Cell Mixer

Requirements

For this package to work you will need to install the following python3 packages:

pip3 install pandas anndata absl-py

You will also need to install the following R/Biocinductor packages

install.packages("devtools")
install.packages("argparse")
install.packages("Seurat")
install.packages("purrr")
devtools::install_github(repo = "hhoeflin/hdf5r")
devtools::install_github(repo = "mojaveazure/loomR", ref = "develop")

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SingleCellExperiment")
BiocManager::install("scater")
BiocManager::install("scran")
BiocManager::install("DropletUtils")

Use

Downloading the data

The current data comes from Zheng et al and was downloaded from the 10X website.

To download the data, simply run fetch_data.sh and specify the path to which you want to save the data to.

export DATA_PATH=/tmp/data
bash fetch_data.sh $DATA_PATH

Note that the raw data only needs to be fetched once.

Generating a mixture

The script to generate the mixtures is cell_mixer.R, it allows for standard QC steps:

• --qc_counts_mad_lower: Removing cells with low read count, filtered based on the number of MADs under the median read counts.
• --qc_feature_count_mas_lower: Removing cells with few genes expressed, filtered based on the number of MADs under the median number of genes expressed.
• --qc_mito_mad_upper: Removing cells with high number of mitochondrial reads (which is the case for dead cells), based on the number of MADs above the median number of mitochondrial RNA counts.

It also allows the select the quantity of cells of various types in the following table:

Cell type	Number of cells	Flag
CD19+ B cells	10085	--b_cells
CD8+/CD45RA+ Naive Cytotoxic T Cells	11953	--naive_cytotoxic
CD14+ monocytes	2612	--cd14_monocytes
CD4+/CD25+ Regulatory T Cells	10263	--regulatory_t
CD56+ natural killer cells	8385	--cd56_nk
CD4+ helper T cells	11213	--cd4_t_helper
CD4+/CD45RO+ Memory T Cells	10224	--memory_t
CD4+/CD45RA+/CD25- Naive T cells	10479	--naive_t

The seed for the subsampling is set by default to 1234 in order to reproduce the data sets from Duo et al, but it can be changed to generate multiple mixtures with similar cells (for studying the stability of a result under similar setups).

The cell type identity will be written in the label cell attribute.

Supported formats

This repository can currently generate data in the following formats:

• SingleCellExperiment
• Seurat
• LoomR/Loompy
• csv
• AnnData

The first four can be done directly with cell_mixer.R by specifying the --format flag.

AnnData has to be generate by first generating the data in csv, then by running the convert.py script.

Rscript cell_mixer.R \
--data_path=$DATA_PATH \
--name=mixture \
--format=csv \
--b_cells=3000 \
--naive_t=3000
python3 converter.py \
--input_csv=mixture \
--format=anndata

Adding new data

In order to add new cell types you can send a Pull Request, the files you will need to change are:

• fetch_data.sh: to download the data
• cell_mixer.R: add the appropriate flag, read the data, add the label, and add it to the mixtures. All the locations to modify have a comment to locate them.

Adding in new formats

The R formats have to be added in the cell_mixer.R script, internally it uses SingleCellExperiment which is the most commonly used format.

The python formats have to be added in converter.py.

If you want more formats to be supported please open an issue or send a pull request.