RCTD-class: An S4 class used to run the RCTD and CSIDE algorithms

In lulizou/spASE: Detecting Allele-Specific Expression in Spatial Transcriptomics

An S4 class used to run the RCTD and CSIDE algorithms

Description

Created using the create.RCTD function, a user can run RCTD using the run.RCTD function.

Slots

spatialRNA

a SpatialRNA object containing the Spatial RNA dataset to be used for RCTD

originalSpatialRNA

a SpatialRNA object containing the Spatial RNA dataset with all genes

reference

a Reference object containing the cell type-labeled single cell reference

config

a list of configuration options, set using the create.RCTD function

cell_type_info

a named list of cell type profiles (means), containing two elements: info, directly calculated from the scRNA-seq reference, and renorm, renormalized the match the SpatialRNA dataset.

internal_vars

a list of internal variables used by RCTD's computation

results

(created after running RCTD) a list of results_df (a dataframe of RCTD results in doublet mode), weights (a dataframe of RCTD predicted weights in full mode), and weights_doublet (a dataframe of predicted weights in doublet mode, with cell type information in results_df).

In doublet-mode, The results of 'doublet_mode' are stored in '@results$results_df' and '@results$weights_doublet', the weights of each cell type. More specifically, the 'results_df' object contains one column per pixel (barcodes as rownames). Important columns are: * ‘spot_class', a factor variable representing RCTD’s classification in doublet mode: "singlet" (1 cell type on pixel), "doublet_certain" (2 cell types on pixel), "doublet_uncertain" (2 cell types on pixel, but only confident of 1), "reject" (no prediction given for pixel). * Next, the 'first_type' column gives the first cell type predicted on the bead (for all spot_class conditions except "reject"). * The 'second_type column' gives the second cell type predicted on the bead for doublet spot_class conditions (not a confident prediction for "doublet_uncertain").

Note that in multi-mode, results consists of a list of results for each pixel, which contains all_weights (weights from full mode), cell_type_list (cell types on multi mode), conf_list (which cell types are confident on multi mode) and sub_weights (proportions of cell types on multi mode).

de_results

results of the CSIDE algorithm. Contains 'gene_fits', which contains the results of fits on individual genes, whereas 'res_gene_list' is a list, for each cell type, of significant genes detected by CSIDE.

internal_vars_de

a list of variables that are used internally by CSIDE

lulizou/spASE documentation built on May 22, 2024, 5:24 a.m.