MWT
MWT implements a moderated Welch test to detect differential expression in microarray data by accommodating unequal variances, weighting pooled and unpooled standard errors, leveraging cross-gene variance information for improved gene-level variance estimation, and providing refined FDR estimates for small-sample comparisons.
Key Features:
- Moderated Welch Test Approach: Modifies the standard Welch test to accommodate unequal variances between groups while addressing sensitivity issues in small samples relative to the standard Welch and moderated t-tests.
- Weighting of Standard Errors: Applies a weighting mechanism to both pooled and unpooled standard errors to enhance robustness of variance estimation across genes.
- Improved Gene-Level Variance Estimation: Leverages information from across all genes to produce more accurate gene-level variance estimates when variances are unequal between groups.
- False Discovery Rate (FDR) Control: Provides FDR estimates that are more reliable than those from standard t-tests, Welch tests, or moderated t-tests under unequal-variance conditions.
- Versatility Across Data Conditions: Outperforms standard t-test, Welch test, and moderated t-test when group variances are unequal and matches or exceeds moderated t-test performance when variances are equal.
- Reliability in Varied Scenarios: Improves identification of differentially expressed (DE) genes across a wide range of variance and sample-size conditions.
Scientific Applications:
- Microarray Data Analysis: Detection of differential expression in microarray experiments where group variances may differ.
- Small Sample Comparisons: Statistical testing for differential expression in studies with small sample sizes common in biological experiments and clinical studies.
Methodology:
Modifies the Welch test to accommodate unequal variances; applies weighting to pooled and unpooled standard errors using information pooled across genes; estimates gene-level variances by leveraging cross-gene information; computes FDR estimates for differential expression calls.
Topics
Details
- Tool Type:
- command-line tool
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- R
- Added:
- 12/18/2017
- Last Updated:
- 11/25/2024
Operations
Publications
Demissie M, Mascialino B, Calza S, Pawitan Y. Unequal group variances in microarray data analyses. Bioinformatics. 2008;24(9):1168-1174. doi:10.1093/bioinformatics/btn100. PMID:18344518.