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Methodology article
Fumihiko Takeuchi
Kokuritsu Kenkyu Kaihatsu Hojin Kokuritsu Kokusai Iryo Kenkyu Center
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3185-5661
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity.
Results: First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data.
Conclusion: Nonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas
Keywords
Epigenome-wide association study, Differential gene expression analysis, Cell type, Nonlinear regression, Ridge regression, mQTL, eQTL
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View the published article at BMC Bioinformatics.
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Editorial decision: Accept
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On 20 Dec, 2020
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On 20 Dec, 2020
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Editorial decision: Major revision
On 30 Oct, 2020
Review #1 received
Received 26 Oct, 2020
Review #2 received
Received 12 Oct, 2020
Reviewer #2 agreed
On 08 Oct, 2020
Reviewer #1 agreed
On 07 Oct, 2020
Reviewers invited
Invitations sent on 05 Oct, 2020
Editor assigned
On 01 Oct, 2020
Submission checks complete
On 30 Sep, 2020
Editor invited
On 30 Sep, 2020
No comments provided
Editorial decision: Major revision
On 21 Aug, 2020
Review #2 received
Received 20 Aug, 2020
Reviewer #2 agreed
On 19 Jul, 2020
Review #1 received
Received 05 Jul, 2020
Reviewer #1 agreed
On 15 Jun, 2020
Editor assigned
On 04 Jun, 2020
Reviewers invited
Invitations sent on 04 Jun, 2020
Submission checks complete
On 03 Jun, 2020
Editor invited
On 03 Jun, 2020
First submitted
On 25 May, 2020
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See the published version of this preprint at BMC Bioinformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Methodology article
Fumihiko Takeuchi
Kokuritsu Kenkyu Kaihatsu Hojin Kokuritsu Kokusai Iryo Kenkyu Center
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3185-5661
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
Version 3
Posted 06 Jan, 2021
No community comments so far
Editorial decision: Accept
On 24 Jan, 2021
Reviewer #1 agreed
On 20 Jan, 2021
Review #1 received
Received 20 Jan, 2021
Reviewers invited
Invitations sent on 17 Jan, 2021
Editor assigned
On 20 Dec, 2020
Submission checks complete
On 20 Dec, 2020
Editor invited
On 20 Dec, 2020
No comments provided
Editorial decision: Major revision
On 30 Oct, 2020
Review #1 received
Received 26 Oct, 2020
Review #2 received
Received 12 Oct, 2020
Reviewer #2 agreed
On 08 Oct, 2020
Reviewer #1 agreed
On 07 Oct, 2020
Reviewers invited
Invitations sent on 05 Oct, 2020
Editor assigned
On 01 Oct, 2020
Submission checks complete
On 30 Sep, 2020
Editor invited
On 30 Sep, 2020
No comments provided
Editorial decision: Major revision
On 21 Aug, 2020
Review #2 received
Received 20 Aug, 2020
Reviewer #2 agreed
On 19 Jul, 2020
Review #1 received
Received 05 Jul, 2020
Reviewer #1 agreed
On 15 Jun, 2020
Editor assigned
On 04 Jun, 2020
Reviewers invited
Invitations sent on 04 Jun, 2020
Submission checks complete
On 03 Jun, 2020
Editor invited
On 03 Jun, 2020
First submitted
On 25 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background: Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity.
Results: First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data.
Conclusion: Nonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This preprint is available for download as a PDF.
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