MORESCA (MOdular and REproducible Single-Cell Analysis)

This repository provides a template on standardized scRNA-seq analysis using Python and the Scanpy library. All parameters of the workflow are controlled with single config file.

Usage

Setting up the environment

Clone the repository

git clone [email protected]:claassenlab/MORESCA.git

Change into the directory

cd MORESCA

Create a virtual environment using Conda with Python version >=3.10

conda create -n <envName> python=3.10

Activate the environment

conda activate <envName>

Install MORESCA using:

pip install -e .

This creates a symbolic link, making changes to the code basis instantanious.

Calling the template

Flag	Type	Description	Default
-d, --data	Path	Path to the h5ad file	data/adata_raw.h5ad
-p, --parameters	Path	Path to the config file	config.gin
-v, --verbose	Boolean	If set, prints to output	False
-f, --figures	Boolean	If set, figures will be generated	False

By default, template.py expects the data in H5AD format to be in data. The two folders figures and results are generated on the fly if they don't exist yet.

Currently, the script will perform the most common operations from doublet removal to DEG analysis of found clusters. If you want to apply ambient RNA correction beforehand, you need to run this separately.

The following example executes the template with the h5ad file example_data.h5ad, the parameter file config.gin and enables both print-statements and figures.

python template.py -d example_data.h5ad -p config.gin -v -f

Using the config.gin

By default, the used parameter file looks like this:

# config.gin
quality_control:
    doublet_removal = False
    outlier_removal = False
    min_genes = 200
    min_cells = 10
    n_genes_by_counts = None
    mt_threshold = 50
    rb_threshold = 10
    hb_threshold = 2
    remove_mt = False
    remove_rb = False
    remove_hb = False
    remove_custom_genes = None
normalization:
    method = "PFlog1pPF"
feature_selection:
    method = "seurat"
    number_features = 2000
scaling:
    apply = True
    max_value = None
pca:
    apply = True
    n_comps = 50
    use_highly_variable = True
batch_effect_correction:
    method = "harmony"
    batch_key = None
neighborhood_graph:
    n_neighbors = 15
    n_pcs = None
clustering:
    method = "leiden"
    resolution = 1.0
diff_gene_exp:
    method = "wilcoxon"
    groupby = "leiden_r1.0"
    use_raw = True
    tables = False

The following values of the parameters are currently possible

Parameter	Values
quality_control
doublet_removal	bool
doublet_removal	bool
min_genes	int, null
min_cells	int, null
mt_threshold	float, null
rb_threshold	float, null
hb_threshold	float, null
remove_mt	float, null
remove_rb	float, null
remove_hb	float, null
remove_custom_genes	list(str), null
normalization
method	log1pCP10k, log1PF, PFlog1pPF, pearson_residuals, null
feature_selection
method	seurat, seurat_v3, pearson_residuals, anti_correlation, null
number_features	int, null
scaling
apply	bool
max_value	int, float
batch_effect_correction
method	harmony, null
batch_key	Not implemented / null
neighborhood_graph
n_neighbors	int
n_pcs	int, null
clustering
method	str, null
resolution	float
diff_gene_exp
method	wilcoxon, logreg, t-test, t-test_overestim_var
groupy	str
use_raw	bool
tables	bool

Code generator

After deciding for a suitable pipeline and specific parameters, you can create a Python file which reflects the exact step in a minimal fashion.

Contributing

For contribution purposes, you should install MORESCA in dev mode:

pip install -e ".[dev]"

This additionally install flake8, Black and pylint, which we use for formatting and code style control. Please run these before you commit new code. Note: This will be made mandatory by using pre-commit hooks.