A Self-Directed Method for Cell-Type Identification and Separation of Gene Expression Microarrays

Zuckerman, Neta S.; Noam, Yair; Goldsmith, Andrea; Lee, Peter P.

doi:10.1371/journal.pcbi.1003189

Cited by 34 publications

(40 citation statements)

References 14 publications

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“…Potential limitations include the use of tissue from hepatectomy, which may have influenced the gene expression in HCs 34 and the small amount of tissue available in NAFLD, which did not allow for a more detailed lipid analysis. Future studies could separate different cell types from the whole tissue as the cellular composition influences gene expression 43 and perform proteomic analysis to examine gene transcription. 5 Furthermore, the cross-sectional nature of our study did not allow us to establish causal relationships, especially since hepatic gene expression and FA composition are subject to reciprocal regulation.…”

Section: Discussionmentioning

confidence: 99%

Altered hepatic gene expression in nonalcoholic fatty liver disease is associated with lower hepatic n‐3 and n‐6 polyunsaturated fatty acids

Arendt

Comelli²,

W.L.³

et al. 2015

Hepatology

273

240

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In nonalcoholic fatty liver disease, hepatic gene expression and fatty acid (FA) composition have been reported independently, but a comprehensive gene expression profiling in relation to FA composition is lacking. The aim was to assess this relationship. In a cross‐sectional study, hepatic gene expression (Illumina Microarray) was first compared among 20 patients with simple steatosis (SS), 19 with nonalcoholic steatohepatitis (NASH), and 24 healthy controls. The FA composition in hepatic total lipids was compared between SS and NASH, and associations between gene expression and FAs were examined. Gene expression differed mainly between healthy controls and patients (SS and NASH), including genes related to unsaturated FA metabolism. Twenty‐two genes were differentially expressed between NASH and SS; most of them correlated with disease severity and related more to cancer progression than to lipid metabolism. Biologically active long‐chain polyunsaturated FAs (PUFAs; eicosapentaenoic acid + docosahexaenoic acid, arachidonic acid) in hepatic total lipids were lower in NASH than in SS. This may be related to overexpression of FADS1, FADS2, and PNPLA3. The degree and direction of correlations between PUFAs and gene expression were different among SS and NASH, which may suggest that low PUFA content in NASH modulates gene expression in a different way compared with SS or, alternatively, that gene expression influences PUFA content differently depending on disease severity (SS versus NASH). Conclusion: Well‐defined subjects with either healthy liver, SS, or NASH showed distinct hepatic gene expression profiles including genes involved in unsaturated FA metabolism. In patients with NASH, hepatic PUFAs were lower and associations with gene expression were different compared to SS. (Hepatology 2015;61:1565–1578)

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Section: Discussionmentioning

confidence: 99%

Altered hepatic gene expression in nonalcoholic fatty liver disease is associated with lower hepatic n‐3 and n‐6 polyunsaturated fatty acids

Arendt

Comelli²,

W.L.³

et al. 2015

Hepatology

273

240

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“…Experimental results show that both overall and subtype-specific OVE-sFC test achieve a well-controlled FDR that matches the q-value cutoff (Figure S5-S6 For pAUC, the OVE strategy in OVE-FC/sFC test achieved the highest power in detecting SDEGs (Figure 3, Figure S7-S8, Table S1-S3). More specifically, for more realistic SDEGs (with sufficiently large fold change), OVE-sFC test shows the best performance; for the ideal SDEGs (i.e., marker genes with significantly large fold change 8,19 ), both OVE-FC and OVE-sFC achieve the best performance with slight outperformance by OVE-FC. In comparison with the peer methods, OVE-sFC test consistently outperforms OVO t-test in these challenging experiments involving more subtypes and using RNAseq data.…”

Section: Comparative Assessment Of Ove-fc/sfc Test On Power Of Detectmentioning

confidence: 99%

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

Chen

et al. 2019

Preprint

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Tissue or cell subtype-specific and differentially-expressed genes (SDEGs) are defined as being differentially expressed in a particular tissue or cell subtype among multiple subtypes. Detecting SDEGs plays a critical rolse in molecularly characterizing and identifying tissue or cell subtypes, and facilitating supervised deconvolution of complex tissues. Unfortunately, classic differential analysis assumes a convenient null hypothesis and associated test statistic that is subtype-non-specific and thus, resulting in a high false positive rate and/or lower detection power with respect to particular subtypes. Here we introduce One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. To assess the statistical significance of such test, we also propose the scaled test statistic OVE-sFC together with a mixture null distribution model and a tailored permutation scheme. Validated with realistic synthetic data sets on both type 1 error and detection power, OVE-FC/sFC test applied to two benchmark gene expression data sets detects many known and de novo SDEGs. Subsequent supervised deconvolution results, obtained using the SDEGs detected by OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.

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“…We note that the input cell-subset proportions in these two method classes, may come either from actual measurements or computationally estimated. In fact, a fifth category (E) consists of complete deconvolution methods which estimate both proportions and cell type-specific expression profiles, often using a combination of deconvolution methods (B and D), and require some limited prior knowledge on proportions [31] or expression profiles [30, 32, 19, 33, 34, 35, 36]. …”

Section: Extracting Cell Type-specific Information From Heterogenementioning

confidence: 99%

Computational deconvolution: extracting cell type-specific information from heterogeneous samples

Shen-Orr

Gaujoux

2013

Current Opinion in Immunology

267

255

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The quanta unit of the immune system is the cell; yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.

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A Self-Directed Method for Cell-Type Identification and Separation of Gene Expression Microarrays

Cited by 34 publications

References 14 publications

Altered hepatic gene expression in nonalcoholic fatty liver disease is associated with lower hepatic n‐3 and n‐6 polyunsaturated fatty acids

Altered hepatic gene expression in nonalcoholic fatty liver disease is associated with lower hepatic n‐3 and n‐6 polyunsaturated fatty acids

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

Computational deconvolution: extracting cell type-specific information from heterogeneous samples

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