aldex_bioc
ALDEx_bioc is the working directory for updating bioconductor
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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✓Committers with academic emails
3 of 17 committers (17.6%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.4%) to scientific vocabulary
Keywords from Contributors
bioconductor-package
gene
bioinformatics
genomics
derfinder
metabolomics
rnaseq
mass-spectrometry
annotation-agnostic
cleavage
Last synced: 11 months ago
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JSON representation
Repository
ALDEx_bioc is the working directory for updating bioconductor
Statistics
- Stars: 28
- Watchers: 6
- Forks: 16
- Open Issues: 18
- Releases: 0
Created almost 9 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
README.Rmd
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
## 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 gh-installation, eval = FALSE}
# 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!).
```{r example}
library(ALDEx2)
data(selex)
group <- c(rep("A", 7), rep("B", 7))
res <- aldex(selex, group, denom = "iqlr")
```
See the vignette for more details.
Owner
- Name: Greg Gloor
- Login: ggloor
- Kind: user
- Location: London, ON, CANADA
- Company: University of Western Ontario
- Website: http://ggloor.github.io
- Twitter: gbgloor
- Repositories: 28
- Profile: https://github.com/ggloor
GitHub Events
Total
- Issues event: 2
- Watch event: 1
- Issue comment event: 6
- Push event: 6
- Pull request event: 4
- Fork event: 3
Last Year
- Issues event: 2
- Watch event: 1
- Issue comment event: 6
- Push event: 6
- Pull request event: 4
- Fork event: 3
Committers
Last synced: almost 3 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| ggloor | g****r@u****a | 129 |
| michellepistner | p****r@p****u | 66 |
| Thom Quinn | c****n@g****m | 23 |
| Nitesh Turaga | n****a@g****m | 14 |
| Brandon Lieng | b****g@g****m | 11 |
| Dan Tenenbaum | d****a@f****g | 8 |
| Herve Pages | h****s@f****g | 5 |
| Jean M Macklaim | j****m@g****m | 3 |
| J Wokaty | j****y@s****u | 2 |
| Marc Carlson | m****n@f****g | 2 |
| vobencha | v****a@g****m | 2 |
| vobencha | v****n@r****g | 2 |
| Hervé Pagès | h****s@f****g | 2 |
| J Wokaty | j****y | 2 |
| brandonlieng | b****g@u****a | 1 |
| Vladimir Mikryukov | v****k | 1 |
| p | p****n@n****v | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 53
- Total pull requests: 29
- Average time to close issues: 5 months
- Average time to close pull requests: 24 days
- Total issue authors: 42
- Total pull request authors: 9
- Average comments per issue: 2.32
- Average comments per pull request: 0.45
- Merged pull requests: 26
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 4
- Pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 3 months
- Issue authors: 4
- Pull request authors: 2
- Average comments per issue: 1.25
- Average comments per pull request: 0.67
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- aimirza (5)
- jmmack (3)
- sformel (2)
- valscherz (2)
- elayton13 (2)
- tpq (2)
- sbslee (2)
- johnne (1)
- MonicaSteffi (1)
- K-Pimentel (1)
- JEckenberger (1)
- davis-rudolfs (1)
- mikemc (1)
- mhibberd (1)
- schyells (1)
Pull Request Authors
- ggloor (16)
- jmmack (3)
- tpq (3)
- kyle-mcgovern (2)
- boopalaarul (2)
- vmikk (1)
- michellepistner (1)
- brandonlieng (1)
- pooranis (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
DESCRIPTION
cran
- methods * depends
- stats * depends
- zCompositions * depends
- BiocParallel * imports
- GenomicRanges * imports
- IRanges * imports
- Rfast * imports
- S4Vectors * imports
- SummarizedExperiment * imports
- multtest * imports
- BiocStyle * suggests
- knitr * suggests
- rmarkdown * suggests
- testthat * suggests