Data-driven detection of subtype-specific and differentially expressed genes

Chen, Lulu; Lu, Yingzhou; Yu, Guoqiang; Clarke, Robert; Eyk, Jennifer E. Van; Herrington, David M.; Wang, Yue

doi:10.1101/517961

Cited by 5 publications

(13 citation statements)

References 29 publications

(46 reference statements)

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“…In relation to previous work, the effort to detect SMG can be traced back to One-Versus-Rest (OVR) test (Yu, Feng et al 2010), One-Versus-One (OVO) test (Hunt, Freytag et al 2019), and most recently One-Versus-Everyone (OVE) test (Wang, Hoffman et al 2016). We and others have recognized that the test statistics used by most existing methods do not exactly satisfy SMG definition (1) and often require ad hoc OVE set intersection (Yu, Feng et al 2010, Hunt, Freytag et al 2019, Chen, Lu et al 2020.…”

Section: Resultsmentioning

confidence: 99%

“…More importantly, COT framework is efficient in that neither OVE set intersection nor intractable sample permutation is needed in estimating the null distribution (Hunt, Freytag et al 2019, Chen, Lu et al 2020, where a significant majority of genes are assumed to be associated with the null hypothesis. The null distribution plays a crucial role in large-scale multiple testing.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…The null distribution plays a crucial role in large-scale multiple testing. However, because the size of subtype-enriched samples is often relatively small and the non-SMG patterns are highly complex, classical methods to estimate the null distribution in a two-sample test setting is impractical or intractable (Chen, Lu et al 2020) and…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…and Multiple Comparisons with the Best (MCB) (Hsu 1996, Wang, Master et al 2006, Newman, Liu et al 2015. Importantly, we and others have recognized that the test statistics used by most existing methods do not exactly satisfy SMG definition and often ad hoc OVE set intersections have been used to finalize SMG (Wang, Hoffman et al 2016, Chen, Lu et al 2020, Patrick, Taga et al 2020.…”

Section: Methodsmentioning

confidence: 99%

“…We implement and test the latest functionalities of COT workflow in Python through a three-step assessment. We have previously demonstrated the successful applications of COT prototype on gene expression and proteomics data (Yu, Feng et al 2010, Chen, Lu et al 2020.…”

Section: Introductionmentioning

confidence: 99%

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COT: an efficient Python tool for detecting marker genes among many subtypes

Chao

Parker

et al. 2021

Preprint

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We develop an accurate and efficient method to detect marker genes among many subtypes using subtype-enriched expression profiles. We implement a Cosine based One-sample Test (COT) Python software that is easy to use and applicable to multi-omics data. We demonstrate the performance and utility of COT on gene expression and proteomics data acquired from tissue or cell subtypes. Formulated as a one-sample test with Cosine similarity test statistic in scatter space, the detected de novo marker genes will allow biologists to perform a more comprehensive and unbiased molecular characterization, deconvolution and classification of complex tissue or cell subtypes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussion and Outlookmentioning

confidence: 99%

Section: Discussion and Outlookmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COT: an efficient Python tool for detecting marker genes among many subtypes

Chao

Parker

et al. 2021

Preprint

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swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

et al. 2021

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Motivation Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and subtype-specific expressions. Existing deconvolution methods can only estimate averaged subtype-specific expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. Results We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and subtype-specific expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate subtype-specific expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer’s disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. Availability The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user’s guide and a vignette are provided. Supplementary information Supplementary data are available at Bioinformatics online.

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swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

Chen

Chao

Lin

et al. 2021

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We report a sample-wise fully unsupervised deconvolution method, namely sample-wise Convex Analysis of Mixtures (swCAM), that can estimate constituent proportions and subtype-specific expressions in individual samples using tissue-level bulk data (Chen 2019). The swCAM software tool enables statistically-principled subtype-level downstream analyses, such as detecting subtype-specific differentially expressed genes (sDEG) and differential dependency networks (DDN) (Zhang, Li et al. 2009, Chen, Lu et al. 2020). Significantly different from population-level deconvolution, individual-level deconvolution is mathematically an underdetermined problem because there are more variables than observations. We therefore extend the existing CAM framework by adding an extra term of between-sample variations and formulate swCAM as a nuclear-norm regularized low-rank matrix factorization problem (Wang, Hoffman et al. 2016). We determine hyperparameter value by random entry exclusion based cross-validation scheme and obtain swCAM solution using a modified efficient alternating direction method of multipliers (ADMM). Experimental results on realistic simulation data sets show that swCAM can accurately perform sample-wise unsupervised deconvolution of complex tissues and successfully recover subtype-specific correlation networks that are otherwise unobtainable using existing methods.

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Data-driven detection of subtype-specific and differentially expressed genes

Cited by 5 publications

References 29 publications

COT: an efficient Python tool for detecting marker genes among many subtypes

COT: an efficient Python tool for detecting marker genes among many subtypes

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

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