Analysis workflow steps' impact on performance. Precision and recall for each iteration, separated by read aligner and expression estimator (rows) and differential gene tool (columns). Colors represent read depths and shapes represent sample number. These are the same data presented in Fig. 1 with color and shape labels switched. (PDF 2300 kb
Inference for two-sample T-statistic, NaĂŻve LRT implementation, Publicly available RNA-seq datasets...
RNA-seq is capable of making transcript isoform-specific measurements. However, long reads of high d...
Correlation RIN BioAnalyzer vs MultiNA. RIN values as measured by the Bioanalyzer (Agilent) were com...
Impact on performance by read depth and sample number. Precision and recall, averaged over the 10 it...
Impact on rank performance by read depth and sample number. Rank precision and rank recall, averaged...
Interactive figure for comparison of performance metrics. (A) Absolute precision and recall for each...
Sample combinations for each iteration at varying sample numbers. The same sample combinations were ...
Number of significant genes by number of biological replicates. Bar represents average number of sig...
Literature survey citations and average sample number. 200 studies containing RNA-Seq differential e...
Figure of recall and precision, for each reference dataset. Precision and recall as assessed using t...
Number of SAMseq failed iterations. Iteration was counted as a failure if SAMseq was not successfull...
Table of number of significant genes identified, for each workflow against each reference dataset. (...
Abstract Background RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for ea...
Figure of similarity in performance characteristics of significant gene identification by limma and ...
Simulated data benchmark performance. This table contains the precision and recall estimates for sev...
Inference for two-sample T-statistic, NaĂŻve LRT implementation, Publicly available RNA-seq datasets...
RNA-seq is capable of making transcript isoform-specific measurements. However, long reads of high d...
Correlation RIN BioAnalyzer vs MultiNA. RIN values as measured by the Bioanalyzer (Agilent) were com...
Impact on performance by read depth and sample number. Precision and recall, averaged over the 10 it...
Impact on rank performance by read depth and sample number. Rank precision and rank recall, averaged...
Interactive figure for comparison of performance metrics. (A) Absolute precision and recall for each...
Sample combinations for each iteration at varying sample numbers. The same sample combinations were ...
Number of significant genes by number of biological replicates. Bar represents average number of sig...
Literature survey citations and average sample number. 200 studies containing RNA-Seq differential e...
Figure of recall and precision, for each reference dataset. Precision and recall as assessed using t...
Number of SAMseq failed iterations. Iteration was counted as a failure if SAMseq was not successfull...
Table of number of significant genes identified, for each workflow against each reference dataset. (...
Abstract Background RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for ea...
Figure of similarity in performance characteristics of significant gene identification by limma and ...
Simulated data benchmark performance. This table contains the precision and recall estimates for sev...
Inference for two-sample T-statistic, NaĂŻve LRT implementation, Publicly available RNA-seq datasets...
RNA-seq is capable of making transcript isoform-specific measurements. However, long reads of high d...
Correlation RIN BioAnalyzer vs MultiNA. RIN values as measured by the Bioanalyzer (Agilent) were com...