Fig. 1

PCA (a) and hierarchical clustering of subjects with heatmap showing subject-to-subject distances (b). PCA and subject clustering were performed with the gene expression count data, previously transformed with the regularized logarithm (as implemented in the DESeq2 rlog function). (a) PCA distinguished two patient groups from HC. Each point on the plot represents an individual sample, as HC are in red, IgAVN patients in green and sl-IgAV patients in blue. Samples that cluster closer together on the plot share more similar gene expression patterns. (b) Hierarchical clustering of subjects, utilizing the Euclidean distance and the complete-linkage agglomeration method, revealed distinct transcriptomes for IgAVN, sl-IgAV and HC. However, the subject sl-IgAV [1] was an exception, clustering with the HC subjects instead. Each row and column correspond to a different individual. The matrix depicts the degree of similarity or dissimilarity between pairs of samples based on their gene expression profiles. Darker colours or shorter distances indicate greater similarity, while brighter colours or longer distances represent increased dissimilarity. PCA, principal component analysis; IgAV, Immunoglobulin A vasculitis; HC, healthy controls; IgAVN, IgA with renal involvement; sl-IgAV, skin-limited IgAV.