Statistical expression deconvolution from mixed tissue samples

Clarke, Jennifer; Seo, Pearl H.; Clarke, Bertrand

doi:10.1093/bioinformatics/btq097

Cited by 66 publications

(65 citation statements)

References 28 publications

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“…Various methods have been used to estimate tissue percentage in tissues (2, 6–8). In an earlier study, we examined the cell composition of a series of fresh surgical specimens from prostate cancer patients by expression analysis using 88 Affymetrix U95Av2 GeneChips (2).…”

Section: Introductionmentioning

confidence: 99%

In silico Estimates of Tissue Components in Surgical Samples Based on Expression Profiling Data

et al. 2010

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Tissue samples from many diseases have been used for gene expression profiling studies, but these samples often vary widely in the cell types they contain. Such variation could confound efforts to correlate expression with clinical parameters. In principle, the proportion of each major tissue component can be estimated from the profiling data and used to triage samples before studying correlations with disease parameters. Four large gene expression microarray data sets from prostate cancer, whose tissue components were estimated by pathologists, were used to test the performance of multivariate linear regression models for in silico prediction of major tissue components. Ten-fold cross-validation within each data set yielded average differences between the pathologists' predictions and the in silico predictions of 8% to 14% for the tumor component and 13% to 17% for the stroma component. Across independent data sets that used similar platforms and fresh frozen samples, the average differences were 11% to 12% for tumor and 12% to 17% for stroma. When the models were applied to 219 arrays of “tumor-enriched” samples in the literature, almost one quarter were predicted to have 30% or less tumor cells. Furthermore, there was a 10.5% difference in the average predicted tumor content between 37 recurrent and 42 nonrecurrent cancer patients. As a result, genes that correlated with tissue percentage generally also correlated with recurrence. If such a correlation is not desired, then some samples might be removed to rebalance the data set or tissue percentages might be incorporated into the prediction algorithm. A web service, “CellPred,” has been designed for the in silico prediction of sample tissue components based on expression data.

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Section: Introductionmentioning

confidence: 99%

In silico Estimates of Tissue Components in Surgical Samples Based on Expression Profiling Data

et al. 2010

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“…This was supported by the results of a simulation study, in which the best sensitivity achieved by t-test was 66.7% (Figure 3(b)), while the sensitivity for the MLR analysis was 93.3% (Table 2). In addition, there have been attempts to tackle the problem of expression deconvolution using MLR-like approaches [24 - 26]. However, precise measures of cell components are not commonly available for many studies where a t-test still applies.…”

Section: Discussionmentioning

confidence: 99%

A Sample Selection Strategy to Boost the Statistical Power of Signature Detection in Cancer Expression Profile Studies

Jia¹,

Wang²,

Hu³

et al. 2013

ACAMC

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In case-control profiling studies, increasing the sample size does not always improve statistical power because the variance may also be increased if samples are highly heterogeneous. For instance, tumor samples used for gene expression assay are often heterogeneous in terms of tissue composition or mechanism of progression, or both; however, such variation is rarely taken into account in expression profiles analysis. We use a prostate cancer prognosis study as an example to demonstrate that solely recruiting more patient samples may not increase power for biomarker detection at all. In response to the heterogeneity due to mixed tissue, we developed a sample selection strategy termed Stepwise Enrichment by which samples are systematically culled based on tumor content and analyzed with t-test to determine an optimal threshold for tissue percentage. The selected tissue-percentage threshold identified the most significant data by balancing the sample size and the sample homogeneity; therefore, the power is substantially increased for identifying the prognostic biomarkers in prostate tumor epithelium cells as well as in prostate stroma cells. This strategy can be generally applied to profiling studies where the level of sample heterogeneity can be measured or estimated.

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“…A similar method has also been designed to estimate the tissue components from surgical samples 68 . When the mixed cell type proportions are not available, methods based on 69 or 70 have been developed to estimate simultaneously both mixture proportions and expression profiles for each component cell type.…”

Section: Data Mining For Transcriptomicsmentioning

confidence: 99%

Computational solutions for omics data

2013

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High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.

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Statistical expression deconvolution from mixed tissue samples

Cited by 66 publications

References 28 publications

In silico Estimates of Tissue Components in Surgical Samples Based on Expression Profiling Data

In silico Estimates of Tissue Components in Surgical Samples Based on Expression Profiling Data

A Sample Selection Strategy to Boost the Statistical Power of Signature Detection in Cancer Expression Profile Studies

Computational solutions for omics data

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