Package: exoTras
exoTras delineates exosome profiles at droplet resolution from single-cell transcriptomesexoTras /ˈɛkoʊˌtɹeɪs/ stands for exosome-containing droplet identification and source tracking in scRNA-seq data.You can freely use exoTras to explore exosome heterogeneity at single droplet, characterize cell type dynamics in light of exosome activity and unlock diagnostic potential of exosomes in concert with cells.Overview of exoTras.Prerequisites"numpy", "pandas", "scipy",
"scanpy", "statsmodels", "gseapy"InstallationpipinstallexoTrasWe also suggest to use a separate conda environment for installing exoTras.condacreate-y-nexoTras_envpython=3.7
condaactivateexoTras_env
pipinstallexoTrasBasic UsageInput for exoTras is a cell-by-gene matrix. In the case of scRNA-seq dataset using 10X Genomics, we usedraw_feature_bc_matrixdirectory generated by Cell Ranger as input. Output of exoTras consists of the score of exosome signals and classification for each droplet. Such exosome information will be used for downstream analysis and as basis for the construction of the exosome secretion activity index (ESAI) and source tracking functions.We implemented four functions for exosomes recognizing and functional analyses. In exoTras,exosomes_recognizerrecognizes exosome-containing droplets in the raw scRNA-seq data;source_trackerandESAI_celltypetrace these droplets to their original cell type and estimited corresponding exosome secretion activity; andcellfree_simulatorsimulates transcriptional profile of cell free droplets in scRNA-seq.ExamplesFor a study with multiple scRNA-seq samples, we support two input ways.### first    if samples locate in one directoryexoTras.exosomes_recognizer(input_path='directory_path',sample_file='sample_name',out_path='output_path',species='Homo')### second   if samples locate in differet directoriesexoTras.exosomes_recognizer(sample_file='sample_path',out_path='output_path',species='Homo')In the first way,directory_pathis the path for the directory that contains all sample; andsample_nameis the file that list each sample in th directory row by row. Every sample should contain directory structure like 'sample/outs/raw_feature_bc_matrix/', and exoTras would automatically recognize for each sample. The second way supports one sample file with abosulte path for each sample, if they are not in one directory.Andout_pathdefines the output of exoTras that is one h5ad file, named 'raw_exoTras.h5ad', with exoTras score and exosome classification in the 'obs' for all droplets, and one named 'exosomes_exoTras.h5ad' with only exosome-containing droplets.Then, we could source these recoginzed exosomes to original cell type and calculate the exosome secretion activity.celltype_exosome_number,adata_exo,adata_combined=exoTras.source_tracker(adata_exo,adata_cell,OBSsample='batch',OBScelltype='celltype')exo_activity_dat=exoTras.ESAI_celltype(adata_exo,adata_cell,OBSsample='batch',OBScelltype='celltype')In the parameters, the two adata variable represent the exosome- and cell- anndata objects. Andbatchandcelltypeare the indexes of sample names and cell types in the anndata of cells. The output ofsource_trackeris in the 'obsm' of 'adata_exo' indexed as 'source'.Further tutorials please refer tohttps://exoTras.readthedocs.io/.