findCells: Find cell type proportions from bulk gene expression data...

Description Usage Arguments Value References Examples

View source: R/findCells.R

Description

Input a gene expression matrix and your own data frame of marker genes, and this function will estimate cell type proportions in your data set using one of the SVD or PCA dimension reduction approaches.

Usage

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findCells(inputMat, markers, nMarker = 50, method = "SVD",
  scale = TRUE)

Arguments

inputMat

Numeric gene expression data frame or matrix, with rownames corresponding to gene names, some of which are marker genes, and columns corresponding to samples.

markers

Data frame with marker genes in one column (named "marker") and the cell type that that gene symbol corresponds to in another column (named "cell").

nMarker

The number of marker genes (that are present in your expression data set) to use in estimating the surrogate cell type proportion variable for each cell type.

method

To estimate the cell type proportions, can either use PCA or SVD.

scale

Whether or not to scale the gene expression data from each marker gene prior to using it as an input for dimension reduction.

Value

A sample-by-cell type matrix of estimate cell type proportion variables.

References

Chikina M, Zaslavsky E, Sealfon SC. CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations. Bioinformatics. 2015;31(10):1584-91.

Examples

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cell_type_proportions = findCells(aba_marker_expression,
 markers = markers_df_brain, nMarker = 10)
str(cell_type_proportions)

BRETIGEA documentation built on May 2, 2019, 2:43 a.m.