Develop (#67)

* revise log info * update reference * update reference in introduction
Novartis · Nov 23, 2023 · 14b430b · 14b430b
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diff --git a/docs/Introduction.rst b/docs/Introduction.rst
@@ -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]_                        |
 +-------------------------------------------+-------------------------------------------+

diff --git a/docs/Reference.rst b/docs/Reference.rst
@@ -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>`__,

diff --git a/scar/main/_setup.py b/scar/main/_setup.py
@@ -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:
@@ -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()