This directory contains the source code necessary to reproduce analyses presented in the manuscript:
Agnieszka Geras, Shadi Darvish Shafighi, Kacper Domżał, Igor Filipiuk, Alicja Rączkowska, Paulina Szymczak, Hosein Toosi, Leszek Kaczmarek, Łukasz Koperski, Jens Lagergren, Dominika Nowis and Ewa Szczurek.
Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data. Genome Biol 24, 120 (2023). https://doi.org/10.1186/s13059-023-02951-8
Cells counting procedure is here.
Questions about the implementation and general questions:
Agnieszka Geras, A.Geras[at]mini.pw.edu.pl
Manuscript's corresponding author:
Ewa Szczurek, szczurek[at]mimuw.edu.pl
Celloscope takes as input the following files provided in the input_data directory:
param.txt- file containing run setting (hyperparameters, number of iterations, etc.),matB.csv- binary matrix with prior knowledge about marker genes,C_gs.csv- gene expression data,n_cells.csv- estimates for the number of cells in each ST spot.
Celloscope can be run from the code directory as follows:
bash Celloscope-run.sh 'input_data' 'results' number_of_chains
The arguments of the bash script:
input_data- directory containing input data,results- directory dedicated to results,number_of_chains- number of independent chains to run.
Exemplary input files can be found in this repository in the example directory.
Please see the manual for the details on preparing data and guidance on visualising results.
data- data used for the analysis presented in the paper (ST data from mouse brain and human prostate and estimates for the number of cells for each spot),code- model's implementation,example- exemplary input files ready to run on Celloscope,manualexplaining the details of how to prepare data, run the model, and visualize results,comparison- code used to compare to preceding approaches.