runSTG: Run STG

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

Description

Run STG to select a set of genes that separate cells with label.1 from label.2 (other labels)

Usage

runSTG(X, X.labels, label.1, label.2 = NULL, lambda = 1.5,
  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
X.labels	numeric vector, specify labels for each cell, must be 0 or 1
label.1	cell label to define markers for
label.2	second cell label to for comparison, if NULL, use all other labels
lambda	numeric, regularization parameter that weights the number of selected genes, a larger lambda leads to fewer genes, default 1.5
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:

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.