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  "Title": "Probabilistic Outlier Identification for RNA Sequencing\nGeneralized Linear Models",
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  "Description": "Relative transcript abundance has proven to be a valuable\ntool for understanding the function of genes in biological\nsystems. For the differential analysis of transcript abundance\nusing RNA sequencing data, the negative binomial model is by\nfar the most frequently adopted. However, common methods that\nare based on a negative binomial model are not robust to\nextreme outliers, which we found to be abundant in public\ndatasets. So far, no rigorous and probabilistic methods for\ndetection of outliers have been developed for RNA sequencing\ndata, leaving the identification mostly to visual inspection.\nRecent advances in Bayesian computation allow large-scale\ncomparison of observed data against its theoretical\ndistribution given in a statistical model. Here we propose\nppcseq, a key quality-control tool for identifying transcripts\nthat include outlier data points in differential expression\nanalysis, which do not follow a negative binomial distribution.\nApplying ppcseq to analyse several publicly available datasets\nusing popular tools, we show that from 3 to 10 percent of\ndifferentially abundant transcripts across algorithms and\ndatasets had statistics inflated by the presence of outliers.",
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