Name
Title | ||
|---|---|---|
A survey and comparative study of statistical tests for identifying differential expression from microarray data |
Abstract | ||
|---|---|---|
DNA microarray is a powerful technology that can simultaneously determine the levels of thousands of transcripts (generated, for example, from genes/miRNAs) across different experimental conditions or tissue samples. The motto of differential expression analysis is to identify the transcripts whose expressions change significantly across different types of samples or experimental conditions. A number of statistical testing methods are available for this purpose. In this paper, we provide a comprehensive survey on different parametric and non-parametric testing methodologies for identifying differential expression from microarray data sets. The performances of the different testing methods have been compared based on some real-life miRNA and mRNA expression data sets. For validating the resulting differentially expressed miRNAs, the outcomes of each test are checked with the information available for miRNA in the standard miRNA database PhenomiR 2.0. Subsequently, we have prepared different simulated data sets of different sample sizes (from 10 to 100 per group/population) and thereafter the power of each test have been calculated individually. The comparative simulated study might lead to formulate robust and comprehensive judgements about the performance of each test in the basis of assumption of data distribution. Finally, a list of advantages and limitations of the different statistical tests has been provided, along with indications of some areas where further studies are required. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCBB.2013.147 | IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM |
Keywords | Field | DocType |
biology and genetics,design,differentially expressed transcripts,multiple testing corrections,parametric and nonparametric tests,power of test,statistical computing | Population,Data mining,Data set,Computer science,Microarray analysis techniques,Parametric statistics,Bioinformatics,Sample size determination,Gene expression profiling,Statistical hypothesis testing,DNA microarray | Journal |
Volume | Issue | ISSN |
11 | 1 | 1557-9964 |
Citations | PageRank | References |
14 | 0.53 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanghamitra Bandyopadhyay | 1 | 3977 | 222.92 |
| Saurav Mallik | 2 | 31 | 5.55 |
| Anirban Mukhopadhyay | 3 | 711 | 50.07 |