The multivariate nonparametric methods for identifying gene sets with differential expression
Related Experiment Video
Updated: Apr 24, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
Soheila Khodakarim1, Seyyed Mohammad Tabatabaei2, Hamid AlaviMajd2
1Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background:
Gene Set Analysis (GSA) identifies differential expression gene sets amid the different phenotypes. The results of published papers in this filed are inconsistent and there is no consensus on the best method. In this paper two new methods, in comparison to the previous ones, are introduced for GSA.
Methods:
The MMGSA and MRGSA methods based on multivariate nonparametric techniques were presented. The implementation of five GSA methods (Hotelling's T(2), Globaltest, Abs_Cat, Med_Cat and Rs_Cat) and the novel methods to detect differential gene expression between phenotypes were compared using simulated and real microarray data sets.
Results:
In a real dataset, the results showed that the powers of MMGSA and MRGSA were as well as Globaltest and Tsai. The MRGSA method has not a good performance in the simulation dataset.
Conclusions:
The Globaltest method is the best method in the real or simulation datasets. The performance of MMGSA in simulation dataset is good in small-size gene sets. The GLS methods are not good in the simulated data, except the Med_Cat method in large-size gene sets.
Related Concept Videos
Statistical Methods to Analyze Parametric Data: ANOVA
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
DNA Microarrays
Comparing the Survival Analysis of Two or More Groups
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...

