awst

Workflows

Standard Workflow

library(awst)
library(SummarizedExperiment)
library(EDASeq)

# Load data (e.g., SummarizedExperiment)
data(airway, package = "airway")

# Filter lowly expressed genes
filter <- rowMeans(assay(airway)) >= 10
se <- airway[filter,]

# Apply Asymmetric Within-Sample Transformation
se <- awst(se)

# Filter uninformative genes using entropy
filtered <- gene_filter(se)

# Optional: Apply AWST after full-quantile normalization
assay(se, "fq") <- betweenLaneNormalization(assay(se), which = "full")
se_normalized <- awst(se, expr_values = "fq")

Input/Output Note: Inputs a SummarizedExperiment containing raw RNA-seq counts; outputs a SummarizedExperiment with transformed values in the "awst" assay and optionally filtered features.

When to Use

• To regularize RNA-seq read counts and reduce the effect of noise on sample clustering or classification, especially for highly degraded or low input material samples like tumors or single cells.
• To perform per-sample standardization and asymmetric winsorization using awst() to reduce the influence of highly expressed features and noise of lowly expressed features.
• To filter out uninformative genes that contribute only noise to sample distances using the entropy-based gene_filter() function.

When NOT to Use

• For differential expression analysis where raw or specifically modeled counts are required (use DESeq2 or edgeR instead).
• For multi-sample batch effect correction across highly heterogeneous batches without prior normalization (use sva::ComBat or removeBatchEffect from limma instead).

Data Requirements

• Input must be a SummarizedExperiment object (or a matrix of counts).
• Contains raw or normalized RNA-seq read counts (e.g., from airway dataset).
• Lowly expressed genes should be pre-filtered (e.g., keeping genes with rowMeans(assay(se)) >= 10).