A pseudo-value regression approach for differential network analysis of co-expression data
© 2023. The Author(s)..
BACKGROUND: The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a pseudo-value regression approach for network analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information criteria, followed by pseudo-value calculations, which are then entered into a robust regression model.
RESULTS: This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus database to identify DC genes that are associated with chronic obstructive pulmonary disease to demonstrate its utility.
CONCLUSION: To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
BMC bioinformatics - 24(2023), 1 vom: 09. Jan., Seite 8 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ahn, Seungjun [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Differential network analysis |
|---|
| Anmerkungen: |
Date Completed 11.01.2023 Date Revised 20.10.2023 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1186/s12859-022-05123-w |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM351354301 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM351354301 | ||
| 003 | DE-627 | ||
| 005 | 20231226051150.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/s12859-022-05123-w |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1171.xml |
| 035 | |a (DE-627)NLM351354301 | ||
| 035 | |a (NLM)36624383 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ahn, Seungjun |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A pseudo-value regression approach for differential network analysis of co-expression data |
| 264 | 1 | |c 2023 | |
| 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 11.01.2023 | ||
| 500 | |a Date Revised 20.10.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a BACKGROUND: The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a pseudo-value regression approach for network analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information criteria, followed by pseudo-value calculations, which are then entered into a robust regression model | ||
| 520 | |a RESULTS: This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus database to identify DC genes that are associated with chronic obstructive pulmonary disease to demonstrate its utility | ||
| 520 | |a CONCLUSION: To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Differential network analysis | |
| 650 | 4 | |a Gene regulatory network | |
| 650 | 4 | |a Pseudo-value | |
| 650 | 4 | |a RNA-seq data | |
| 650 | 4 | |a Regression method | |
| 700 | 1 | |a Grimes, Tyler |e verfasserin |4 aut | |
| 700 | 1 | |a Datta, Somnath |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t BMC bioinformatics |d 2000 |g 24(2023), 1 vom: 09. Jan., Seite 8 |w (DE-627)NLM109215982 |x 1471-2105 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2023 |g number:1 |g day:09 |g month:01 |g pages:8 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1186/s12859-022-05123-w |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2023 |e 1 |b 09 |c 01 |h 8 | ||