We are analyzing https://link.springer.com/article/10.1186/1471-2105-11-94.

Title:
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments | BMC Bioinformatics
Description:
Background High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data. Results We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection. Conclusions Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.
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Keywords {🔍}

expression, genes, normalization, gene, sequencing, figure, data, counts, reads, file, statistics, lane, additional, mrnaseq, effects, qrtpcr, article, differential, pubmed, supplemental, differences, lanes, brain, methods, biological, uhr, number, library, google, scholar, samples, maqc, test, microarray, length, read, flowcell, analysis, preparation, standard, small, results, statistical, genome, total, ratio, phi, tstatistics, totalcount, rna,

Topics {✒️}

org/packages/release/bioc/html/genominator org/packages/release/bioc/html/genomegraphs absolute expression log-fold-change qrt-pcr absolute log-ratio high-throughput sequencing technologies delta method t-statistic brain expression log-fold-change ultra high-throughput sequencing default base-calling algorithm uhr/brain expression log-ratio determining true/false positives/negatives article download pdf median uhr/brain log-ratio flow-cell/library preparation effects base-calling calibration method purity-filtered perfectly matching delta method t-statistics high-throughput sequencing assays high-throughput dna sequencing uhr/brain expression fold-change high-throughput biological data drosophila melanogaster supports low-count scenario occurs calling true/false positives composite gene-level region mrna-seq datasets related high-throughput studies total-count normalization makes χ2 quantile-quantile plots 35 base-pair-long reads mantel-haenszel test compare likelihood ratio statistics absolute log-ratio 5 absolute log-ratio standard total-count normalization single lane-specific factor test statistic makes length-independent de filter including protein-coding genes false positive rates high-throughput sequencing phi x-calibrated sets exon-exon junction reads mrna-seq de results exact test statistic library preparation protocol privacy choices/manage cookies maximum likelihood estimators tag density required nominal p-values cluster

Questions {❓}

Here, we evaluate a variety of normalization procedures and focus on two main questions: (1) Does the normalization improve DE detection (sensitivity)?

Schema {🗺️}

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         Algorithms
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