A multiplet removal tool for processing cell hashing data
A Gaussian Mixture Model based software for processing cell hashing data.
Below shows an example classification result. Orange dots are multi-sample multiplets.
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.
- Remove cell-hashing-identifiable multiplets from the dataset.
- Estimate the fraction of cell-hashing-unidentifiable multiplets in the remaining dataset (the RSSM value).
- 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.
Hongyi Xin, Qi Yan, Yale Jiang, Jiadi Luo, Carla Erb, Richard Duerr, Kong Chen* and Wei Chen*
Hongyi Xin email@example.com
GMM-Demux requires python3 (>3.5).
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 .
- Post installation processes
If this is the first time you install a python3 software through pip, make sure you add the pip binary folder to your
Typically, the pip binary folder is located at
To temporarily add the pip binary folder, run the following command:
To permenantly add the pip library folder to your
PATH variable, append the following line to your
The source code of GMM-Demux is supplied in the
An example cell hashing dataset is also provided, located in the
Once installed, the github folder is no longer needed. Instead, GMM-Demux is directly accessible with the
GMM-demux <cell_hashing_path> <HTO_names> <estimated_cell_num>
<HTO_names> is a list of strings separated by ',' without whitespace.
For example, there are four HTO tags in the example cell hashing dataset supplied in this repository.
They are HTO_1, HTO_2, HTO_3, HTO_4. The
<HTO_names> variable therefore is
MSM-free droplets are stored in folder GMM_Demux_mtx under the current directory by default.
The output path can also be specified through the
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. The feature.tsv file of the example cell hashing dataset is shown below.
- -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.
- CellRanger MSM-free drops, in MTX format. Compatible with CellRanger 3.0.
- Dataset summary. An example summary is shown below.
- 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.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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