Independent component analysis of microarray data in the study of endometrial cancer

Saidi, Samir; Holland, Cathrine; Kreil, David P.; Mackay, David; Charnock-Jones, D. Stephen; Print, Cristin G.; Smith, Stephen K.

doi:10.1038/sj.onc.1207562

Cited by 90 publications

(65 citation statements)

References 27 publications

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“…Similar conclusions were made in [16] for a dataset of endometrial cancers. In [17], the first meta-study comparing ICA with other methods in several large-scale gene expression datasets was performed, including normal human tissues dataset.…”

Section: Ica and Nmf Outperforms Other Statistical Methods With Respesupporting

confidence: 66%

Blind source separation methods for deconvolution of complex signals in cancer biology

Zinovyev

Kairov

Karpenyuk

et al. 2013

Biochemical and Biophysical Research Communications

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Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.

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Section: Ica and Nmf Outperforms Other Statistical Methods With Respesupporting

confidence: 66%

Blind source separation methods for deconvolution of complex signals in cancer biology

Zinovyev

Kairov

Karpenyuk

et al. 2013

Biochemical and Biophysical Research Communications

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“…A number of studies have utilized microarray-based platforms to identify genes that are aberrantly expressed in endometrial tumors as compared to normal endometrium (Mutter et al, 2001;Moreno-Bueno et al, 2003;Risinger et al, 2003;Cao et al, 2004;Saidi et al, 2004;Smid-Koopman et al, 2004;Ferguson et al, 2005;Maxwell et al, 2005). These analyses have provided useful data, but all have been limited by a number of factors including heterogeneous tumor populations, an insufficient number of tumor and/or control specimens and microarray formats with relatively few features.…”

Section: Discussionmentioning

confidence: 99%

Identification of molecular markers and signaling pathway in endometrial cancer in Hong Kong Chinese women by genome-wide gene expression profiling

et al. 2006

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“…Consequently, recent publications have applied complementary computational strategies in the analysis of large datasets, such as coexpression analysis [32] or partial least squares analysis [33]. Other, more advanced computational approaches, that have not yet been applied to CHO, might become feasible once either larger data sets become available, such as independent component analysis [34,35], or methods that exploit a supervised setting, such as deep learning algorithms [36].…”

Section: Introductionmentioning

confidence: 99%

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in‐creased production capacity

Harreither

Hackl

Pichler

et al. 2015

Biotechnology Journal

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Over the last three decades, product yields from CHO cells have increased dramatically, yet specific productivity (qP) remains a limiting factor. In a previous study, using repeated cell-sorting, we have established different host cell subclones that show superior transient qP over their respective parental cell lines (CHO-K1, CHO-S). The transcriptome of the resulting six cell lines in different biological states (untransfected, mock transfected, plasmid transfected) was first explored by hierarchical clustering and indicated that gene activity associated with increased qP did not stem from a certain cellular state but seemed to be inherent for a high qP host line. We then performed a novel gene regression analysis identifying drivers for an increase in qP. Genes significantly implicated were first systematically tested for enrichment of GO terms using a Bayesian approach incorporating the hierarchical structure of the GO term tree. Results indicated that specific cellular components such as nucleus, ER, and Golgi are relevant for cellular productivity. This was complemented by targeted GSA that tested functionally homogeneous, manually curated subsets of KEGG pathways known to be involved in transcription, translation, and protein processing. Significantly implicated pathways included mRNA surveillance, proteasome, protein processing in the ER and SNARE interactions in vesicular transport.

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Independent component analysis of microarray data in the study of endometrial cancer

Cited by 90 publications

References 27 publications

Blind source separation methods for deconvolution of complex signals in cancer biology

Blind source separation methods for deconvolution of complex signals in cancer biology

Identification of molecular markers and signaling pathway in endometrial cancer in Hong Kong Chinese women by genome-wide gene expression profiling

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in‐creased production capacity

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