STARCH

Spatial Transcriptomics Algorithm Reconstructing Copy-number Heterogeneity

Required Python Libraries

Numpy, Pandas, Scipy, SKlearn, multiprocessing, HMMlearn

Required input

1. Gene x Spot STRNA-seq expression matrix. This can be a text file (.csv, .tsv, .txt) where the first column is contains the gene names in HUGO format and the first row contains the spot coordinates in the format '1x2'. STARCH can also read in the output from 10X's Space Ranger pipeline. In this case, the input is the path to a directory containing the following files: barcodes.tsv, features.tsv, matrix.mtx, and tissue_positions_list.csv

2. Gene mapping file which maps each HUGO gene name to chromosomal positions. The mapping file for human assembly hg19 is provided by default (hgTables_hg19.txt). For other organisms, an hgTables file can be downloaded from https://genome.ucsc.edu/cgi-bin/hgTables (when generating the hgTable, select group = Genes and Gene predictions, track = all GENCODE V33, region = Genome).

Running from Command Line

python run_STARCH.py -i gene_expression_matrix.csv (or 10X_directory/) --output name --n_clusters 3 --outdir output/directory/

Output

run_STARCH.py will output two files (1) the gene x clone CNV matrix which is saved to outdir/states_output.csv. (2) a spot label vector which assigns each spot to one of n_clusters clones which is saved to outdir/labels_output.csv.