Sample Size Estimation for Detection of Splicing Events in Transcriptome Sequencing Data

Merging data from multiple samples is required to detect low expressed transcripts or splicing events that might be present only in a subset of samples. However, the exact number of required replicates enabling the detection of such rare events often remains a mystery but can be approached through probability theory. Here, we describe a probabilistic model, relating the number of observed events in a batch of samples with observation probabilities. Therein, samples appear as a heterogeneous collection of events, which are observed with some probability. The model is evaluated in a batch of 54 transcriptomes of human dermal fibroblast samples. The majority of putative splice-sites (alignment gap-sites) are detected in (almost) all samples or only sporadically, resulting in an U-shaped pattern for observation probabilities. The probabilistic model systematically underestimates event numbers due to a bias resulting from finite sampling. However, using an additional assumption, the probabilistic model can predict observed event numbers within a <10% deviation from the median. Single samples contain a considerable amount of uniquely observed putative splicing events (mean 7122 in alignments from TopHat alignments and 86,215 in alignments from STAR). We conclude that the probabilistic model provides an adequate description for observation of gap-sites in transcriptome data. Thus, the calculation of required sample sizes can be done by application of a simple binomial model to sporadically observed random events. Due to the large number of uniquely observed putative splice-sites and the known stochastic noise in the splicing machinery, it appears advisable to include observation of rare splicing events into analysis objectives. Therefore, it is beneficial to take scores for the validation of gap-sites into account.