scProjection

Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection. Refer to our paper:https://www.nature.com/articles/s41467-023-40744-6

Tutorials

First follow the install instructions below, at the bottom of the page, before following the tutorials.

Tutorial 1: Deconvolution of CellBench mixtures

Tutorial 2: Deconvolution of spatial MERFISH data

Tutorial 3: Projection of pseudo bulk data

Tutorial 4: Imputation of gene expression patterns of spatial osmFISH data

Install scProjection

pip3 install scProjection

The install time should be less than 30 min.

Package requirements

scProjection requires: Python 3. This is a guide to installing python on different operating systems.

(Python)

All platforms:

1. Download install binaries for Python 3 here

Alternative (On Windows):

1. Download Python 3
2. Make sure pip is included in the installation.

Alternative (On Ubuntu):

1. sudo apt update
2. sudo apt install python3-dev python3-pip

Alternative (On MacOS):

1. /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
2. export PATH="/usr/local/bin:/usr/local/sbin:$PATH"
3. brew update
4. brew install python # Python 3

Setup of virtualenv

scProjection also requires: tensorflow, tensorflow-probability, sklearn and numpy. It is generally easier to setup the dependencies using a virtual environment which can be done as follows:

## Create the virtual environment
virtualenv -p python3 pyvTf2

## Launch the virtual environment
source ./pyvTf2/bin/activate

## Setup dependencies
pip3 install tensorflow
pip3 install tensorflow-probability
pip3 install scikit-learn
pip3 install numpy

## Install scProjection
pip3 install scProjection

Updates

(3/16/2023) More tutorials have been added.

(5/23/2022) Codebase from publication made public. Need to improve user interface with method.

(11/9/2022) Added more tutorials with examples running scProjection in both R and Python