Predicting Transcription Factor Activities from Combined Analysis of Microarray and ChIP Data: a Partial Least Squares Approach

Overview

Journal Theor Biol Med Model

Publisher Biomed Central

Date 2005 Jun 28

PMID 15978125

Citations 45

Authors

Anne-Laure Boulesteix

Korbinian Strimmer

Affiliations

Soon will be listed here.

Abstract

Background: The study of the network between transcription factors and their targets is important for understanding the complex regulatory mechanisms in a cell. Unfortunately, with standard microarray experiments it is not possible to measure the transcription factor activities (TFAs) directly, as their own transcription levels are subject to post-translational modifications.

Results: Here we propose a statistical approach based on partial least squares (PLS) regression to infer the true TFAs from a combination of mRNA expression and DNA-protein binding measurements. This method is also statistically sound for small samples and allows the detection of functional interactions among the transcription factors via the notion of "meta"-transcription factors. In addition, it enables false positives to be identified in ChIP data and activation and suppression activities to be distinguished.

Conclusion: The proposed method performs very well both for simulated data and for real expression and ChIP data from yeast and E. Coli experiments. It overcomes the limitations of previously used approaches to estimating TFAs. The estimated profiles may also serve as input for further studies, such as tests of periodicity or differential regulation. An R package "plsgenomics" implementing the proposed methods is available for download from the CRAN archive.

Citing Articles

Flexible modeling of regulatory networks improves transcription factor activity estimation.

Chen C, Padi M NPJ Syst Biol Appl. 2024; 10(1):58.

PMID: 38806476 PMC: 11133322. DOI: 10.1038/s41540-024-00386-w.

Identifying genetic regulatory variants that affect transcription factor activity.

Li X, Lappalainen T, Bussemaker H Cell Genom. 2023; 3(9):100382.

PMID: 37719147 PMC: 10504674. DOI: 10.1016/j.xgen.2023.100382.

Network reconstruction for trans acting genetic loci using multi-omics data and prior information.

Hawe J, Saha A, Waldenberger M, Kunze S, Wahl S, Muller-Nurasyid M Genome Med. 2022; 14(1):125.

PMID: 36344995 PMC: 9641770. DOI: 10.1186/s13073-022-01124-9.

Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data.

Wu Y, Xue L, Huang W, Deng M, Lin Y PLoS Comput Biol. 2022; 18(1):e1009762.

PMID: 35007289 PMC: 8782462. DOI: 10.1371/journal.pcbi.1009762.

Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.

Ma C, Brent M Bioinformatics. 2020; 37(9):1234-1245.

PMID: 33135076 PMC: 8189679. DOI: 10.1093/bioinformatics/btaa947.

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