# QRscore
QRscore is an R package designed for analysis of differentially expressed genes
(DEGs) and differentially dispersed genes (DDGs). The workflow is shown
in the following figure.
## Installation
QRscore requires the following packages to run effectively:
```r
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
if (!requireNamespace("DESeq2", quietly = TRUE)) {
BiocManager::install("DESeq2")
}
```
You can install the **QRscore** package from
[Bioconductor](https://bioconductor.org) with the following command:
```r
if (!requireNamespace("QRscore", quietly = TRUE)) {
BiocManager::install("QRscore")
}
```
Alternatively, you can install the **QRscore** package from github:
```r
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("Fanding-Zhou/QRscore")
```
## Loading QRscore
After installation, load QRscore and its dependencies:
```r
library(QRscore)
library(DESeq2)
```
## Usage
Here’s a brief example of how to use QRscore in your analysis pipeline:
1. **Preparing Data**: QRscore takes in RNA-seq count matrix and group labels
as input and perform prefiltering and normalization.
```r
data("example_dataset_raw_3000_genes")
bulk_sparse_mat = example_dataset_raw_3000_genes$COUNTS
ages = example_dataset_raw_3000_genes$METADATA$AGE
## filter out low expressed genes
col_means <- colMeans(bulk_sparse_mat, na.rm = TRUE)
col_zeros <- colMeans(bulk_sparse_mat==0, na.rm = TRUE)
col_ids <- which(col_means>5&col_zeros<0.2) # The threshold can be modified
## normalization
bulk_df <- bulk_sparse_mat[,col_ids]
bulk_df_inv <- t(bulk_df)
coldata <- data.frame(age = ages)
dds <- DESeqDataSetFromMatrix(countData = bulk_df_inv,
colData = coldata,
design = ~ age)
dds <- estimateSizeFactors(dds)
normalized_mat <- counts(dds, normalized=TRUE)
```
2. **Running Analysis**: Example code for setting up and performing the QRscore
analysis.
```r
kept_samples <- coldata$age %in% c("40-49", "60-69")
normalized_mat <- normalized_mat[, kept_samples]
coldata <- coldata[kept_samples,]
results <- QRscoreGenetest(normalized_mat_1, coldata_1, pairwise_test = TRUE,
pairwise_logFC = TRUE, test_mean = TRUE, test_dispersion = TRUE, num_cores = 4,
approx = "asymptotic")
```
3. **Interpreting Results**: QRscore outputs include differential expression
and differential dispersion p-values, together with log Fold Change and
log Variance Change.
## License
GPL (>= 3)
## References
For more information and detailed usage, refer to the QRscore vignette,
documentation and package help files.
