The optimal discovery procedure for significance analysis of general gene expression studies
© The Author(s) 2020. Published by Oxford University Press..
MOTIVATION: Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the optimal discovery procedure (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here, we extend this work to the analysis of complex study designs and RNA-sequencing studies.
RESULTS: We apply our extended framework to a static RNA-sequencing study, a longitudinal study, an independent sampling time-series study,and an independent sampling dose-response study. Our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus, the extended ODP enables a favorable significance analysis of genome-wide gene expression studies.
AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in our freely available R package called edge and can be downloaded at https://www.bioconductor.org/packages/release/bioc/html/edge.html.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
|---|---|
| Enthalten in: |
Bioinformatics (Oxford, England) - 37(2021), 3 vom: 20. Apr., Seite 367-374 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Bass, Andrew J [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 28.04.2021 Date Revised 17.05.2023 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1093/bioinformatics/btaa707 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM313920613 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM313920613 | ||
| 003 | DE-627 | ||
| 005 | 20231225151620.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/bioinformatics/btaa707 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1046.xml |
| 035 | |a (DE-627)NLM313920613 | ||
| 035 | |a (NLM)32818252 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Bass, Andrew J |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The optimal discovery procedure for significance analysis of general gene expression studies |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 28.04.2021 | ||
| 500 | |a Date Revised 17.05.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2020. Published by Oxford University Press. | ||
| 520 | |a MOTIVATION: Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the optimal discovery procedure (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here, we extend this work to the analysis of complex study designs and RNA-sequencing studies | ||
| 520 | |a RESULTS: We apply our extended framework to a static RNA-sequencing study, a longitudinal study, an independent sampling time-series study,and an independent sampling dose-response study. Our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus, the extended ODP enables a favorable significance analysis of genome-wide gene expression studies | ||
| 520 | |a AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in our freely available R package called edge and can be downloaded at https://www.bioconductor.org/packages/release/bioc/html/edge.html | ||
| 520 | |a SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 700 | 1 | |a Storey, John D |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Bioinformatics (Oxford, England) |d 1998 |g 37(2021), 3 vom: 20. Apr., Seite 367-374 |w (DE-627)NLM094620342 |x 1367-4811 |7 nnns |
| 773 | 1 | 8 | |g volume:37 |g year:2021 |g number:3 |g day:20 |g month:04 |g pages:367-374 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/bioinformatics/btaa707 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 37 |j 2021 |e 3 |b 20 |c 04 |h 367-374 | ||