STGmarkerFinder: DA-seq Step 4: STG feature selection

In JunZhao1990/DAseq: Detecting regions of differential abundance between scRNA-seq datasets

Description

Use STG (stochastic gates) to select genes that separate each DA region from the rest of the cells. For a full description of the algorithm, see Y. Yamada, O. Lindenbaum, S. Negahban, and Y. Kluger. Feature selection using stochastic gates. arXiv preprint arXiv:1810.04247, 2018.

Usage

STGmarkerFinder(X, cell.idx, da.region.label, da.regions.to.run = NULL,
  lambda = 1.2, n.runs = 5, return.model = F,
  python.use = "/usr/bin/python", GPU = "")

Arguments

X	matrix, normalized expression matrix of all cells in the dataset, genes are in rows, rownames must be gene names
cell.idx	result "da.cell.idx" from the output of function getDAcells
da.region.label	result "cluster.res" from the output of function getDAregion
da.regions.to.run	numeric (vector), which DA regions to run the marker finder, default is to run all regions
lambda	numeric, regularization parameter that weights the number of selected genes, a larger lambda leads to fewer genes, default 1.2
n.runs	integer, number of runs to run the model, default 5
return.model	a logical value to indicate whether to return the actual model of STG
python.use	character string, the Python to use, default "/usr/bin/python"
GPU	which GPU to use, default ”, using CPU

Value

a list of results:

da.markers

a list of data.frame with markers for each DA region

accuracy

a numeric vector showing mean accuracy for each DA region

model

a list of model for each DA region, each model contains:

model

the model of STG of the final run

features

features used to train the model

selected.features

the selected features of the final run

pred

the linear prediction value for each cell from the model

JunZhao1990/DAseq documentation built on April 30, 2020, 10:10 a.m.