| name | bioconductor-awst |
| description | We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experi |
awst
Workflows
Standard Workflow
library(awst)
library(SummarizedExperiment)
library(EDASeq)
data(airway, package = "airway")
filter <- rowMeans(assay(airway)) >= 10
se <- airway[filter,]
se <- awst(se)
filtered <- gene_filter(se)
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).
Key Parameters
- expr_values ("counts"): Character string specifying which assay of the input
SummarizedExperiment to use for the transformation (e.g., "fq" after running betweenLaneNormalization).
Best Practices
- Pre-filter non-expressed or lowly expressed genes (e.g., average count < 10) before applying AWST to reduce computational load and noise.
- Apply a prior normalization step, such as full-quantile normalization via
betweenLaneNormalization(), which allows awst() to estimate parameters only once for all samples.
- Use
gene_filter() after awst() to remove uninformative genes and improve sample separation in downstream analyses like PCA (prcomp()).
Common Pitfalls
- Pitfall: Running
awst() on log-transformed data. Fix: Ensure the input assay contains non-log-transformed counts, as awst() internally exploits the log-normal probability distribution.
- Pitfall: Retaining too many uninformative genes which dilutes the biological signal in PCA. Fix: Run
gene_filter() on the transformed object to retain only genes contributing to sample distance.
Alternatives
DESeq2 for variance stabilizing transformation (VST) or rlog.edgeR for calculating CPM/RPKM or TMM normalization.scater / scran for single-cell specific normalization and pooling.
Citations
- Risso D, Pagnotta SM (2021). "Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles." Bioinformatics. doi:10.1093/bioinformatics/btab091.
References
