Closed decarlin closed 2 years ago
decarlin
commented
2 years ago OK, I figured this out. scplus_obj.X_EXP was a <class 'scipy.sparse._csr.csr_matrix'> where calculate_TFs_to_genes_relationships() was expecting a dense ndarray. So I solved this with
scplus_obj.X_EXP=scplus_obj.X_EXP.toarray()
You may want to support sparse matrices for the RNAseq
SeppeDeWinter
commented
2 years ago Hi Decarlin
You are right. At this moment sparse matrices aren't fully supported yet. I'll mark this issue as an enhancement.
Best,
S
cbravo93
commented
2 years ago Hi @decarlin !
It should work as well, we have this conversion step: https://github.com/aertslab/scenicplus/blob/main/src/scenicplus/TF_to_gene.py [288-297]. Anyways, happy you solved it :)! We will also add an issue to arboreto to directly allow it to accept sparse matrices.
Cheers!
C
Describe the bug In calculate_TFs_to_genes_relationships(), after initialization, throws this error:
Traceback (most recent call last): File "", line 1, in
File "/home/ubuntu/scenicplus/src/scenicplus/TF_to_gene.py", line 333, in calculate_TFs_to_genes_relationships
ex_matrix = pd.DataFrame(
File "/home/ubuntu/miniconda3/envs/scenicplus/lib/python3.8/site-packages/pandas/core/frame.py", line 737, in init
mgr = ndarray_to_mgr(
File "/home/ubuntu/miniconda3/envs/scenicplus/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 351, in ndarray_to_mgr
_check_values_indices_shape_match(values, index, columns)
File "/home/ubuntu/miniconda3/envs/scenicplus/lib/python3.8/site-packages/pandas/core/internals/construction.py", line 422, in _check_values_indices_shape_match
raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
ValueError: Shape of passed values is (25945, 1), indices imply (25945, 18892)
To Reproduce Here's the code, starting at a successfully created cistopic object
import itertools import anndata
with open('/data/scenic_demo/output/cistopic_obj.pkl', 'rb') as f: cistopic_obj = pickle.load(f)
import scanpy as sc
adata=sc.read_mtx('/data/CAREHF/multiome_rna_counts.mtx').T adata.obs=atac_metadata cell_data_raw = pd.read_csv('/data/CAREHF/multiome_samples.txt') adata.obs_names =cell_data_raw['x']
gene_names=pd.read_csv('/data/CAREHF/multiome_rna_features.txt') adata.var_names =gene_names['x']
adata=adata.T
import dill
menr = dill.load(open('/data/scenic_demo/carehf/motifs/menr.pkl', 'rb'))
from scenicplus.scenicplus_class import create_SCENICPLUS_object import numpy as np scplus_obj = create_SCENICPLUS_object( GEX_anndata = adata, cisTopic_obj = cistopic_obj, menr = menr, key_to_group_by = 'predicted.celltype_fromRNA', multi_ome_mode = True, bc_transform_func = lambda x: x+'___cisTopic' )
from scenicplus.preprocessing.filtering import *
filter_genes(scplus_obj, min_pct = 0.5) filter_regions(scplus_obj, min_pct = 0.5)
from scenicplus.cistromes import * merge_cistromes(scplus_obj)
from scenicplus.enhancer_to_gene import get_search_space, calculate_regions_to_genes_relationships, GBM_KWARGS from scenicplus.enhancer_to_gene import GBM_KWARGS
get_search_space(scplus_obj, biomart_host = 'http://www.ensembl.org', species = 'hsapiens', assembly = 'hg38', upstream = [1000, 150000], downstream = [1000, 150000])
calculate_regions_to_genes_relationships(scplus_obj, ray_n_cpu = 20, _temp_dir = tmp_dir, importance_scoring_method = 'GBM', importance_scoring_kwargs = GBM_KWARGS)
with open('/data/scenic_demo/carehf/scplus_obj.pkl', 'wb') as f: pickle.dump(scplus_obj, f)
from scenicplus.TF_to_gene import * tf_file = '/data/scenic_demo/allTFs_hg38.txt'
calculate_TFs_to_genes_relationships(scplus_obj, tf_file = tf_file, ray_n_cpu = 20, method = 'GBM', _temp_dir = tmp_dir, key= 'TF2G_adj')
Version (please complete the following information):
Additional context Perhaps this is related to creating the scenic object from a .mtx rather than AnnData object? However, prior to TF to genes inference, the scenic object looks fine:
Anyway, thanks for the work, excited to see the results...