Somatic evolution leads to clonal heterogeneity, which fuels cancer progression and therapy resistance. To decipher the consequences of clonal heterogeneity, we require a method that deconvolutes complex clonal architectures and their downstream transcriptional states. We developed Genotyping of Transcriptomes for multiple targets and sample types (GoT-Multi), a high-throughput, formalin-fixed paraffin-embedded (FFPE) tissue-compatible single-cell multi-omics for co-detection of multiple somatic genotypes and whole transcriptomes. We developed an ensemble-based machine learning pipeline to optimize genotyping. We applied GoT-Multi to frozen or FFPE samples of Richter transformation, a progression of chronic lymphocytic leukemia to therapy-resistant large B cell lymphoma. GoT-Multi detected heterogeneous cancer cell states with genotypic data of 27 mutations, enabling clonal architecture reconstruction linked with their transcriptional programs. Distinct subclonal genotypes, including therapy-resistant mutations, converged on an inflammatory state. Other subclones displayed enhanced proliferation and/or MYC program. Thus, GoT-Multi revealed that distinct genotypic identities may converge on similar transcriptional states to mediate therapy resistance.