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:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
if (!requireNamespace("DESeq2", quietly = TRUE)) {
  BiocManager::install("DESeq2")
}
if (!requireNamespace("BiocParallel", quietly = TRUE)) {
  BiocManager::install("BiocParallel")
}
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}
devtools::install_github("Fanding-Zhou/QRscore")

Loading QRscore

After installation, load QRscore and its dependencies:

library(QRscore)
library(DESeq2)
library(BiocParallel)

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.

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.

kept_samples = coldata$age %in% c("40-49", "60-69")
normalized_mat = normalized_mat[, kept_samples]
coldata = coldata[kept_samples,]
results <- QRscore.genetest(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.