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RNA-seq

Source: vignettes/omics-rnaseq.Rmd
omics-rnaseq.Rmd

This vignette demonstrates a compact RNA-seq preprocessing workflow using Rtoolset functions for filtering, normalization, and feature selection.

Overview

Rtoolset provides functions for:

  • filtering low-expression genes
  • calculating counts per million (CPM)
  • log transformation with pseudo counts
  • identifying the most variable genes

These functions integrate naturally with the edgeR package for downstream differential expression analysis.

Complete Workflow

Step 1: Load and Prepare Data 

library(Rtoolset)
library(edgeR)

# Count matrix: genes (rows) x samples (columns)
count_df <- read.csv("count_matrix.csv", row.names = 1)

# Group labels (optional)
group <- c(rep("Control", 3), rep("Treatment", 3))

Step 2: Filter and Normalize

The filter_calcpm_dge() function performs multiple preprocessing steps in one call:

  1. Creates a DGEList object
  2. Calculates CPM
  3. Filters low-expression genes
  4. Normalizes library sizes
  5. Computes filtered CPM and log-transformed CPM
result <- filter_calcpm_dge(
  count_df = count_df,
  group = group,
  min_count = 10,
  min_prop = 0.1
)

Step 3: Access Results 

# Original DGEList
result$dge_count

# CPM for all genes
result$dge_cpm

# Filtered DGEList
result$dge_keep

# CPM for retained genes
result$dge_keep_cpm

# Log2(CPM + 1) matrix for retained genes
result$dge_keep_cpm_log

Step 4: Continue with Differential Expression 

dge <- estimateDisp(result$dge_keep)
fit <- glmQLFit(dge)
qlf <- glmQLFTest(fit, contrast = c(-1, 1))
top_genes <- topTags(qlf, n = 100)

Individual Functions

Log Transformation

Use log_transform() when you want to control the pseudo count or log base:

log_data <- log_transform(
  df_mat = count_df,
  count = 1,
  base = 2
)

Most Variable Genes

Use get_top_var_mat() to focus on the most variable features for PCA, clustering, or exploratory analysis:

var_result <- get_top_var_mat(
  count_df = result$dge_keep_cpm_log,
  prop = 0.2
)

var_result$var_genes
var_result$topN_mat

Filtering Parameters

min_count

The minimum count threshold for a gene to be kept:

result_strict <- filter_calcpm_dge(
  count_df = count_df,
  min_count = 50,
  min_prop = 0.1
)

result_lenient <- filter_calcpm_dge(
  count_df = count_df,
  min_count = 5,
  min_prop = 0.1
)

min_prop

The minimum proportion of samples in the smallest group that must pass the expression threshold:

result <- filter_calcpm_dge(
  count_df = count_df,
  group = group,
  min_count = 10,
  min_prop = 0.5
)

Suggested Use Cases

Standard Differential Expression Analysis 

library(Rtoolset)
library(edgeR)

count_df <- read.csv("counts.csv", row.names = 1)
group <- c(rep("Control", 4), rep("Treatment", 4))

result <- filter_calcpm_dge(
  count_df = count_df,
  group = group,
  min_count = 10,
  min_prop = 0.1
)

dge <- estimateDisp(result$dge_keep)
fit <- glmQLFit(dge)
qlf <- glmQLFTest(fit, contrast = c(-1, 1))
top_genes <- topTags(qlf, n = 100)

Exploratory Data Analysis 

var_result <- get_top_var_mat(
  count_df = result$dge_keep_cpm_log,
  prop = 0.2
)

pca_data <- var_result$topN_mat

See Also