BRIE: transcriptome-wide splicing quantification in single cells

Huang, Yuanhua; Sanguinetti, Guido

doi:10.1186/s13059-017-1248-5

Cited by 98 publications

(116 citation statements)

References 31 publications

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“…scRNA-Seq; Pierson & Yau, 2015) or binomial distributed data (i.e. splicing events; Huang & Sanguinetti, 2017). Finally, while here we focus our attention on the point estimates of inferred factors, future extensions could attempt a more comprehensive Bayesian treatment that propagates evidence strength and estimation uncertainties to diagnostics and downstream analyses.…”

Section: Discussionmentioning

confidence: 99%

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

Argelaguet

Velten

Arnol

et al. 2018

Molecular Systems Biology

836

804

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Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

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Section: Discussionmentioning

confidence: 99%

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

Argelaguet

Velten

Arnol

et al. 2018

Molecular Systems Biology

836

804

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“…Simulation is one alternative task that has already been mentioned but there is also a group of tools designed to detect biological signals in scRNA-seq data apart from changes in expression. For example alternative splicing (BRIE 40 , Outrigger 41 , SingleSplice 42 ), single nucleotide variants (SSrGE 43 ) and allele-specific expression (SCALE 44 ). Reconstruction of immune cell receptors is another area that has received considerable attention from tools such as BASIC 45 , TraCeR 46 and TRAPeS 47 .…”

Section: Alternative Analysesmentioning

confidence: 99%

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Zappia

Phipson

Oshlack

2017

Preprint

101

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As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers.In order to better facilitate selection of appropriate analysis tools we have been cataloguing and curating new analysis tools, as they become available, in the scRNAtools database (www.scRNA-tools.org). Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools are developed to perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and as a record of the growth of the field over time.

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“…GLM analysis was also performed using the same event counts. MISO and BRIE were run using their own event annotation, corresponding to hg19 genome version and Ensembl annotation release 75 for all methods (21,24). An additional N ullmodel for dSreg without regulatory information, as in the simulations, was run to test the improvement in detection of splicing changes by including regulatory features.…”

Section: Regulatory Features: Clip-seq Derived Rbps Binding Sitesmentioning

confidence: 99%

“…We analyzed the data also with MISO, BRIE and DARTS. Both BRIE and DARTS use prior information to improve detection of splicing changes (21,24,58). dSreg and the N ull model showed the best performance, compared to all other methods, except in extremely low coverages (dilution factor > 100), in which DARTS overcame dSreg ( Figure 4A,B).…”

Section: Model Calibration Remains Robust While Decreasing the Propormentioning

confidence: 99%

“…Alternative mRNA processing can be studied at two different levels: 1) transcript quantification level, which can be based on a prior alignment (40,52), or can be directly estimated from fast pseudoalignment methods (9,39); and 2) event level quantification, as performed by popular tools such as MISO, MATS, vast-tools, DEXseq or SUPPA (1,3,23,24,46,53). Recent toolsshowed improved performance for the estimation of AS changes by using information about exon features and perturbation experiments (21,58). Since regulation is expected to take place locally, the event level analysis is the preferred approach to study the regulatory mechanisms underlying changes in AS profiles.…”

Section: Introductionmentioning

confidence: 99%

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dSreg: A bayesian model to integrate changes in splicing and RNA binding protein activity

Martí-Gómez

Lara‐Pezzi

Sánchez-Cabo

2019

Preprint

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Alternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two step-framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis. To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNAseq with data from regulatory features, e.g. binding sites of RNA binding proteins (RBPs). dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified alternative splicing changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RBPs experiments from ENCODE, as opposed to traditional enrichment methods such as Over-representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). dSreg opens the possibility to integrate a large amount of readily available RNAseq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments. dSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg. Bayesian models | Alternative splicing | RNA binding proteins | Splicing regulationCorrespondence: *elara@cnic.es, fscabo@cnic.es

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BRIE: transcriptome-wide splicing quantification in single cells

Cited by 98 publications

References 31 publications

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

dSreg: A bayesian model to integrate changes in splicing and RNA binding protein activity

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