estimate.proportion: SSMD: A semi-supervised approach for a robust identification...

In zy26/SSMD:

Description

This package implements methods for estimate cell type proportion in bulk tissue gene expression data.

Usage

estimate.proportion <- function(data, lambda = 0.75)

Arguments

data	input gene expression matrix. MGI gene symbol should be as their row names
lambda	threshold of mean correlation to define rank-1 co-expression module

Value

An object of class is also invisibly returned. This is a list containing the following components:

Stat_all	statistics for all rank-1 co-expression module. CT: cell type; mean: mean correlation inside the module; Core_overlap_number: Overlap number with core marker list; Core_overlap_rate: overlap rate with core marker list; BCV_rank: bcv rank of the first base
module_keep	modules with the high overlap number with core marker list for each cell type
proportion	estimated proportion for each cell type

Examples

 # Simulate gene expression data for 1000 probes and 6 microarrays.
 # Samples are in two groups
 # First 50 probes are differentially expressed in second group
 # candidate marker list and core markers we found in traing step
 load('IM_markers_20190302_mouse.RData')
 load('Mouse_selected_core_markers.RData')
 # load your own gene expression data
 load('example_bulk.RData')
 aaa=estimate.proportion(data,lambda = 0.8)

zy26/SSMD documentation built on Dec. 31, 2019, 3:58 a.m.