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34 352 Recent advances in single-cell transcriptomics are ideally placed to unravel 36 intratumoral heterogeneity and selective resistance of cancer stem cell (SC) 37 subpopulations to molecularly targeted cancer therapies. However, current 38 single-cell RNA-sequencing approaches lack the sensitivity required to 39 reliably detect somatic mutations. We developed a method combining high-40

Repeated emergence of SARS-CoV-2 variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR 0 , a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR 0 to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase fitness, including previously identified spike mutations and many non-spike mutations within the nucleocapsid and nonstructural proteins. PyR 0 forecasts growth of new lineages from their mutational profile, ranks the fitness of lineages as new sequences become available, and prioritizes mutations of biological and public health concern for functional characterization.

Single-cell RNA-seq is being increasingly applied in complex study designs, commonly spanning multiple individuals, conditions, or tissues. Analysis of such heterogeneous collections requires a way of identifying recurrent cell subpopulations. We developed Conos , an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.