Technical and biological variance structure in mRNA-Seq data
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE29022 | GEO | 2012/07/17
SECONDARY ACCESSION(S): PRJNA140475
REPOSITORIES: GEO
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Project description:mRNA expression data from next generation sequencing platforms is obtained in the form of counts per gene or exon. Counts are generally assumed to follow a Poisson distribution in which the variance is equal to the mean. However, it is common in observed count data with biological variation for over-dispersion to be present, i.e., for the variance to be greater than the mean. examination of technical and biological variance in mRNA-seq count data
2012-07-16 | E-GEOD-29022 | biostudies-arrayexpress
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2018-09-21 | GSE118723 | GEO
Project description:Next-generation sequencing is currently the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be pre-processed prior to conducting downstream analyses. Often overlooked, data pre-processing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis.
2015-03-21 | GSE67074 | GEO
Project description:The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers is hindered by high variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in library amplification steps and sequencing depth variation. Our analytical contributions are the description of the Linear Quadratic (LQ) relation between the mean and variance of the sequence counts in an RNA-seq experiment and the derivation of the Poisson truncated mixture as the underlying probability distribution for RNA-seq data. Using a large number of sequencing datasets, we demonstrate here how one can use this modeling framework to calculate empirical correction factors for ligase bias, while accounting for random variation in sequence counts. Bias correction may remove the majority of bias in the absence of differential expression and more than 40% of the bias in the presence of variable expression of miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition.
2017-03-15 | GSE93399 | GEO
Project description:BackgroundmRNA expression data from next generation sequencing platforms is obtained in the form of counts per gene or exon. Counts have classically been assumed to follow a Poisson distribution in which the variance is equal to the mean. The Negative Binomial distribution which allows for over-dispersion, i.e., for the variance to be greater than the mean, is commonly used to model count data as well.ResultsIn mRNA-Seq data from 25 subjects, we found technical variation to generally follow a Poisson distribution as has been reported previously and biological variability was over-dispersed relative to the Poisson model. The mean-variance relationship across all genes was quadratic, in keeping with a Negative Binomial (NB) distribution. Over-dispersed Poisson and NB distributional assumptions demonstrated marked improvements in goodness-of-fit (GOF) over the standard Poisson model assumptions, but with evidence of over-fitting in some genes. Modeling of experimental effects improved GOF for high variance genes but increased the over-fitting problem.ConclusionsThese conclusions will guide development of analytical strategies for accurate modeling of variance structure in these data and sample size determination which in turn will aid in the identification of true biological signals that inform our understanding of biological systems.
| S-EPMC3505161 | biostudies-literature
Project description:BackgroundRecent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.ResultsOur novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.ConclusionsPerformance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better.AvailabilityThe R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.
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Project description:Variance in microarray studies has been widely discussed as a critical topic of the identification of differentially expressed gene; however, few studies have addressed the influence of estimating variance. To break intra- and inter-individual variance in clinical studies down to three levels: technical, anatomic, and individual, we designed experiments and algorithms to investigate three forms of variances. As a case study, a group of “inter-individual variable genes” were identified to exemplify the influence of underestimated variance on the statistical and biological aspects in identification of differentially expressed genes. Our results showed that inadequate estimation of variance inevitably led to the inclusion of non-statistically significant genes into those listed as significant, thereby interfering with the correct prediction of biological functions. Applying a higher cutoff value of fold changes in the selection of significant genes reduce/eliminate the effects of underestimated variance. Our data demonstrates that an appropriate evaluation of variance is critical in selecting significant genes of differential expression. If the estimation of precise variance has not been adequately considered in the experimental design, using a higher fold change criteria is one possible solution to overcome the difficulties associated with the identification of significant genes, but it paid by losing the number of selected genes.
2011-03-02 | GSE27646 | GEO
Project description:Purpose: muscle transcriptomics of subjects supplemented with Urolithin A at different doses or placebo for 4 month. Method: RNA-seq was performed using Illumina HiSeq 4000 sequencing; single read 1 x 50 bp . The quantification of mRNA from the RNA-seq FASTQ files was performed using Salmon. Sample-wise quant.sf files containing raw transcript-level read estimates were read into R, v. 4.0.3 and were combined into a data matrix. Transcripts with very low total counts (< 10) across all samples were filtered out. The data was transformed using the variance stabilizing transformation (VST) method of R package DESeq2, v. 1.30.0. Top 10,000 transcripts with the highest variance across all samples were used for principal component analysis (PCA) using DESeq2. Data transformation and PCA was also done separately for each treatment group. Based on the PCAs, probable outlier samples were excluded and new PCAs were plotted without these samples. The raw transcript-level read count estimates were read in R and summarized to gene-level counts based on the provided transcript and gene ID annotations using summarizeToGene function of R package tximport, v. 1.18.0. DESeqDataSetFromTximport function of DESeq2 was then used for constructing a DESeqDataSet object for DE analysis. Pre-filtering was applied before the DE analysis by excluding genes with < 10 total counts across samples. Subset DE analysis was performed, contrasting Visit time D120 with baseline (BL) and by adjusting for the subject effect. The normalization and DE analysis was done separately for the three different treatment groups. Independent filtering option of DESeq2 was enabled (default), filtering out genes with very low counts and thus unlikely to show significant evidence. R package biomaRt. v. 2.46.0 was used for annotating the results with HGNC gene symbols, gene descriptions and gene biotypes. DESeq2-normalised expression values of all the samples in the given comparison were added to the result tables. Non-adjusted p-value 0.05 was used to filter the results by statistical significance. Results were also generated using DESeq2 function lfcShrink that allows for the shrinkage of the log2 fold change (LFC) estimates toward zero when the information for a gene is low (such as in those cases with low counts or high dispersion values) but has little effect on genes with high counts. The shrinked log2FC values were subsequently used for visualisation and ranking the genes.
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2023-09-22 | GSE218383 | GEO
Project description:A collection of budding yeast wild types grown in parallel to 1,484 budding yeast deletion mutants to assess day-to-day variance. Related to series GSE42526 and GSE42527.
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