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Thus another possible explanation for disagreement between pairs of probe sets is that each is reporting the transcription level of a different transcript of the same gene.
Thus schooled, the software could still identify only 80% of the characters in a different transcript of the same typist on the same machine.
To model this, for dataset T1 we hid roughly 40% of the expressed transcripts, at random, and replaced each one with a different unexpressed transcript of the same gene.
Briefly, each gene was assigned to have a sense and antisense transcript of the same length (i.e. coding region), which were then treated as independent transcripts in the EdgeR analyses.
Some reporters unambiguously distinguish between transcripts of a single gene, but others hybridize with more than one transcript of the same gene.
More precisely, the test statistic R compares the null hypothesis, L0 ("the abundance of a transcript is the same in all libraries"), with the alternative hypothesis, L1 ("the abundance of the transcript in each library is different", i.e., the corresponding gene is differentially expressed), using the log ratio of the two likelihoods, i.e., log(L 1 / L 0 ).
There are different approaches for EST clustering; the most commonly used being (1) each cluster represents a distinct gene, alternative transcripts of the same gene are grouped together into the same cluster.
We did not choose "gene-consistent" CDFs (probes of a probe set mapping to transcripts of the same set of genes) to avoid inconsistencies as recommended by Liu et al. [ 23].
