SATS stands for Signature Analyzer for Targeted Sequencing and performs mutational signature analysis for targeted sequenced tumors. Unlike the canonical analysis of mutational signatures, SATS factorizes the mutation counts matrix into a panel context matrix (measuring the size of the targeted sequenced genome for each tumor in the unit of million base pairs (Mb)), a signature profile matrix, and a signature activity matrix. SATS also calculates the expected number of mutations attributed by a signature, namely signature burden, for each targeted sequenced tumor. For more details see Lee et al. (2024) <doi:10.1101/2023.05.18.23290188>.

A metric expressing the quality of a UMAP layout. This is a
    package that contains the Saturn_coefficient() function that reads an
    input matrix, its dimensionality reduction produced by UMAP, and
    evaluates the quality of this dimensionality reduction by producing a
    real value in the [0; 1] interval. We call this real value Saturn
    coefficient. A higher value means better dimensionality reduction; a
    lower value means worse dimensionality reduction.
    Reference: Davide Chicco et al. (February 2026), "The advantages of
    our proposed Saturn coefficient over continuity and trustworthiness
    for UMAP dimensionality reduction evaluation", PeerJ Computer
    Science 12:e3424 (pp. 1-30), <doi:10.7717/peerj-cs.3424>.

Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.