M3DropDifferentialExpression {M3Drop}R Documentation

Differentially Expressed Genes.

Description

Use Michaelis-Menten curve to find differentially expressed (DE) genes.

Usage

  M3DropDifferentialExpression(expr_mat, mt_method="bon", mt_threshold=0.05, suppress.plot=FALSE)

Arguments

expr_mat

a numeric matrix of normalized (not log-transformed) expression values, columns = samples, rows = genes.

mt_method

the multiple testing method used in p.adjust

mt_threshold

the threshold for identifying significantly DE genes.

suppress.plot

logical, whether to plot the fitted curve and highlight DE genes.

Details

Fits a Michaelis-Menten function to the dropout-rate (if not provided) of the provided expression matrix. Identifies genes where the gene-specific K calculated as (S = mean expression, P = dropout rate):

K = (S * P)/(1 - P)

is significantly larger than the K fitted to the entire dataset. Combines standard errors of the fitted K, the gene-specific dropout rate and the gene-specific average expression using error propagation rules. Determines the signficance of the gene-specific K using a Z-test of the log-transformed Ks with the propagated error then applies the specified multiple testing correction to identify DE genes. Plots the dropout rate vs gene expression with the fitted MM curve and highlights in purple the significantly DE genes.

Value

M3Drop_Differential Expression : a data.frame of significantly differentially expressed genes with columns: Gene, p.value, q.value

Examples

  library(M3DExampleData)
  Normalized_data <- M3DropCleanData(Mmus_example_list$data,
                  labels = Mmus_example_list$labels,
                  is.counts=TRUE, min_detected_genes=2000)
  DE_genes <- M3DropDifferentialExpression(Normalized_data$data, 
		mt_method="fdr", mt_threshold=0.01)

[Package M3Drop version 1.6.0 Index]