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Develop #67

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merged 4 commits into from
Nov 23, 2023
Merged

Develop #67

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de83809
revise log info
CaibinSh Jul 20, 2023
d2db2d5
Merge pull request #1 from Novartis/develop
CaibinSh Nov 23, 2023
d85cf42
update reference
CaibinSh Nov 23, 2023
166b1e5
update reference in introduction
CaibinSh Nov 23, 2023
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2 changes: 1 addition & 1 deletion docs/Introduction.rst
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Expand Up @@ -32,7 +32,7 @@ Several methods exist for modeling noise in single-cell omics data. In general,
+-------------------------------------------+-------------------------------------------+
| Background noise | Stachastic noise |
+========+===============+==================+========+===============+==================+
| CellBender [Fleming2019]_ | scVI [Lopez2018]_ |
| CellBender [Fleming2023]_ | scVI [Lopez2018]_ |
+-------------------------------------------+-------------------------------------------+
| SoupX [Young2020]_ | DCA [Eraslan2019]_ |
+-------------------------------------------+-------------------------------------------+
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6 changes: 3 additions & 3 deletions docs/Reference.rst
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Expand Up @@ -9,9 +9,9 @@ Reference
`Single-cell RNA-seq denoising using a deep count autoencoder <http://dx.doi.org/10.1038/s41467-018-07931-2>`__,
Nature Communications.

.. [Fleming2019] Fleming *et al.* (2019),
`CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets <https://doi.org/10.1101/791699>`__,
bioRxiv.
.. [Fleming2023] Fleming *et al.* (2023),
`Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender <https://doi.org/10.1038/s41592-023-01943-7>`__,
Nature Methods.

.. [Gayoso2021] Gayoso *et al.* (2021),
`Joint probabilistic modeling of single-cell multi-omic data with totalVI <http://dx.doi.org/10.1038/s41592-020-01050-x>`__,
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7 changes: 4 additions & 3 deletions scar/main/_setup.py
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Expand Up @@ -104,8 +104,12 @@ def setup_anndata(

if sample is not None:
sample = int(sample)
setup_logger.info(
f"Randomly sample {sample:d} droplets from {raw_adata.shape[0]:d} droplets."
)
else:
sample = raw_adata.shape[0]
setup_logger.info(f"Use all {sample:d} droplets.")

# check n_batch
if n_batch is None:
Expand All @@ -124,9 +128,6 @@ def setup_anndata(
)
raw_adata = raw_adata[idx]

setup_logger.info(
f"Randomly sample {sample:d} droplets from {raw_adata.shape[0]:d} droplets."
)
# initial estimation of ambient profile, will be update
ambient_prof = raw_adata.X.sum(axis=0) / raw_adata.X.sum()

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