Sample size calculation and power estimation are essential components of experimental designs in biomedical research. It is very challenging to estimate power for RNA-Seq differential expression under complex experimental designs. Moreover, the dependency among genes should be taken into account ...
Read More »Feasibility of sample size calculation for RNA-seq studies
Sample size calculation is a crucial step in study design but is not yet fully established for RNA sequencing (RNA-seq) analyses. To evaluate feasibility and provide guidance, researchers from the Johannes Gutenberg University Mainz evaluated RNA-seq sample size tools identified ...
Read More »RNAtor – a mobile application for designing RNA-seq experiments
RNA sequencing (RNA-seq) is a powerful technology for identification of novel transcripts (coding, non-coding and splice variants), understanding of transcript structures and estimation of gene and/or allelic expression. There are specific challenges that biologists face in determining the number of ...
Read More »The impact of amplification on differential expression analyses by RNA-Seq
Currently quantitative RNA-Seq methods are pushed to work with increasingly small starting amounts of RNA that require PCR amplification to generate libraries. However, it is unclear how much noise or bias amplification introduces and how this effects precision and accuracy ...
Read More »Gene set analysis approaches for RNA-seq data – performance evaluation and application guideline
Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret ...
Read More »Modelling sample and observational level variability improves power in RNA-seq analyses
Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to ...
Read More »The Level of Residual Dispersion Variation and the Power of Differential Expression Tests for RNA-Seq Data
RNA-Sequencing (RNA-Seq) has been widely adopted for quantifying gene expression changes in comparative transcriptome analysis. For detecting differentially expressed genes, a variety of statistical methods based on the negative binomial (NB) distribution have been proposed. These methods differ in the ...
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