scMappR (version 1.0.12)

tissue_scMappR_internal: Gene List Visualization and Enrichment (Internal)

Description

This function loops through every signature matrix in a particular tissue and generates heatmaps, cell-type preferences, and co-enrichment.

Usage

tissue_scMappR_internal(
  gene_list,
  species,
  output_directory,
  tissue,
  rda_path = "",
  cluster = "Pval",
  genecex = 0.01,
  raw_pval = FALSE,
  path = NULL,
  toSave = FALSE,
  drop_unkown_celltype = FALSE
)

Value

List with the following elements:

background_heatmap

Data frame of the entire gene by cell-type signature matrix inputted.

gene_list_heatmap

Data frame of inputted signature matrix subsetted by input genes.

single_celltype_preferences

Data frame of enriched cell-types.

group_celtype_preference

Data frame of groups of cell-types enriched by the same genes.

Arguments

gene_list

A list of gene symbols, mouse or human.

species

"mouse", "human" or "-9" if using a precomputed signature matrix.

output_directory

If toSave = TRUE, the name of the output directory that would be built.

tissue

Name of the tissue in "get_tissues".

rda_path

Path to the .rda file containing all of the signature matrices.

cluster

'Pval' or 'OR' depending on if you want to cluster odds ratios or p-values of cell-type preferences.

genecex

The size of the gene names of the rows in the heatmap.

raw_pval

If the inputted signature matrix are raw (untransformed) p-values -- recommended to generate rank first (T/F).

path

If toSave == TRUE, path to the directory where files will be saved.

toSave

Allow scMappR to write files in the current directory (T/F).

drop_unkown_celltype

Whether or not to remove "unknown" cell-types from the signature matrix (T/F).

Details

This function takes a list of genes and a tissue that is contained in current signature matrices before and generating heatmaps of cell-type preferences. It then completes cell-type enrichment of each individual cell-type, then, if more than two cell-types are significantly enriched, co-enrichment. of those enriched cell-types is then computed.

Examples

# \donttest{

data(POA_example) # region to preoptic area
Signature <- POA_example$POA_Rank_signature # signature matrix
rowname <- get_gene_symbol(Signature) # get signature
rownames(Signature) <- rowname$rowname
genes <- rownames(Signature)[1:60]
rda_path1 = "" # data directory (if it exists)

# set toSave = TRUE and path = output directory of your choice
internal <- tissue_scMappR_internal(gene_list = genes, species = "mouse",
                                   output_directory = "scMappR_TesInternal",
                                   tissue = "hypothalamus", toSave = FALSE) 

 # }