GMM-Demux 0.0.4

A multiplet removal tool for processing cell hashing data

GMM-Demux

A Gaussian Mixture Model based software for processing cell hashing data.

Blow shows an example classification result. Orange dots are multi-sample multiplets.

Description

GMM-Demux removes Multi-Sample-Multiplets (MSMs) in a cell hashing dataset and estimates the fraction of Same-Sample-Multiplets (SSMs) and singlets in the remaining dataset.

Features

• Remove cell-hashing-identifiable multiplets from the dataset.
• Estimate the fraction of cell-hashing-unidentifiable multiplets in the remaining dataset (the RSSM value).

Example Dataset

• An example cell hashing data is provided in the example_input folder. It contains the per drop HTO count matrix of a 4-sample cell hashing library prep.

Authors

Hongyi Xin, Qi Yan, Yale Jiang, Jiadi Luo, Carla Erb, Richard Duerr, Kong Chen* and Wei Chen*

Maintainer

Hongyi Xin [e-mail]

Requirement

GMM-Demux requires python3 (>3.5).

Install

GMM-Demux can be directly installed from PyPi. Or it can be built and installed locally.

• Install GMM-Demux from PyPi.

pip3 install --user GMM_Demux

If choose to install from PyPi, it is unnecessary to download GMM-Demux from github. However, we still recommend downloading the example dataset to try out GMM-Demux.

• Install GMM-Demux locally using setuptools and pip3.

cd <GMM-Demux dir>
python3 setup.py sdist bdist_wheel
pip3 install --user . 

Usage

Once installed, the github folder is no longer needed. Instead, GMM-Demux is directly accessible with the GMM-demux command.

GMM-demux <cell_hashing_path> <HTO_names> <estimated_cell_num>

MSM-free droplets are stored in folder GMM_Demux_mtx by default.

Example Usage

An example cell hashing data is provided in example_input. <HTO_names> can be obtained from the features.tsv file.

GMM-demux example_input/outs/filtered_feature_bc_matrix HTO_1,HTO_2,HTO_3,HTO_4 35685

<HTO_names> are obtained from the features.tsv file. An example is shown below.

Optional Arguments

• -h: show help information.
• -f FULL, --full FULL Generate the full classification report. Require a path argument.
• -s SIMPLIFIED, --simplified SIMPLIFIED Generate the simplified classification report. Require a path argument.
• -o OUTPUT, --output OUTPUT Specify the folder to store the result. Require a path argument.
• -r REPORT, --report REPORT Specify the file to store summary report. Require a file argument.

Output Values

• CellRanger MSM-free drops, in MTX format. Compatible with CellRanger 3.0.
• Dataset summary. An example summary is shown below.

Output Explanation

• MSM denotes the percentage of identified and removed multiplets among all droplets.
• SSM denotes the percentage of unidentifiable multiplets among all droplets.
• RSSM denotes the percentage of multiplets among the output droplets (after removing identifiable multiplets). RSSM measures the quality of the cell hashing dataset.

Online Cell Hashing Experiment Planner

A GMM-Demux based online cell hashing experiment planner is publically accessible at here.