Unknown

Dataset Information

0

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles.

ABSTRACT: Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear. Here, we present an Asymmetric Winsorization per Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters both in bulk and in single-cell applications. The AWST method is available at https://github.com/drisso/awst. The code to reproduce the analyses is available at https://github.com/drisso/awst\_analysis. Supplementary data are available at Bioinformatics online.

SUBMITTER: Risso D 

PROVIDER: S-EPMC8388024 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles.

Risso Davide D   Pagnotta Stefano Maria SM  

Bioinformatics (Oxford, England) 20210801 16

<h4>Motivation</h4>Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear.<h4>Results</h4>Here, we present an Asymmetric Winsorization per-Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters bot  ...[more]

Similar Datasets

Unknown Accurate feature selection improves single-cell RNA-seq cell clustering.
| S-EPMC8644062 | biostudies-literature
Unknown Accurate inference of isoforms from multiple sample RNA-Seq data.
| S-EPMC4331715 | biostudies-literature
Unknown Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.
| S-EPMC9754601 | biostudies-literature
Unknown scHSC: enhancing single-cell RNA-seq clustering via hard sample contrastive learning.
| S-EPMC12451106 | biostudies-literature
Unknown CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.
| S-EPMC5371246 | biostudies-literature
Unknown Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts.
| S-EPMC4881296 | biostudies-literature
Unknown Robust principal component analysis for accurate outlier sample detection in RNA-Seq data.
| S-EPMC7324992 | biostudies-literature
Unknown A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.
| S-EPMC5411036 | biostudies-literature
Unknown Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis.
| S-EPMC7214470 | biostudies-literature
Unknown scEFSC: Accurate single-cell RNA-seq data analysis via ensemble consensus clustering based on multiple feature selections.
| S-EPMC9108753 | biostudies-literature