Figure 1. Subclass mapping (SubMap) methodology. Two independent data sets, A and B, are clustered separately, compared and integrated.

(a) Candidate subclasses are defined by clustering A and B (predetermined phenotype can also be used). Marker genes of each candidate subclass in A (A_i) are selected, and mapped onto a gene list ranked according to their differential expression with respect to a subclass of B (B_j). Their over-representation at the top of the ranking is evaluated using the enrichment score (ES_AiBj), and significance is assessed as a nominal p-value, p_AiBj, by randomly permuting sample class labels in B. This process is repeated by interchanging the role of A and B to compute ES_BjAi and p_BjAi. (b) Mutual enrichment information, p_AiBj and p_BjAi, are combined using the Fisher inverse chi-square statistic, F_ij. Its significance is estimated based on a null distribution for the F_ij generated by randomly picking the nominal-p from corresponding null distributions for ES_AiBj and ES_BjAi. After multiple hypothesis testing (MHT) correction, p-values for F_ij are summarized in the subclass association (SA) matrix. Clustering of the SA matrix reveals subclasses common to A and B.