newSlalomModel: Create a new SlalomModel object.

In PMBio/Rslalom: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data

Description

Slalom fits relatively complicated hierarchical Bayesian factor analysis models with data and results stored in a "SlalomModel" object. This function builds a new "SlalomModel" object from minimal inputs.

Usage

newSlalomModel(object, genesets, n_hidden = 5, prune_genes = TRUE,
  min_genes = 15, design = NULL, anno_fpr = 0.01, anno_fnr = 0.001,
  assay_name = "logcounts", verbose = TRUE)

Arguments

object	"SingleCellExperiment" object N x G expression data matrix (cells x genes)
genesets	a "GeneSetCollection" object containing annotated gene sets
n_hidden	number of hidden factors to fit in the model (2-5 recommended)
prune_genes	logical, should genes that are not annotated to any gene sets be filtered out? If TRUE, then any genes with zero variance in expression are also filtered out.
min_genes	scalar, minimum number of genes required in order to retain a gene set for analysis
design	numeric design matrix providing values for covariates to fit in the model (rows represent cells)
anno_fpr	numeric(1), false positive rate (FPR) for assigning genes to factors (pathways); default is 0.01
anno_fnr	numeric(1), false negative rate (FNR) for assigning genes to factors (pathways); default is 0.001
assay_name	character(1), the name of the assay of the object to use as expression values. Default is logcounts, assumed to be normalised log2-counts-per-million values or equivalent.
verbose	logical(1), should information about what's going be printed to screen?

Details

This function builds and returns the object, checking for validity, which includes checking that the input data is of consistent dimensions.

Value

a new Rcpp_SlalomModel object

Examples

gmtfile <- system.file("extdata", "reactome_subset.gmt", package = "slalom")
genesets <- GSEABase::getGmt(gmtfile)
data("mesc")
model <- newSlalomModel(mesc, genesets, n_hidden = 5, min_genes = 10)

exprsfile <- system.file("extdata", "mesc.csv", package = "slalom")
mesc_mat <- as.matrix(read.csv(exprsfile))
sce <- SingleCellExperiment::SingleCellExperiment(assays = list(logcounts = mesc_mat))
# model2 <- newSlalomModel(mesc_mat, genesets, n_hidden = 5, min_genes = 10)

PMBio/Rslalom documentation built on May 28, 2019, 2:23 p.m.