process_dgTMatrix_lists function

Count Matrix To Signature Matrix

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

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.

Returns

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.

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.

Examples

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