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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) (3.10.12, R 4.2.3, JupyterLab 3.6.2, RStudio 2022.07.0-548) starter environment and add the following packages to the appropriate package manager:

mamba

r-irkernel=1.3.2

r-presto=1.0.0

r-rcpp=1.0.11

r-seurat=5.1.0

R (CRAN)

markdown=2.0

Bioconductor

BiocVersion=3.16.0

limma=3.54.2

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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