Table 1.
The adopted mapping methods.
Fig 1.
Overview of the pipeline presented in this work.
The adopted biological samples to generate the qRT-PCR data were the same as those used to generate the RNA-Seq data.
Table 2.
Adopted methods for DEGs identification.
Fig 2.
Comparison of identified DEGs from different expression analysis tools, associated to distinct RNA-Seq mapping methods compared to qRT-PCR.
(A) Venn diagram comparing identified DEGs by the baySeq tool with BWA, TopHat, Bowtie and qRT-PCR mappers. (B) Venn diagram comparing identified DEGs by the edgeR tool with BWA, TopHat, Bowtie and qRT-PCR mappers. (C) Venn diagram comparing identified DEGs by the NOIseq with BWA, TopHat, Bowtie and qRT-PCR mappers. (D) Venn diagram comparing identified DEGs by the DESeq with BWA, TopHat, Bowtie and qRT-PCR mappers.
Table 3.
Comparison of the number of identified DEGs from different expression analysis tools, associated to different RNA-Seq mapping methods compared to qRT-PCR.
DEGs indicated by the edgeR and NOISeq tool using data from different mappers. qRT-PCR row indicates the amount of correctly labeled DEGs.
Table 4.
Performance of the DEGs software tools regarding the qRT-PCR results.
Performance measures adopted: TPR (True Positive Rate), SPC (Specificity), PPV (Positive Predict Value), ACC (Accuracy) and F1 measure [46, 47].
Fig 3.
Histogram from DEGs identification methods integration.
The red bars indicate the DEGs identified as differentially expressed (True Positives). The blue bars indicate the not differentially expressed transcripts identified as DEGs from methods (False Positives). The Y axis indicates the amount of tools that identified correctly the transcripts as differentially expressed or not. The first row (bars with 0 in Y axis) indicate DEGs and not differentially expressed genes from qRT-PCR (gold standard) with 413 DEGs and 584 not differentially expressed transcripts, totaling 997 genes analyzed. There are no performance values for nine tools, since there was no convergence of the results with transcripts indicated by nine methods as DEG.
Table 5.
Performance of each subset of DEGs identification methods.
Fig 4.
ROC curve from integration of DEG identification methods.
Each point indicate the performance of the best subset regarding the adopted qRT-PCR.
Fig 5.
Projection curves of TPR and SPC.
Projection curves of TPR and SPC values when combining DEGs identification methods. The X axis is the quantity of combined DEGs identification methods. The Y axis is the evolution of TPR and SPC values regarding the adopted qRT-PCR.
Table 6.
Relation between True Positives (TP) and aggregate results from number of methods.
Table 7.
Number of correctly identified DEGs from each method considering the aggregate results (consensus).