dorsal/arxiv
View SchemaA statistical framework for the design of microarray experiments and effective detection of differential gene expression
| Authors | Shu-Dong Zhang, Timothy W. Gant |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0405015 |
| URL | https://arxiv.org/abs/q-bio/0405015 |
| DOI | 10.1093/bioinformatics/bth336 |
| Journal | Bioinformatics, vol 20, no 16, pp 2821-2828, November 2004 |
Abstract
Four reasons why you might wish to read this paper: 1. We have devised a new statistical T test to determine differentially expressed genes (DEG) in the context of microarray experiments. This statistical test adds a new member to the traditional T-test family. 2. An exact formula for calculating the detection power of this T test is presented, which can also be fairly easily modified to cover the traditional T tests. 3. We have presented an accurate yet computationally very simple method to estimate the fraction of non-DEGs in a set of genes being tested. This method is superior to an existing one which is computationally much involved. 4. We approach the multiple testing problem from a fresh angle, and discuss its relation to the classical Bonferroni procedure and to the FDR (false discovery rate) approach. This is most useful in the analysis of microarray data, where typically several thousands of genes are being tested simultaneously.
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"abstract": "Four reasons why you might wish to read this paper: 1. We have devised a new\nstatistical T test to determine differentially expressed genes (DEG) in the\ncontext of microarray experiments. This statistical test adds a new member to\nthe traditional T-test family. 2. An exact formula for calculating the\ndetection power of this T test is presented, which can also be fairly easily\nmodified to cover the traditional T tests. 3. We have presented an accurate yet\ncomputationally very simple method to estimate the fraction of non-DEGs in a\nset of genes being tested. This method is superior to an existing one which is\ncomputationally much involved. 4. We approach the multiple testing problem from\na fresh angle, and discuss its relation to the classical Bonferroni procedure\nand to the FDR (false discovery rate) approach. This is most useful in the\nanalysis of microarray data, where typically several thousands of genes are\nbeing tested simultaneously.",
"arxiv_id": "q-bio/0405015",
"authors": [
"Shu-Dong Zhang",
"Timothy W. Gant"
],
"categories": [
"q-bio.QM",
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],
"doi": "10.1093/bioinformatics/bth336",
"journal_ref": "Bioinformatics, vol 20, no 16, pp 2821-2828, November 2004",
"title": "A statistical framework for the design of microarray experiments and effective detection of differential gene expression",
"url": "https://arxiv.org/abs/q-bio/0405015"
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