Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures

Zaitsev, Konstantin; Bambousková, Monika; Swain, Amanda; Artyomov, Maxim N.

doi:10.1038/s41467-019-09990-5

Cited by 99 publications

(141 citation statements)

References 29 publications

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“…Previous studies show that the expression profiles from each cell type are linearly additive; this makes the contribution of each cell type proportional to its fraction in the mixture profile [34].…”

Section: Resultsmentioning

confidence: 99%

“…Studying the variation of cell-type composition also opens new avenues in analyzing a tremendous yet underexplored quantity of biomedical data that has already been collected in clinics. Therefore, a number of bulk deconvolution techniques have been proposed in the literature for analyzing cellular composition from mixture samples [12,13,16,20,28,27,34]. These techniques mainly rely on a so-called signature matrix consisting of the gene signatures chiefly of well defined cell types [2,20].…”

Section: Introductionmentioning

confidence: 99%

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AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

Aliee¹,

Theis

2020

Preprint

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Tissues are complex systems of interacting cell types. Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. When measuring such responses using RNA-seq, bulk RNA-seq masks cellular heterogeneity. Hence, several computational methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles highly depends on the set of genes undergoing deconvolution. These genes are often selected based on prior knowledge or a single-criterion test that might not be useful to dissect closely correlated cell types. In this work, we introduce AutoGeneS, a tool that automatically extracts informative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. It can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Results from human samples of peripheral blood illustrate that AutoGeneS outperforms other methods. Our results also highlight the impact of our approach on analyzing bulk RNA samples with noisy single-cell reference profiles and closely correlated cell types. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of the predictions of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

Aliee¹,

Theis

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Gene expression variation of cell type markers can be also used to estimate cell type heterogeneity in bulk RNA-sequencing experiments. If the cell types within a culture have been previously well characterised, deconvolution approaches will use these profiles to estimate the cell type proportions within a heterogeneous culture (Wang et al, 2019;Zaitsev et al, 2019). However, single-marker genes can also be used.…”

Section: Identify and Remove Unwanted Variationmentioning

confidence: 99%

Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility

Volpato

Webber

2020

Disease Models &Amp; Mechanisms

257

169

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Induced pluripotent stem cell (iPSC) technologies have provided in vitro models of inaccessible human cell types, yielding new insights into disease mechanisms especially for neurological disorders. However, without due consideration, the thousands of new human iPSC lines generated in the past decade will inevitably affect the reproducibility of iPSC-based experiments. Differences between donor individuals, genetic stability and experimental variability contribute to iPSC model variation by impacting differentiation potency, cellular heterogeneity, morphology, and transcript and protein abundance. Such effects will confound reproducible disease modelling in the absence of appropriate strategies. In this Review, we explore the causes and effects of iPSC heterogeneity, and propose approaches to detect and account for experimental variation between studies, or even exploit it for deeper biological insight.

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“…We benchmarked DeCompress performance across 6 datasets (see Supplemental Table S2): (1) insilico mixing experiments using tissue-specific expression profiles from the Genotype-Tissue Expression (GTEx) Project (53,54), (2) expression from 4 published datasets with known compartment proportions (11,23,58,59), and (3) and tumor expression from the Carolina Breast Cancer Study (43,55). We compared the performance of DeCompress against 5 other reference-free deconvolution methods (summarized in Supplemental Table S1): deconf (20), Linseed (22), DeconICA (24), iterative non-10 negative matrix factorization with feature selection using TOAST (TOAST + NMF) (25), and…”

Section: Benchmarking Decompress Against Other Reference-free Deconvomentioning

confidence: 99%

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Bhattacharya

Hamilton

Troester

et al. 2020

Preprint

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Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

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Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures

Cited by 99 publications

References 29 publications

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

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