Name Mode Size
R 040000
data 040000
inst 040000
man 040000
tests 040000
vignettes 040000
.DS_Store 100644 14 kb
.Rbuildignore 100644 0 kb
.gitignore 100644 0 kb
ALDEx2.Rproj 100644 0 kb
DESCRIPTION 100644 2 kb
NAMESPACE 100644 1 kb
NEWS 100644 4 kb
README.Rmd 100644 2 kb 100644 3 kb
<!-- is generated from README.Rmd. Please edit that file --> <!-- badges: start --> [![R-CMD-check](]( <!-- badges: end --> ## 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: ``` r # install.packages("devtools") devtools::install_github("ggloor/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!). ``` r library(ALDEx2) #> Loading required package: zCompositions #> Loading required package: MASS #> Loading required package: NADA #> Loading required package: survival #> #> Attaching package: 'NADA' #> The following object is masked from 'package:stats': #> #> cor #> Loading required package: truncnorm #> Loading required package: lattice #> Loading required package: latticeExtra data(selex) group <- c(rep("A", 7), rep("B", 7)) res <- aldex(selex, group, denom = "iqlr") #> aldex.clr: generating Monte-Carlo instances and clr values #> operating in serial mode #> computing iqlr centering #> aldex.ttest: doing t-test #> aldex.effect: calculating effect sizes ``` See the scaleSim or ALDEx2 vignettes for more details.