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A predictive framework to identify signaling genes active in cell-cell interaction

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Introduction

MESSI (Mixture of Experts for Spatial Signaling genes Identification) is a predictive framework to identify signaling genes active in cell-cell interaction. It jointly models gene interactions within and between cells, using the recently developed spatial single cell expression data. MESSI combines the ability to subdivide cell types with multi-task learning to accurately infer the expression of a set of response genes based on signaling genes and to provide useful biological insights about key signaling genes and cell subtypes.

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Prerequisites

  • Python >= 3.6
  • Python side-packages:
    -- scikit-learn >= 0.22.1
    -- scipy >= 1.3.0
    -- numpy >= 1.16.3
    -- pandas >= 0.25.3

Installation

Install within a virtual environment

It is recommended to use a virtural environment/pacakges manager such as Anaconda. After successfully installing Anaconda/Miniconda, create an environment by following:

conda create -n myenv python=3.6

You can then install and run the package in the virtual environment. Activate the virtural environment by:

conda activate myenv

Make sure you have pip installed in your environment. You may check by

conda list

If not installed, then:

conda install pip

Then install MESSI, together with all its dependencies by:

pip install --upgrade  https://github.com/doraadong/MESSI/tarball/master

Not using virtural environment

If you prefer not to use a virtual envrionment, then you may install MESSI and its dependencies by (may need to use sudo):

pip3 install --upgrade  https://github.com/doraadong/MESSI/tarball/master

You may find where the package is installed by:

pip show messi

Command-line

Download helper data & expression data and convert them to required formats

In terminal, type (arguments are taken for example):

readyData.py -i ../input/ -d merfish

The usage of this script is listed as follows:

usage: readyData.py [-h] -i INPUT -d DATATYPE

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        string, path to the folder to save the expression
                        data, default 'input/'
  -d DATATYPE, --dataType DATATYPE
                        string, type of expression data, default 'merfish'

Train (and test) MESSI model

Run MESSI by (arguments are taken for example):

messi -i ../input/ -o ../output/ -d merfish -g Female -b Parenting -c Excitatory -m train -c1 1 -c2 8 -e 5

The usage of this file is listed as follows:

usage: messi [-h] -i INPUT [-ilr INPUT_LR] -o OUTPUT -d
             {merfish,merfish_cell_line,starmap} -g GENDER -b BEHAVIOR -c
             CELLTYPE -m MODE [-c1 NUMLEVEL1] [-c2 NUMLEVEL2] [-e EPOCHS]
             [-gs GRID_SEARCH] [-ns N_SETS] [-r NUMREPLICATES] [-p PREPROCESS]
             [-tr TOPKRESPONSES] [-ts TOPKSIGNALS]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        string, path to the input folder with the expression
                        data, default 'input/'
  -ilr INPUT_LR, --input_lr INPUT_LR
                        string, optional, path to the input folder with the
                        ligands and receptors list, default 'input/'
  -o OUTPUT, --output OUTPUT
                        string, path to the output folder, default 'output/'
  -d {merfish,merfish_cell_line,starmap}, --dataType {merfish,merfish_cell_line,starmap}
                        string, type of expression data, 'merfish' for MERFISH
                        hypothalamus data, 'merfish_cell_line' for MERFISH U-2
                        OS cells, 'starmap' for 'STARmap mPFC cells';default
                        'merfish'
  -g GENDER, --gender GENDER
                        string, gender of input animal sample, default
                        'Female', put 'na' if not available
  -b BEHAVIOR, --behavior BEHAVIOR
                        string, behavior of input animal sample, default
                        'Naive', put 'na' if not available
  -c CELLTYPE, --cellType CELLTYPE
                        string, cell type that will be built a model for, use
                        \ for white-space, e.g. 'OD\ Mature\ 2', default
                        'Excitatory'
  -m MODE, --mode MODE  string, any of 'train', 'CV'; if 'train', then all
                        data will be used for training and output a pickle
                        file for learned parameters; if 'CV', then cross-
                        validation will be conducted each time with an
                        animal/sample left out and each CV run output a pickle
                        file and prediction result, default 'train'
  -c1 NUMLEVEL1, --numLevel1 NUMLEVEL1
                        integer, optional, number of classes at level 1,
                        number of experts = number of classes at level 1 x
                        number of classes at level 2, default 1
  -c2 NUMLEVEL2, --numLevel2 NUMLEVEL2
                        integer, optional, number of classes at level 2,
                        default 5
  -e EPOCHS, --epochs EPOCHS
                        integer, optional, number of epochs to train MESSI,
                        default 20
  -gs GRID_SEARCH, --grid_search GRID_SEARCH
                        boolean, optional, if conduct grid search for hyper-
                        parameters, default False
  -ns N_SETS, --n_sets N_SETS
                        integer, optional, number of CV sets for grid search,
                        default 3
  -r NUMREPLICATES, --numReplicates NUMREPLICATES
                        integer, optional, number of times to run with same
                        set of parameters, default 1
  -p PREPROCESS, --preprocess PREPROCESS
                        string, optional, the way to include neighborhood
                        information; neighbor_cat: include by concatenating
                        them to the cell own features; neighbor_sum: include
                        by addinding to the cell own features; anything
                        without 'neighbor': no neighborhood information will
                        be used as features; 'baseline': only baseline
                        features; default 'neighbor_cat'
  -tr TOPKRESPONSES, --topKResponses TOPKRESPONSES
                        integer, optional, number of top dispersed responses
                        genes to model,default None (to include all response
                        genes)
  -ts TOPKSIGNALS, --topKSignals TOPKSIGNALS
                        integer, optional, number of top dispersed signalling
                        genes to use as features, default None (to include all
                        signalling genes)

Tutorials

See tutorials/MESSI for MERFISH hypothalamus, for a detailed intro on how to

  • Train and test a MESSI model
  • Analyze the model parameters to infer cell subtypes differ in signaling genes
  • Train and test the data with other model configurations

We also prepared tutorials/results reprudction to reproduce MESSI's results shown in our manuscript.

Updates-log

  • 2-20-2022:
    -- Uploaded the jupyter notebook for reproducing MESSI's results shown in the manuscript

Learn-more

Check our paper published in Bioinformatics for more information. The pre-print version is available at biorxiv.

Credits

The software is an implementation of the method MESSI, jointly developed by Dongshunyi "Dora" Li, Jun Ding and Ziv Bar-Joseph from System Biology Group @ Carnegie Mellon University.

Contacts

  • dongshul at andrew.cmu.edu

License

This project is licensed under the MIT License - see the LICENSE file for details

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