Specification of running parameters of CalicoST

Supporting reference files

geneticmap_file: str: The path to genetic map file.
hgtable_file: str: The path to the location of genes in the genome. This file should be a tab-delimited file with the following columns: gene_name, chrom, cdsStart, cdsEnd.
normalidx_file: str, optional: The path to the file containing the indices of normal spots in the spatial transcriptomics data. Each line is a single index without header.
tumorprop_file: str, optional: The path to inferred tumor proportions per spot. This file should be a tab-delimited file with the following columns names: barcode, Tumor.
filtergenelist_file: str, optional: The file to a list of genes to exclude from CNA inference, based on prior knowledge.
filterregion_file: str, optional: The file to a list of genomic regions to exclude from CNA inference in BED format. E.g., HLA regions.

logphase_shift: float, optional: Adjustment to the strength of Markov Model self-transition in phasing. The higher the value, the higher self-transition probability. Default is -2.0.
secondary_min_umi: int, optional: The minimum UMI count a genome segment has in pseudobulk of spots in the step of genome segmentation. Default is 300.

n_clones: int: The number of clones to infer using only BAF signals. Default is 3.
n_clones_rdr: int, optional: The number of clones to refine for each BAF-identified clone using RDR and BAF signals. Default is 2.
min_spots_per_clone: int, optional: The minimum number of spots required to call a clone should have. Default is 100.
min_avgumi_per_clone: int, optional: The minimum average UMI count required for a clone. Default is 10.
nodepotential: str, optional: One of the following two options: “max” or “weighted_sum”. “max” refers to using the MLE decoding of HMM in evaluating the probability of spots being in each clone. “weighted_sum” refers to using the full HMM posterior probabilities to evaluate the probability of spots being in each clone. Default is “weighted_sum”.
spatial_weight: float, optional: The strength of spatial coherence in HMRF. The higher the value, the stronger the spatial coherence. Default is 1.0.

n_states: int: The number of allele-specific copy number states in the HMM for CNA inference.
t: float, optional: The self-transition probability of HMM. The higher the value, the higher probability that adjacent genome segments are in the same CNA state. Default is 1-1e-5.
max_iter: int, optional: The number of Baum-Welch steps to perform in HMM. Default is 30.
tol: float, optional: The convergence threshold to terminate Baum-Welch steps. Default is 1e-4.

np_threshold: float, optional: The threshold of Neyman Pearson statistics to decide two clones have distinct CNA events. The higher the value, the two clones are merged more easily. Default is 1.0.
np_eventminlen: int, optional: The minimum number of consecutive genome segments to be considered as a CN event. Default is 10.