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Single Cell Analysis with Seurat

This repository serves as the starting point for creating a GitHub-integrated Capsule for preprocessing and exploratory analysis of single cell data from four human innate T cells.

Runtime environment

Choose the Python and R (Jupyterlab/RStudio) (python3.10.12-R4.2.3-IRkernel-ubuntu22.04) starter environment and add the following packages to the mamba package manager with the bioconda channel:

bioconductor-biocversion=3.20.0

bioconductor-limma=3.62.1

r-biocmanager=1.30.25

r-irkernel=1.3.2

r-markdown=1.13

r-rcpp=1.0.14

r-seurat=5.1.0

To the R(GitHub) package manager, add:

immunogenomics/presto

Required Data

Running this Rmd requires a data asset which can be created from the following S3 storage:

S3 bucket: codeocean-public-data

Path: example_datasets/innate_Tcell_transcriptome/

Data Asset Metadata

Title: Innate T Cell RNA-Seq Counts

Folder name: counts

Description: Low input RNA-seq and single-cell RNA-seq data from four human innate T cell. Initially retrieved from NCBI: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124731

Note: do not import this repository directly. Instead, create a new repo from this template.

Notebook based on the original Scanpy Tutorial.

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