https://github.com/alextkalinka/delboy
Differential-representation analysis by Elastic-net Logistic regression with BinOmial-thinning validitY tests
https://github.com/alextkalinka/delboy
differential-expression r-package rna-seq
Last synced: 21 days ago
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Differential-representation analysis by Elastic-net Logistic regression with BinOmial-thinning validitY tests
- Host: GitHub
- URL: https://github.com/alextkalinka/delboy
- Owner: alextkalinka
- License: other
- Created: 2020-09-06T19:59:14.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-07-03T19:22:45.000Z (about 4 years ago)
- Last Synced: 2023-03-11T06:11:27.251Z (over 3 years ago)
- Topics: differential-expression, r-package, rna-seq
- Language: R
- Homepage:
- Size: 430 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# `delboy`
## Summary
`delboy` is an `R` package for conducting differential-expression analyses on RNA-seq data in which there are exactly **two groups** to be contrasted. The method is designed to improve sensitivity for under-powered data-sets, in which the effect sizes are small and there are few replicates, while controlling the False Discovery Rate (FDR).
`delboy` - **D**ifferential-representation analysis by **E**lastic-net **L**ogistic regression with **B**in**O**mial-thinning validit**Y** tests.
You can read about the method in the companion [manuscript](https://www.biorxiv.org/content/10.1101/2020.10.15.340737v1.full).
## Installation
```r
# install.packages("devtools")
devtools::install_github("alextkalinka/delboy")
```
## Usage
Input data should be a data frame of normalized counts in which there is a gene column with the remaining columns being sample columns.
```r
db <- delboy::run_delboy(
data = expr_data_frame,
group_1 = c("ctrl-1","ctrl-2","ctrl-3"),
group_2 = c("treat-1","treat-2","treat-3"),
filter_cutoff = 40,
gene_column = "gene_id",
batches = NULL
)
# To print a summary report to the console:
db
# To extract a data frame of hits
# (includes a 'Predicted_False_Positive' column):
my_hits <- delboy::hits(db)
```
To plot validation performance next to original data showing the false-positive decision boundary (axes limits can be controlled using the `xlim` and `ylim` arguments):
```r
plot(db, type = "lfc_expr")
```
To visualize false negatives in the validation data relative to the false-positive decision boundary:
```r
plot(db, type = "lfc_expr_FN")
```
To plot the distrubution of log-fold changes used for the validation data:
```r
plot(db, type = "lfc_nonnull")
```
## References
Kalinka, A. T. (2020). Improving the sensitivity of differential-expression analyses for under-powered RNA-seq experiments. bioRxiv [10.1101/2020.10.15.340737](https://www.biorxiv.org/content/10.1101/2020.10.15.340737v1.full).
## Bugs, Issues, or Requests
Please contact [Alex Kalinka](mailto:alex.t.kalinka@gmail.com).