Comparing methods detecting differentially expressed genes in RNA-seq data
Sprache des Titels:
ISMB 2012 Proceedings
Second Generation Sequencing (SGS) paved the way for deeper understanding of
cellular processes characterized by the expression of specific genes.
RNA-seq as an SGS-based approach is evolving into the go-to technique for analyzing the transcriptome.
One of the main goals in transcriptomics is
to identify differentially expressed genes across different conditions.
In this work we compare the most commonly used RNA-seq methods that detect
differentially expressed genes and provide a more extensive
comparison than previously published.
Toward this end we define evaluation criteria that take all thresholds for
considering a gene as being differentially expressed into account.
These measures are well established in the machine learning community and
provide a more appropriate performance measure than fixing an
arbitrary threshold for viewing a gene as being differentially expressed.
Both the sensitivity and the specificity are combined into one value using these measures.
Simulated datasets allow to assess the performance of the competitors
for various coverages and fold changes while knowing the ground truth.