Interactive figure for comparison of performance metrics. (A) Absolute precision and recall for each workflow. (B) Relative ranks of precision and recall for each workflow. Grey dots show performance for all workflows for the selected read depth(s) and sample number(s); red dots highlight the selected workflow(s). (XLSX 536 kb
Figure S1. RNA-seq quality detection of differential regulated genes in air and ethylene treatment. ...
Abstract Background RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for ea...
Benchmark performance for Williams et al. data. This table contains the precision and recall estimat...
Analysis workflow steps' impact on performance. Precision and recall for each iteration, separated b...
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...
Sample combinations for each iteration at varying sample numbers. The same sample combinations were ...
Literature survey citations and average sample number. 200 studies containing RNA-Seq differential e...
Number of significant genes by number of biological replicates. Bar represents average number of sig...
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 S1. Column 1 and 2 show the sample type and replicates index; Column 3 shows total read pairs,...
Table of number of significant genes identified, for each workflow against each reference dataset. (...
Simulated data benchmark performance. This table contains the precision and recall estimates for sev...
Figure of similarity in performance characteristics of significant gene identification by limma and ...
Figure S1. RNA-seq quality detection of differential regulated genes in air and ethylene treatment. ...
Abstract Background RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for ea...
Benchmark performance for Williams et al. data. This table contains the precision and recall estimat...
Analysis workflow steps' impact on performance. Precision and recall for each iteration, separated b...
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...
Sample combinations for each iteration at varying sample numbers. The same sample combinations were ...
Literature survey citations and average sample number. 200 studies containing RNA-Seq differential e...
Number of significant genes by number of biological replicates. Bar represents average number of sig...
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 S1. Column 1 and 2 show the sample type and replicates index; Column 3 shows total read pairs,...
Table of number of significant genes identified, for each workflow against each reference dataset. (...
Simulated data benchmark performance. This table contains the precision and recall estimates for sev...
Figure of similarity in performance characteristics of significant gene identification by limma and ...
Figure S1. RNA-seq quality detection of differential regulated genes in air and ethylene treatment. ...
Abstract Background RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for ea...
Benchmark performance for Williams et al. data. This table contains the precision and recall estimat...