In ggloor/ALDEx_bioc: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account

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Introduction

Welcome to a scale simulation within ALDEx2!

The ALDEx2 package is a Bioconductor package for differential abundance analysis across two or more conditions. It is useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Unlike other packages, ALDEx2 uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcox rank test or Welch t-test (via aldex.ttest), or a glm and Kruskal-Wallis test (via aldex.glm). The ALDEx2 package reports p-values and Benjamini-Hochberg corrected p-values. Effect sizes > 1 are generally preferred metrics. This repository also allows for scale simulation to be incorporated within ALDEx2.

Quick start

You can install the developmental branch of ALDEx2 plus scale simulation from GitHub:

# install.packages("devtools")
devtools::install_github("michellepistner/ALDEx_bioc")

Getting started with ALDEx2 is easy. All you need is a matrix (with rows as variables and columns as samples) and a character vector of group labels. Finally, use the denom argument to choose a set of variables to use as the reference for the analysis. You can provide a user-defined reference set (e.g., known house-keeping genes), or choose a method that finds references from the data (denom = "iqlr" usually performs well!).

library(ALDEx2)
data(selex)
group <- c(rep("A", 7), rep("B", 7))
res <- aldex(selex, group, denom = "iqlr")

See the vignette for more details.

ggloor/ALDEx_bioc documentation built on Oct. 31, 2023, 1:13 a.m.