Usage

General command line usage

Flag	Type	Description	Default
-d, –data	Path	Path to the h5ad file.	data/adata_raw.h5ad
-o, –output	Path	Path to the output folder for the processed data.	results
-p, –parameters	Path	Path to the config file.	config.gin
-v, –verbose	Flag	Generate verbose logs.

By default, moresca expects the data in H5AD format to be in data. The output directory as well as figure folders specified in the config 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 pipeline with the h5ad file example_data.h5ad and the parameter file config.gin, saving the output in the folder results.

moresca -d example_data.h5ad -o results -p config.gin

Using the config.gin

By default, the used parameter file looks like this:

# config.gin
quality_control:
    apply = True
    doublet_removal = True
    outlier_removal = True
    min_genes = 200
    min_counts = None
    max_counts = None
    min_cells = 10
    max_genes = None
    mt_threshold = 15
    rb_threshold = 10
    hb_threshold = 1
    figures = "figures/"
    pre_qc_plots = True
    post_qc_plots = True

normalization:
    apply = True
    method = "log1pPF"
    remove_mt = False
    remove_rb = False
    remove_hb = False

feature_selection:
    apply = True
    species = "hsapiens"
    method = "seurat_v3"
    number_features = 2000

scaling:
    apply = True
    max_value = None

pca:
    apply = True
    n_comps = 100
    use_highly_variable = True

batch_effect_correction:
    apply = False
    method = "harmony"
    batch_key = None

neighborhood_graph:
    apply = True
    n_neighbors = 30
    n_pcs = None
    metric = "cosine"

clustering:
    apply = True
    method = "leiden"
    resolution = 1.0

diff_gene_exp:
    apply = True
    method = "wilcoxon"
    groupby = "leiden_r1.0"
    use_raw = False
    layer = "unscaled"
    tables = None

umap:
    apply = True

plotting:
    apply = True
    umap = True
    path = "figures/"

The following values of the parameters are currently possible

Note

The parameter species is only used for the feature selection based on anti-correlation. For available values check here. It defaults to hsapiens.

Parameter	Values	Description
quality_control
apply	bool	Whether to apply the quality control steps or not.
doublet_removal	bool	Whether to perform doublet removal or not.
outlier_removal	bool	Whether to remove outliers or not.
min_genes	int, float, bool, None	The minimum number of genes required for a cell to pass quality control.
min_counts	int, float, bool, None	The minimum total counts required for a cell to pass quality control.
max_counts	int, float, bool, None	The maximum total counts allowed for a cell to pass quality control.
min_cells	int, float, bool, None	The minimum number of cells required for a gene to pass quality control.
max_genes	int, float, str, bool, None	The maximum number of genes allowed for a cell to pass quality control.
mt_threshold	int, float, str, bool, None	The threshold for the percentage of counts in mitochondrial genes (maximum).
rb_threshold	int, float, str, bool, None	The threshold for the percentage of counts in ribosomal genes (minimum).
hb_threshold	int, float, str, bool, None	The threshold for the percentage of counts in hemoglobin genes (maximum).
figures	str, Path, None	The path to the output directory for the quality control plots.
pre_qc_plots	bool, None	Whether to generate plots of QC covariates before quality control or not.
post_qc_plots	bool, None	Whether to generate plots of QC covariates after quality control or not.
normalization
apply	bool	Whether to apply the normalization steps or not.
method	log1pCP10k, log1pPF, PFlog1pPF, analytical_pearson, None, False	The normalization method to use.
remove_mt	bool, None	Whether to remove mitochondrial genes or not.
remove_rb	bool, None	Whether to remove ribosomal genes or not.
remove_hb	bool, None	Whether to remove hemoglobin genes or not.
feature_selection
apply	bool	Whether to apply the feature selection steps or not.
method	seurat, seurat_v3, analytical_pearson, anti_correlation, triku, hotspot, None, False	The feature selection method to use.
species	str	Species of the data. Only used if feature_selection=anti_correlation
number_features	int, None	The number of top features to select (only applicable for certain methods).
scaling
apply	bool	Whether to apply the scaling step or not.
max_value	int, float, None	The maximum value to which the data will be scaled. If None, the data will be scaled to unit variance.
pca
apply	bool	Whether to apply the PCA or not.
n_comps	int, float	The number of principal components to compute. A float is interpreted as the proportion of the total variance to retain.
use_highly_variable	bool	Whether to use highly variable genes for PCA computation.
batch_effect_correction
apply	bool	Whether to apply the batch effect correction or not.
method	harmony, None, False	The batch effect correction method to use.
batch_key	str	The key in adata.obs that identifies the batches.
neighborhood_graph
apply	bool	Whether to compute the neighborhood graph or not.
n_neighbors	int	The number of neighbors to consider for each cell.
n_pcs	int, None	The number of principal components to use for the computation.
metric	str	The distance metric to use for computing the neighborhood graph.
clustering
apply	bool	Whether to perform clustering or not.
method	leiden, phenograph, None, False	The clustering method to use.
resolution	float, int, list, tuple, auto	The resolution parameter for the clustering method. Can be a single value or a list of values.
diff_gene_exp
apply	bool	Whether to perform differential gene expression analysis or not.
method	wilcoxon, t-test, logreg, t-test_overestim_var	The differential gene expression analysis method to use.
groupby	str	The key in adata.obs that identifies the groups for comparison.
use_raw	bool, None	Whether to use the raw gene expression data or not.
layer	str, None	The layer in adata.layers to use for the differential gene expression analysis.
corr_method	benjamini-hochberg, bonferroni	The method to use for multiple testing correction.
tables	str, Path, None	The path to the output directory for the differential expression tables.
umap
apply	bool	Whether to run UMAP or not.
plotting
apply	bool	Whether to create plots or not.
umap	bool	Whether to plot the UMAP or not.
path	str, Path	The path to the output directory for the plots.