2,368 research outputs found
Bayesian estimation of Differential Transcript Usage from RNA-seq data
Next generation sequencing allows the identification of genes consisting of
differentially expressed transcripts, a term which usually refers to changes in
the overall expression level. A specific type of differential expression is
differential transcript usage (DTU) and targets changes in the relative within
gene expression of a transcript. The contribution of this paper is to: (a)
extend the use of cjBitSeq to the DTU context, a previously introduced Bayesian
model which is originally designed for identifying changes in overall
expression levels and (b) propose a Bayesian version of DRIMSeq, a frequentist
model for inferring DTU. cjBitSeq is a read based model and performs fully
Bayesian inference by MCMC sampling on the space of latent state of each
transcript per gene. BayesDRIMSeq is a count based model and estimates the
Bayes Factor of a DTU model against a null model using Laplace's approximation.
The proposed models are benchmarked against the existing ones using a recent
independent simulation study as well as a real RNA-seq dataset. Our results
suggest that the Bayesian methods exhibit similar performance with DRIMSeq in
terms of precision/recall but offer better calibration of False Discovery Rate.Comment: Revised version, accepted to Statistical Applications in Genetics and
Molecular Biolog
Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts
As a fruit of the current revolution in sequencing technology, transcriptomes can now be analyzed at an unprecedented level of detail. These advances have been exploited for detecting differential expressed genes across biological samples and for quantifying the abundances of various RNA transcripts within one gene. However, explicit strategies for detecting the hidden differential abundances of RNA transcripts in biological samples have not been defined. In this work, we present two novel statistical tests to address this issue: a 'gene structure sensitive' Poisson test for detecting differential expression when the transcript structure of the gene is known, and a kernel-based test called Maximum Mean Discrepancy when it is unknown. We analyzed the proposed approaches on simulated read data for two artificial samples as well as on factual reads generated by the Illumina Genome Analyzer for two _C. elegans_ samples. Our analysis shows that the Poisson test identifies genes with differential transcript expression considerably better that previously proposed RNA transcript quantification approaches for this task. The MMD test is able to detect a large fraction (75%) of such differential cases without the knowledge of the annotated transcripts. It is therefore well-suited to analyze RNA-Seq experiments when the genome annotations are incomplete or not available, where other approaches have to fail
Perplexity: Evaluating Transcript Abundance Estimation in the Absence of Ground Truth
There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best.
Thus, we derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis.
To our knowledge, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth
Quantifying alternative splicing from paired-end RNA-sequencing data
RNA-sequencing has revolutionized biomedical research and, in particular, our
ability to study gene alternative splicing. The problem has important
implications for human health, as alternative splicing may be involved in
malfunctions at the cellular level and multiple diseases. However, the
high-dimensional nature of the data and the existence of experimental biases
pose serious data analysis challenges. We find that the standard data summaries
used to study alternative splicing are severely limited, as they ignore a
substantial amount of valuable information. Current data analysis methods are
based on such summaries and are hence suboptimal. Further, they have limited
flexibility in accounting for technical biases. We propose novel data summaries
and a Bayesian modeling framework that overcome these limitations and determine
biases in a nonparametric, highly flexible manner. These summaries adapt
naturally to the rapid improvements in sequencing technology. We provide
efficient point estimates and uncertainty assessments. The approach allows to
study alternative splicing patterns for individual samples and can also be the
basis for downstream analyses. We found a severalfold improvement in estimation
mean square error compared popular approaches in simulations, and substantially
higher consistency between replicates in experimental data. Our findings
indicate the need for adjusting the routine summarization and analysis of
alternative splicing RNA-seq studies. We provide a software implementation in
the R package casper.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS687 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org). With correction
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Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.
Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer
Deep generative modeling for single-cell transcriptomics.
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task
MapSplice: Accurate Mapping of RNA-Seq Reads for Splice Junction Discovery
The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. We introduce a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. MapSplice can be applied to both short (\u3c75 bp) and long reads (≥75 bp). MapSplice is not dependent on splice site features or intron length, consequently it can detect novel canonical as well as non-canonical splices. MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy. We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data. Experimental studies also support the accuracy of the algorithm. Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alternative splicing on a global level as well as the differences between molecular subtypes of breast cancer. These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions. Software download URL: http://www.netlab.uky.edu/p/bioinfo/MapSplice
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