l7991-lian / stpbml

STPbml (Spatial Transcriptomics of Plasmodium berghei infected mouse liver)

This repository contains code and data required to reproduce data described in the study "Host-Pathogen Interactions in the Plasmodium-Infected Mouse Liver at Spatial and Single-Cell Resolution" currently available on bioRxiv. Larger datasets are available in a seperate zenodo repository: .

system requirements

All code can be reproduced using R and python software. We recommend using R version 4.0.5 and python 3.10.8 or higher. Moreover to reprodcue the generation of distance measurements the hepaquery package is required. Installation instructionr, requirements and dependencies for this package can be found here.

content

• data

  • annotations

    • contains GFF (General Feature Format) file for aligment of sequencing reads to joined Plasmodium berghei and Mus musculus genome using stpipeline : Mus_musculus.GRCm38.101_PlasmoDB-48_PbergheiANKA.gff.gz

    • gene annotations including corresponding Gene Ontology terms for joined Genome: 20230222_annotation_Symbol_GO.txt

    • gene annoations including gene symbols, biotype etc. for Mus musculus : mus_pb_annotation_seurat.tsv

    • gene annoations including gene symbols, biotype etc. for Plasmodium berghei: pb_annotation_seurat.tsv

  • celltype_proportions contains an output folder for each ST and Visium section with cell type proportion results of the stereoscope cell type deconvolotion and corresponding log files for the analysis.

  • cluster contains cluster information for each spot for each individual sample of the ST analysis

  • distances contains distance calculations performed using the hepaquery package and the yaml files in the yaml files folder and generated masks available at .

  • gene_shortlists contains curated gene lists (by literature research) from expression-by-distance correlation analysis for parasite distances ordered by timepoint.

• res

  • contains folder to download seurat objects from to rerun analysis and generate respective plots and data shown in the study.

• scripts

• R contains R code scripts (Notebooks in .RMD format or regulary .R scripts)

• python contains python code scripts (Notebooks in .ipynb format)