scMappR_and_pathway_analysis: Generate cellWeighted_Foldchange, visualize, and enrich.
In DustinSokolowski/scMappR: Single Cell Mapper

Description Usage Arguments Details Value Examples

View source: R/scMappR_and_pathway_analysis.R

This function generates cell weighted Fold-changes (cellWeighted_Foldchange), visualizes them in a heatmap, and completes pathway enrichment of cellWeighted_Foldchanges and bulk gene list.

scMappR_and_pathway_analysis(
  count_file,
  signature_matrix,
  DEG_list,
  case_grep,
  control_grep,
  rda_path = "",
  max_proportion_change = -9,
  print_plots = T,
  plot_names = "scMappR",
  theSpecies = "human",
  output_directory = "scMappR_analysis",
  sig_matrix_size = 3000,
  drop_unknown_celltype = TRUE,
  internet = TRUE,
  up_and_downregulated = FALSE,
  gene_label_size = 0.4,
  number_genes = -9,
  toSave = FALSE,
  newGprofiler = FALSE,
  path = NULL
)

`count_file`	Normalized RNA-seq count matrix where rows are gene symbols and columns are individuals. Either the object tself of the path of a .tsv file.
`signature_matrix`	Signature matrix (recommended odds ratios) of cell-type specificity of genes. Either the object itself or a pathway to a .RData file containing an object named "wilcoxon_rank_mat_or" – generally internal.
`DEG_list`	An object with the first column as gene symbols within the bulk dataset (doesn't have to be in signature matrix), second column is the adjusted p-value, and the third the log2FC path to a .tsv file containing this info is also acceptable.
`case_grep`	Tag in the column name for cases (i.e. samples representing upregulated) OR an index of cases.
`control_grep`	Tag in the column name for controls (i.e. samples representing downregulated OR an index of controls).
`rda_path`	If downloaded, path to where data from scMappR_data is stored.
`max_proportion_change`	Maximum cell-type proportion change – may be useful if there are many rare cell-types.
`print_plots`	Whether boxplots of the estimated CT proportion for the leave-one-out method of CT deconvolution should be printed. The same name of the plots will be completed for top pathways.
`plot_names`	The prefix of plot pdf files.
`theSpecies`	-9 if using a pre-computed count matrix from scMappR, human, mouse, or a specied directly compatible with gProfileR. Removes Ensembl symbols if appended.
`output_directory`	The name of the directory that will contain output of the analysis.
`sig_matrix_size`	Number of genes in signature matrix for cell-type deconvolution.
`drop_unknown_celltype`	Whether or not to remove "unknown" cell-types from the signature matrix.
`internet`	Whether you have stable Wifi (T/F).
`up_and_downregulated`	Whether you are additionally splitting up/downregulated genes (T/F).
`gene_label_size`	The size of the gene label on the plot.
`number_genes`	The number of genes to cut-off for pathway analysis (good with many DEGs).
`toSave`	Allow scMappR to write files in the current directory (T/F).
`newGprofiler`	Whether to use gProfileR or gprofiler2 (T/F).
`path`	If toSave == TRUE, path to the directory where files will be saved.

This function generates cellWeighted_Foldchanges for every cell-type (see deconvolute_and_contextualize), as well as the relative cell-type proportions (which will be reutrned and pushed through). Then, it generates heatmaps of all cellWeighted_Foldchanges, cellWeighted_Foldchanges overlapping with the signature matrix, the entire signature matrix, the cell-type preference values from the signature matrix that overlap with inputted differentially expressed genes. Then, if you have Wifi, it will complete gProfileR of the reordered cellWeighted_Foldchanges as well as a the ordered list of genes. This function is a wrapper for deconvolute_and_contextualize and pathway_enrich_internal.

List with the following elements:

`cellWeighted_Foldchanges`	Cellweighted Fold-changes for all differentially expressed genes.
`paths`	Enriched biological pathways for each cell-type.
`TFs`	Enirched TFs for each cell-type.

data(PBMC_example)
bulk_DE_cors <- PBMC_example$bulk_DE_cors
bulk_normalized <- PBMC_example$bulk_normalized
odds_ratio_in <- PBMC_example$odds_ratio_in
case_grep <- "_female"
control_grep <- "_male"
max_proportion_change <- 10
print_plots <- FALSE
theSpecies <- "human"
toOut <- scMappR_and_pathway_analysis(bulk_normalized, odds_ratio_in, 
                                      bulk_DE_cors, case_grep = case_grep,
                                      control_grep = control_grep, rda_path = "", 
                                      max_proportion_change = 10, print_plots = TRUE, 
                                       plot_names = "tst1", theSpecies = "human", 
                                       output_directory = "tester",
                                       sig_matrix_size = 3000, up_and_downregulated = FALSE, 
                                       internet = FALSE)