Robust gene co-expression networks via partial robust M regression

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Makale detayları

Robust gene co-expression networks via partial robust M regression

Yazarlar:

Ayça Ölmez | Aylin Alın

Yayıncı:

Hacettepe University

Ana Başlıklar:

Bioinformatics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1518-1532

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356980

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 Gene expression data provide valuable information on the regulation and interactions of thousands of genes. However, constructing robust gene co-expression networks in the presence of outliers remains an open challenge. We propose a partial robust M regression based-method for building ene co-expression networks, which downweights extreme observations instead of discarding them. This preserves critical biological information while safeguarding the overall network structure from distortion. Through comprehensive simulations on the syntren300 dataset - including various outlier distributions e.g. N0, 5, N1, 5, N100, 10 and t2 and contamination levels up to 30, the partial robust M regression-based approach outperforms widely used methods weighted gene co-expression network analysis, bi-weighted midcorrelation and partial least squares regression-based connectivity in terms of precision, F1 and Matthews correlation coefficient. Real-data analysis of mouse liver gene expression further validates the stability and biological relevance of partial robust M regression-based gene co-expression networks, as it accurately identifies functionally enriched genes even under data contamination. These findings underscore the potential of partial robust M regression-based network construction to enhance reliability and uncover novel insights in high-dimensional genomic studies, offering a robust alternative to traditional correlation-based or partial least squares regression-based methods.

 Gene expression data provide valuable information on the regulation and interactions of thousands of genes. However, constructing robust gene co-expression networks in the presence of outliers remains an open challenge. We propose a partial robust M regression based-method for building ene co-expression networks, which downweights extreme observations instead of discarding them. This preserves critical biological information while safeguarding the overall network structure from distortion. Through comprehensive simulations on the syntren300 dataset - including various outlier distributions e.g. N0, 5, N1, 5, N100, 10 and t2 and contamination levels up to 30, the partial robust M regression-based approach outperforms widely used methods weighted gene co-expression network analysis, bi-weighted midcorrelation and partial least squares regression-based connectivity in terms of precision, F1 and Matthews correlation coefficient. Real-data analysis of mouse liver gene expression further validates the stability and biological relevance of partial robust M regression-based gene co-expression networks, as it accurately identifies functionally enriched genes even under data contamination. These findings underscore the potential of partial robust M regression-based network construction to enhance reliability and uncover novel insights in high-dimensional genomic studies, offering a robust alternative to traditional correlation-based or partial least squares regression-based methods.

 Gene expression data provide valuable information on the regulation and interactions of thousands of genes. However, constructing robust gene co-expression networks in the presence of outliers remains an open challenge. We propose a partial robust M regression based-method for building ene co-expression networks, which downweights extreme observations instead of discarding them. This preserves critical biological information while safeguarding the overall network structure from distortion. Through comprehensive simulations on the syntren300 dataset - including various outlier distributions e.g. N0, 5, N1, 5, N100, 10 and t2 and contamination levels up to 30, the partial robust M regression-based approach outperforms widely used methods weighted gene co-expression network analysis, bi-weighted midcorrelation and partial least squares regression-based connectivity in terms of precision, F1 and Matthews correlation coefficient. Real-data analysis of mouse liver gene expression further validates the stability and biological relevance of partial robust M regression-based gene co-expression networks, as it accurately identifies functionally enriched genes even under data contamination. These findings underscore the potential of partial robust M regression-based network construction to enhance reliability and uncover novel insights in high-dimensional genomic studies, offering a robust alternative to traditional correlation-based or partial least squares regression-based methods.

 Gene expression data provide valuable information on the regulation and interactions of thousands of genes. However, constructing robust gene co-expression networks in the presence of outliers remains an open challenge. We propose a partial robust M regression based-method for building ene co-expression networks, which downweights extreme observations instead of discarding them. This preserves critical biological information while safeguarding the overall network structure from distortion. Through comprehensive simulations on the syntren300 dataset - including various outlier distributions e.g. N0, 5, N1, 5, N100, 10 and t2 and contamination levels up to 30, the partial robust M regression-based approach outperforms widely used methods weighted gene co-expression network analysis, bi-weighted midcorrelation and partial least squares regression-based connectivity in terms of precision, F1 and Matthews correlation coefficient. Real-data analysis of mouse liver gene expression further validates the stability and biological relevance of partial robust M regression-based gene co-expression networks, as it accurately identifies functionally enriched genes even under data contamination. These findings underscore the potential of partial robust M regression-based network construction to enhance reliability and uncover novel insights in high-dimensional genomic studies, offering a robust alternative to traditional correlation-based or partial least squares regression-based methods.

Makale detayları

Robust gene co-expression networks via partial robust M regression

Yazarlar:

Ayça Ölmez | Aylin Alın

Yayıncı:

Hacettepe University

Ana Başlıklar:

Bioinformatics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1518-1532

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356980

pISSN:

2651-477X

Bi-weight mid-correlation

gene co-expression network analysis

outliers

partial robust m regression

robust

weighted gene co-expression network analysis

Sayı

Makale detayları

Robust gene co-expression networks via partial robust M regression

Yazarlar: Ayça Ölmez | Aylin Alın

Yayıncı: Hacettepe University

Ana Başlıklar: Bioinformatics

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1518-1532

Belge Tipi: Makale

Yayın Tarihi: 8/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001356980

pISSN: 2651-477X

Bi-weight mid-correlation

gene co-expression network analysis

outliers

partial robust m regression

robust

weighted gene co-expression network analysis

Sayı

Yazarlar:

Ayça Ölmez | Aylin Alın

Yayıncı:

Hacettepe University

Ana Başlıklar:

Bioinformatics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 4, pp.1518-1532

Belge Tipi:

Makale

Yayın Tarihi:

8/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356980

pISSN:

2651-477X