Skip to main content
Fig. 4 | BMC Bioinformatics

Fig. 4

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

Fig. 4

Analysis of ImmVar dataset interprets multiple dimensions of expression variation. a Violin and box plots of percent variation in gene expression explained by each variable. b Principal components analysis of gene expression with experiments colored by batch. c Total variance for each gene is partitioned into the fraction attributable to each dimension of variation in the study design. d Expression of UTY stratified by sex. e Expression of TLR4 stratified by cell type. f Expression of GSTM1 stratified by individual. g Scatter plot of percent GC content and percent variance explained by batch. Red line indicates linear regression line with regression coefficient, coefficient of determination and p-value shown. h Results from variancePartition analysis allowing the contribution of individual to vary in each cell type. i Probability of each gene having a cis-eQTL plotted against the percent variance explained by individual within each cell type. Dashed lines indicate the genome-wide average probability, and curves indicate logistic regression smoothed probabilities as a function of the percent variance explained by individual within each cell type. 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

Back to article page
\