the type of gene IDs in the `srt` object or `geneID` argument. This argument is used to convert the gene IDs to a different type if `IDtype` is different from `result_IDtype`.
`species`	A character vector specifying the species for which the analysis is performed.
`db`	A character vector specifying the name of the database to be used for enrichment analysis.
`db_update`	A logical value indicating whether the gene annotation databases should be forcefully updated. If set to FALSE, the function will attempt to load the cached databases instead. Default is FALSE.
`db_version`	A character vector specifying the version of the database to be used. This argument is ignored if `db_update` is `TRUE`. Default is "latest".
`convert_species`	A logical value indicating whether to use a species-converted database when the annotation is missing for the specified species. The default value is TRUE.
`Ensembl_version`	Ensembl database version. If NULL, use the current release version.
`mirror`	Specify an Ensembl mirror to connect to. The valid options here are 'www', 'uswest', 'useast', 'asia'.
`TERM2GENE`	A data frame specifying the gene-term mapping for a custom database. The first column should contain the term IDs, and the second column should contain the gene IDs.
`TERM2NAME`	A data frame specifying the term-name mapping for a custom database. The first column should contain the term IDs, and the second column should contain the corresponding term names.
`minGSSize`	A numeric value specifying the minimum size of a gene set to be considered in the enrichment analysis.
`maxGSSize`	A numeric value specifying the maximum size of a gene set to be considered in the enrichment analysis.
`BPPARAM`	A BiocParallelParam object specifying the parallel back-end to be used for parallel computation. Defaults to BiocParallel::bpparam().
`seed`	The random seed for reproducibility. Defaults to 11.

Examples

data("pancreas_sub")
pancreas_sub <- RunSlingshot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP")
pancreas_sub <- RunDynamicFeatures(pancreas_sub, lineages = "Lineage1", n_candidates = 200)
ht1 <- DynamicHeatmap(
  srt = pancreas_sub,
  lineages = "Lineage1",
  cell_annotation = "SubCellType",
  n_split = 4
)
ht1$plot

pancreas_sub <- RunDynamicEnrichment(
  srt = pancreas_sub,
  lineages = "Lineage1",
  score_method = "AUCell",
  db = "GO_BP",
  species = "Mus_musculus"
)
ht2 <- DynamicHeatmap(
  srt = pancreas_sub,
  assay = "GO_BP",
  lineages = "Lineage1_GO_BP",
  cell_annotation = "SubCellType",
  n_split = 4,
  split_method = "kmeans-peaktime"
)
ht2$plot

zh542370159/SCP documentation built on Nov. 22, 2023, 2:34 a.m.