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A scalable method to infer fate specific gene regulatory network from single cell gene expression data

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NetID_package

A scalable method to infer fate specific gene regulatory network from single cell gene expression data

NetID

Tutorial

vignette("NetID")

Installation

Basic installation

Create conda environment (recommand but not necessary)

conda create --name NetID python=3.10 r-essentials r-base=4.2.0
conda activate NetID # if it encounter the error, run 'source activate' ahead of this code

Install devtools and geosketch

conda install -c conda-forge r-devtools
pip install geosketch

install NetID

install.packages('NetID_0.1.0.tar.gz', repos=NULL, type='source')

Advance installation

install cellrank and palantir to realize lineage-specific GRN prediction

To speed up installation, user could use conda install mamba at first, then use mamba to install other modules

conda install mamba -c conda-forge
mamba install -c bioconda -c conda-forge cellrank-krylov
## or could use conda to install
## conda install -c bioconda -c conda-forge cellrank-krylov
pip install numpy==1.23.5 palantir

install summa to output global GRN

devtools::install_github("learn-ensemble/R-SUMMA")

install cytotrace and scent to determine the root cell

devtools::install_github("aet21/SCENT")

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A scalable method to infer fate specific gene regulatory network from single cell gene expression data

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