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Introduction Version

Scanpy Data filtration

TASK
Typing name :  TASK.gws_scomix.ScanpyDataFiltration Brick :  gws_scomix

this task permit to the user to eliminate low quality cells

Based on output files analysis generated using "Load count matrices" task , we can proceed to data filtration and low quality cells elimination.

Input

combined_matrices
this file contain h5ad file generated by load count matrices task
File

Output

Combined_filtred_data
This file contain cleaned matrices
File
Metadata associated to each cell
This table stores metadata associated with each cell such as mitochondrial content , number of counts etc
Table
Statistical table
This file contain per sample data
Table
Statistical table
This file contain the min_max values in term of mitochondrial and ribosomal percentage , genes per count and total counts after filtration
Table

Configuration

min_cells

Optional

The minimum number of cells required for a gene to be considered in the filtering process. => genes filtering

Type : intDefault value : 10

min_genes

Optional

The minimum number of genes required for a cell to be considered in the filtering process => cells filtering

Type : intDefault value : 200

min_n_genes_by_counts

Optional

The minimum number of genes with at least 1 count in a cell. Calculated for all cells

Type : intDefault value : 600

max_n_genes_by_counts

Optional

The maximum number of genes with at least 1 count in a cell. Calculated for all cells

Type : intDefault value : 2000

max_pct_counts_mt

Optional

The maximum percentage of counts originating from mitochondrial genes that a cell can have

Type : intDefault value : 20

max_pct_counts_ribo

Optional

The maximum percentage of counts originating from ribosomal genes that a cell can have

Type : intDefault value : 2