M3D_Differential_Expression: Differentially Expressed Genes.

Description Usage Arguments Details Value Examples

Description

Use Michaelis-Menten curve to find differentially expressed (DE) genes. Functions tagged with "bg__" are not meant for direct usage and are not available in the Bioconductor release.

Usage

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  M3DropFeatureSelection(expr_mat, mt_method="bon", mt_threshold=0.05, suppress.plot=FALSE, xlim=NA)
  bg__test_DE_K_equiv(gene_info, fit=NA)

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 selected features.

xlim

specify the limits of the x-axis of the plot.

fit

Output from fitting the Michaelis-Menten equation (see: bg__fit_MM

gene_info

List of calculated gene-specific values output by bg__calc_variables

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

M3DropFeatureSelection : a data.frame of significantly differentially expressed genes with columns: Gene, p.value, q.value bg__test_DE_K_equiv : a named list of containing: pval : the significance of differential expression for each gene fold_change : ratio of gene-specific K to globally fit K

Examples

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	library(M3DExampleData)
	norm <- M3DropConvertData(Mmus_example_list$data, is.counts=TRUE)
	DE_genes <- M3DropFeatureSelection(norm, 
		mt_method="fdr", mt_threshold=0.01)
#  gene_info <- bg__calc_variables(Normalized_data$data[1:1000,])
#  DE_output <- bg__test_DE_K_equiv(gene_info)

tallulandrews/M3D documentation built on May 31, 2019, 2:55 a.m.