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Fig. 2 | BMC Bioinformatics

Fig. 2

From: variancePartition: interpreting drivers of variation in complex gene expression studies

Fig. 2

Analysis of GEUVADIS dataset identifies drivers of expression variation. a Total variance for each gene is partitioned into the fraction attributable to each dimension of variation in the study. b Violin and box plots of percent variation in gene expression explained by each variable. Three representative genes and their major sources of variation are indicated. c Boxplot of UTY expression stratified by sex. d Boxplot of CCDC85B expression stratified by lab. Inset shows scatter plot of percent GC content versus percent variance explained by lab. Red line indicates linear regression line with coefficient of determination and p-value shown. e Boxplot of ZNF470 expression stratified by individual for a subset of individuals with at least 1 technical replicate. Inset illustrates a cis-eQTL for ZNF470 where expression is stratified by genotype at rs2904239. f Probability of each gene having a cis-eQTL plotted against the percent variance explained by individual. Dashed lines indicate the genome-wide average probability (i.e. 18% of genes have a detected eQTL in this dataset), and curves indicate logistic regression smoothed probabilities as a function of the percent variance explained by individual. Points indicate a sliding window average of the probability of genes in each window having a cis-eQTL. Window size is 200 genes with an overlap of 100 genes between windows. The p-value indicates the probability that a more extreme coefficient relating the eQTL probability to percent variation explained by individual is observed under the null hypothesis

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