Skip to main content

Table 1 Poisson mixture model parameter estimates and SNPs classification results

From: Analyzing allele specific RNA expression using mixture models

Mixture component Proportion Poisson mean No. of SNPs No. of genes
Comp.1 0.030 (0.029, 0.031) 43.11 (42.54, 43.84) 18367 784
Comp.2 0.0011 (0.0010, 0.0012) 152.37 (146.08, 166.13) 519 37
Comp.3 0.186 (0.182, 0.190) 20.34 (20.20, 20.49) 82963 3892
Comp.4 0.003 (0.0025, 0.0033) 108.14 (105.13, 115.60) 2073 89
Comp.5 0.0006 (0.0004, 0.0008) 201.01 (196.15, 209.71) 425 27
Comp.6 0.0073 (0.0069, 0.0077) 74.60 (72.56, 78.08) 5156 202
Comp.7 0.771 (0.769, 0.775) 7.82 (7.78, 7.85) 198889 11174
  1. The Poisson mixture model was fitted to the averaged total reads within tissue-specific genes (62326 tissue-specific genes in total, i.e. sample size = 62326; overall log-likelihood = -216846; BIC = 433836). Genes with the same rs number but from different brain region were considered as different tissue-specific genes. We found the optimal number of mixture components to be 7, meaning that we could classify all SNPs into 7 “comparable” SNP groups. Most SNPs in the gene of our interest (SLC1A3) were classified into the mixture component Comp.1. The SNPs in Comp.1 were used to fit the folded Skellam mixture model