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Consequently, choosing the appropriate stringency threshold is of crucial importance.
At the low stringency threshold at which inter-group relationships can be visualized using networks, many of the displayed edges represent poor alignments.
We found that the overlap with a recently published EWS-FLI1 signature [4] was significant, but ∼70% of the genes at a p-value cut-off of 0.1 (GS1) and ∼80% at a stringency threshold of 0.25 (GS2) were not present in this earlier data set.
In other words, using the trend for nonspecific genes at lower stringency levels, a stringency threshold was chosen that was high enough to consider 90% of the genes highly expressed in either fetal liver or placenta to be specifically derived from that tissue and not be a statistical artefact.
Modules were grouped using a stringency threshold of 0.75.
Dist.0.6 represents the most compact modules at the highest stringency threshold and dist.101.6 representing the least compact modules at the lowest stringency threshold.
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We measured the sensitivity, specificity and MCC at certain stringency thresholds.
To reduce false negative calls, we used stringency thresholds somewhat lower than those used in some other studies.
When applying certain stringency thresholds, it also significantly outperforms PSI-BLAST and BLAST at nearly all of the thresholds (Table 2).
To more precisely compare the predictors, we have applied three stringency thresholds corresponding to the 99, 95 and 90% specificities, respectively.
This suggests that the high stringency thresholds in both hybridization and bioinformatics are in fact conservative and there are potentially more genes that have yet to be discovered.
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