unravel.allen_institute.abca.scRNA_seq.filter module#

Use abca_scRNAseq_filter or rna_filter from UNRAVEL to filter ABCA scRNA-seq cells based on columns and values in the cell metadata.

Notes

  • region_of_interest_acronym: ACA, AI, AUD, AUD-TEa-PERI-ECT, CB, CTXsp, ENT, HIP, HY, LSX, MB, MO-FRP, MOp, MY, OLF, P, PAL, PL-ILA-ORB, RHP, RSP, sAMY, SS-GU-VISC, SSp, STRd, STRv, TEa-PERI-ECT, TH, VIS, VIS-PTLp

  • mouse columns: cell_label, feature_matrix_label, region_of_interest_acronym, x, y, cluster_alias, neurotransmitter, class, subclass, supertype, cluster, …, <genes>

  • human columns: cell_label, feature_matrix_label, region_of_interest_acronym, x, y, cluster_alias, neurotransmitter, supercluster, cluster, subcluster, …, <genes>

  • For multiple columns and values, the number of columns must match the number of values.

Next steps:

  • abca_sunburst_expression

Usage:#

abca_scRNAseq_filter -i path/expression.csv [-c column1 column2 … -val value1 value2 …] [-s mouse | human] [-ct Neurons | Nonneurons] [-split split_column] [-o output] [-v]

unravel.allen_institute.abca.scRNA_seq.filter.parse_args()[source]#
unravel.allen_institute.abca.scRNA_seq.filter.filter_by_cell_type(cell_df, species, cell_type)[source]#

Filter cells by species and cell type (neurons vs nonneurons).

Parameters:

  • cell_df (DataFrame) –

  • species (str) –

  • cell_type (str | None) –

Return type:

DataFrame

unravel.allen_institute.abca.scRNA_seq.filter.main()[source]#