process_dgTMatrix_lists: Count Matrix To Signature Matrix

In scMappR: Single Cell Mapper

Count Matrix To Signature Matrix

Description

This function takes a list of count matrices, processes them, calls cell-types, and generates signature matrices.

Usage

process_dgTMatrix_lists(
  dgTMatrix_list,
  name,
  species_name,
  naming_preference = -9,
  rda_path = "",
  panglao_set = FALSE,
  haveUMAP = FALSE,
  saveSCObject = FALSE,
  internal = FALSE,
  toSave = FALSE,
  path = NULL,
  use_sctransform = FALSE,
  test_ctname = "wilcox",
  genes_integrate = 2000,
  genes_include = FALSE
)

Arguments

dgTMatrix_list	A list of matrices in the class of dgTMatrix object – sparce object – compatible with Seurat rownames should be of the same species for each.
name	The name of the outputted signature matrices, cell-type preferences, and Seurat objects if you choose to save them.
species_name	Mouse or human symbols, -9 if internal as Panglao objects have gene symbol and ensembl combined.
naming_preference	For cell-type naming, see if cell-types given the inputted tissues are more likely to be named within one of the categories. These categories are: "brain", "epithelial", "endothelial", "blood", "connective","eye", "epidermis", "Digestive", "Immune", "pancreas", "liver", "reproductive", "kidney", "respiratory".
rda_path	If saved, directory to where data from scMappR_data is downloaded.
panglao_set	If the inputted matrices are from Panglao (i.e. if they're internal).
haveUMAP	Save the UMAPs - requires additional packages (see Seurat for details).
saveSCObject	Save the Seurat object as an RData object (T/F).
internal	Was this used as part of the internal processing of Panglao datasets (T/F).
toSave	Allow scMappR to write files in the current directory (T/F)
path	If toSave == TRUE, path to the directory where files will be saved.
use_sctransform	If you should use sctransform or the Normalize/VariableFeatures/ScaleData pipeline (T/F).
test_ctname	statistical test for calling CT markers – must be in Seurat
genes_integrate	The number of genes to include in the integration anchors feature when combining datasets.
genes_include	TRUE or FALSE – include 2000 genes in signature matrix or all matrix.

Details

This function is a one line wrapper to process count matrices into a signature matrix. It combines process_from_count, two methods of identifying cell-type identities (GSVA and Fisher's test). Then, it takes the output of cell-type markers and converts it into a signature matrix of p-value ranks and odds ratios. It saves the Seurat object (if chosen with saveSCObject), cell-type identities from GSVA (its own object), and the signature matrices. Cell-type marker outputs are also saved in the generes .RData list. This is a list of cell-types containing all of the cell-type markers found with the FindMarkers function. Names of the generes lists and the signature matrices are kept.

Value

List with the following elements:

wilcoxon_rank_mat_t	A dataframe containing the signature matrix of ranks (-log10(Padj) * sign(fold-change)).
wilcoxon_rank_mat_or	A dataframe containing the signature matrix of odds-ratios.
generes	All cell-type markers for each cell-type with p-value and fold changes.
cellLabel	matrix where each row is a cluster and each column provides information on the cell-type. Columns provide info on the cluster from seurat, the cell-type label from CellMarker and Panglao using the fisher's exact test and GSVA, and the top 30 markers per cluser.

Examples

data(sm)
toProcess <- list(example = sm)
tst1 <- process_dgTMatrix_lists(dgTMatrix_list = toProcess, name = "testProcess",
                               species_name = "mouse", naming_preference = "eye", rda_path = "")

scMappR documentation built on July 9, 2023, 6:26 p.m.