flying-sheep
commented
6 days ago Good point! I think the idea might be that they’re actually different versions of a gene that just have been mapped to the same gene name (and have e.g. different ENSEMBL IDs)
But of course that depends on how the data was generated, and your method might be more appropriate for some datasets.
Example from the tutorial:
import pooch
import scanpy as sc
EXAMPLE_DATA = pooch.create(
path=pooch.os_cache("scverse_tutorials"),
base_url="doi:10.6084/m9.figshare.22716739.v1/",
)
EXAMPLE_DATA.load_registry_from_doi()
samples = {
"s1d1": "s1d1_filtered_feature_bc_matrix.h5",
"s1d3": "s1d3_filtered_feature_bc_matrix.h5",
}
adatas = {}
for sample_id, filename in samples.items():
path = EXAMPLE_DATA.fetch(filename)
sample_adata = sc.read_10x_h5(path)
#sample_adata.var_names_make_unique()
adatas[sample_id] = sample_adata
adatas["s1d1"].var[adatas["s1d1"].var.index.duplicated(keep=False)]
gene_ids feature_types genome pattern read sequence TBCE ENSG00000285053 Gene Expression GRCh38 TBCE ENSG00000284770 Gene Expression GRCh38 LINC01238 ENSG00000237940 Gene Expression GRCh38 LINC01238 ENSG00000261186 Gene Expression GRCh38 CYB561D2 ENSG00000114395 Gene Expression GRCh38 CYB561D2 ENSG00000271858 Gene Expression GRCh38 MATR3 ENSG00000280987 Gene Expression GRCh38 MATR3 ENSG00000015479 Gene Expression GRCh38 LINC01505 ENSG00000234323 Gene Expression GRCh38 LINC01505 ENSG00000234229 Gene Expression GRCh38 HSPA14 ENSG00000284024 Gene Expression GRCh38 HSPA14 ENSG00000187522 Gene Expression GRCh38 GOLGA8M ENSG00000188626 Gene Expression GRCh38 GOLGA8M ENSG00000261480 Gene Expression GRCh38 GGT1 ENSG00000286070 Gene Expression GRCh38 GGT1 ENSG00000100031 Gene Expression GRCh38 ARMCX5-GPRASP2 ENSG00000271147 Gene Expression GRCh38 ARMCX5-GPRASP2 ENSG00000286237 Gene Expression GRCh38 TMSB15B ENSG00000158427 Gene Expression GRCh38 TMSB15B ENSG00000269226 Gene Expression GRCh38
VladimirShitov
ShreyParikh07
dekan-aleksandr
What kind of feature would you like to request?
Additional function parameters / changed functionality / changed defaults?
Please describe your wishes
Hi scanpy team! I have a rather conceptual question. Since the beginning of the single-cell analysis era, one of the standard steps in preprocessing is making the feature names unique (e.g. with
adata.var_names_make_unique()) by adding suffixes to their names. It is recommended in the scanpy tutorial and in the best practices book. It is clear how identical feature names make the following data processing challenging, but why are we handling it this way? Wouldn't it make more sense to aggregate features with identical names, summing the counts? From the biological point of view, the same gene name means the same feature, so why split it into several features and corrupt their names?