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Bugfix: #371 #372

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Bugfix: #371 #372

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0589d31
Fix: check if mat is csc_matrix and return dense representation in ca…
Nov 6, 2024
cd868c8
Bugfix: sns.jointplot requires arguments to be passed in using keywords
Nov 6, 2024
0326e06
using cache=True within the scanpy read function can cause issues whe…
Nov 7, 2024
285c075
Add whether to read from cache or source as additional parameter to r…
Nov 8, 2024
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9 changes: 6 additions & 3 deletions besca/Import/_read.py
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@bhoellbacher I think we want to keep the cache behavior in Besca, but not in the pipeline. I would suggest to add "cache" as an additional parameter to the "read_mtx" function, and change this parameter in the pipeline, but keep the default in Besca as True. What do you think?

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Sounds good.
Let me know what you think of the drafted code changes.

Original file line number Diff line number Diff line change
Expand Up @@ -107,7 +107,7 @@ def assert_adata(adata: AnnData, attempFix=True):


def read_mtx(
filepath, annotation=True, use_genes="SYMBOL", species="human", citeseq=None
filepath, annotation=True, use_genes="SYMBOL", species="human", citeseq=None, read_cache=True
):
"""Read matrix.mtx, genes.tsv, barcodes.tsv to AnnData object.
By specifiying an input folder this function reads the contained matrix.mtx,
Expand All @@ -129,6 +129,9 @@ def read_mtx(
citeseq: 'gex_only' or 'citeseq_only' or False or None | default = None
string indicating if only gene expression values (gex_only) or only protein
expression values ('citeseq_only') or everything is read if None is specified
read_cache: `bool` (default=True)
boolian identifier if scanpy should read the AnnData object from fast h5ad
cache or from source

Returns
-------
Expand All @@ -138,7 +141,7 @@ def read_mtx(
if gzfiles == "gz":
print("reading matrix.mtx.gz")
adata = read(
os.path.join(filepath, "matrix.mtx.gz"), cache=True
os.path.join(filepath, "matrix.mtx.gz"), cache=read_cache
).T # transpose the data
print("adding cell barcodes")
adata.obs_names = pd.read_csv(
Expand All @@ -155,7 +158,7 @@ def read_mtx(
else:
print("reading matrix.mtx")
adata = read(
os.path.join(filepath, "matrix.mtx"), cache=True
os.path.join(filepath, "matrix.mtx"), cache=read_cache
).T # transpose the data
print("adding cell barcodes")
adata.obs_names = pd.read_csv(
Expand Down
4 changes: 4 additions & 0 deletions besca/pp/_normalization.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import numpy as np
from scipy.sparse.csr import csr_matrix
from scipy.sparse._csc import csc_matrix
from anndata._core.views import SparseCSRView

def closure(mat):
Expand Down Expand Up @@ -180,6 +181,9 @@ def normalize_geometric(adata):
# need to add a catch for newly encountered datatype
elif type(X) == SparseCSRView:
X = X.todense()
# need to add a catch for new sparse matrix datatype
elif type(X) == csc_matrix:
X = X.todense()

# ensure that X is an array otherwise this will cause type issue with multiplicative replacement function
X = np.array(X)
Expand Down
2 changes: 1 addition & 1 deletion workbooks/standard_workflow_besca2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -760,7 +760,7 @@
" n_prots = len(adata_prot.var_names)\n",
" percent_top = (int(round(0.01*n_prots, 0)) if int(round(0.01*n_prots, 0)) >= 1 else 1, int(round(0.1*n_prots, 0)), int(round(0.25*n_prots, 0)))\n",
" qc_adata = sc.pp.calculate_qc_metrics(adata_prot, percent_top=percent_top, var_type=\"antibodies\", inplace=False)\n",
" fig = sns.jointplot(\"log1p_total_counts\", \"n_antibodies_by_counts\", qc_adata[0], kind=\"hex\", norm=mpl.colors.LogNorm())\n",
" fig = sns.jointplot(x=\"log1p_total_counts\", y=\"n_antibodies_by_counts\", data=qc_adata[0], kind=\"hex\", norm=mpl.colors.LogNorm())\n",
" fig.savefig(os.path.join(results_folder_citeseq, 'citeseq', 'figures', 'CITESEQ_QC_plot.png'))\n",
" \n",
" #generate overview of n_counts\n",
Expand Down