change release to 0.2.4

docs/source/Tutorials/FormatConversion.rst

@@ -8,15 +8,42 @@ The io module provides the function :mod:`stereo.io.stereo_to_anndata` to conver
 corresponding h5ad file(.h5ad).
 
 parameters
-~~~~~~~~~~
+~~~~~~~~~~~~~~~
 
 :param data: StereoExpData object
-:param flavor: 'scanpy' or 'seurat'. If you want to converted the output_h5ad into the rds file, set flavor='seurat'.
+:param flavor: 'scanpy' or 'seurat'. If you want to convert the output_h5ad into the rds file, set flavor='seurat'.
 :param sample_id: name of sample. This will be set as 'orig.ident' in adata.obs.
 :param reindex: whether to reindex the cell. The new index looks like "{sample_id}:{position_x}_{position_y}" format.
 :param output: Default is None. If None, it will not generate a h5ad file.
 :return: Anndata object
 
+If you want to use sctransform to get the normalizetion result and convert the output_h5ad into the rds file,
+you need to save raw data before you use sctransform. Otherwise, it may raise errors during conversion.
+Example like this:
+
+.. code:: python
+
+    import warnings
+    warnings.filterwarnings('ignore')
+    import stereo as st
+
+    # read the gef file
+    mouse_data_path = './stereomics.h5'
+    data = st.io.read_gef(file_path=mouse_data_path, bin_size=50)
+    data.tl.cal_qc()
+
+    # Must save raw data before sctransform.
+    data.tl.raw_checkpoint()
+
+    # Be carefule with sctransform before the conversion.
+    data.tl.sctransform(res_key='sctransform', inplace=True)
+
+    # You can use other functions as you want, like pca and so on.
+    data.tl.pca(use_highly_genes=False, n_pcs=30, res_key='pca')
+
+    # conversion
+    adata = st.io.stereo_to_anndata(data,flavor='seurat',output='out.h5ad')
+
 h5ad to rds file
 ----------------------------------
 The output h5ad could be converted into rds file by `annh5ad2rds.R <https://github.com/BGIResearch/stereopy/blob/dev/docs/source/_static/annh5ad2rds.R>`_.

docs/source/release_note.rst

@@ -3,6 +3,12 @@ Release Notes
 
 .. role:: small
 
+Version 0.2.4
+------------------
+0.2.4 :2022-01-19
+~~~~~~~~~~~~~~~~~~~~~
+1.Fix bug of tar package
+
 Version 0.2.3
 -----------
 0.2.3 :2022-01-17

setup.py

@@ -15,7 +15,7 @@
 
 setup(
     name='stereopy',
-    version='0.2.3',
+    version='0.2.4',
     setup_requires=['setuptools_scm', 'numpy', 'panel', 'pytest', 'quilt3', 'scipy', 'phenograph'],
     description='Spatial transcriptomic analysis in python.',
     long_description=Path('README.md').read_text('utf-8'),

stereo/io/reader.py

@@ -270,12 +270,9 @@ def stereo_to_anndata(data: StereoExpData, flavor='scanpy', sample_id="sample",
             elif key == 'sct':
                 res_key = data.tl.key_record[key][-1]
                 #adata.uns[res_key] = {}
-                if flavor == 'seurat' and len(data.tl.key_record['neighbors']) == 10:
-                    continue
-                else:
-                    logger.info(f"Adding data.tl.result['{res_key}'] in adata.uns['sct_'] .")
-                    adata.uns['sct_counts'] = csr_matrix(data.tl.result[res_key][1]['filtered_corrected_counts'])
-                    adata.uns['sct_data'] = csr_matrix(data.tl.result[res_key][1]['filtered_normalized_counts'])
+                logger.info(f"Adding data.tl.result['{res_key}'] in adata.uns['sct_'] .")
+                adata.uns['sct_counts'] = csr_matrix(data.tl.result[res_key][1]['filtered_corrected_counts'])
+                adata.uns['sct_data'] = csr_matrix(data.tl.result[res_key][1]['filtered_normalized_counts'])
 
             elif key in ['pca', 'umap', 'tsne']:
                 # pca :we do not keep variance and PCs(for varm which will be into feature.finding in pca of seurat.)

stereo/preprocess/sc_transform.py

@@ -67,6 +67,6 @@ def sc_transform(
     data.exp_matrix = residuals.values
     gene_index = np.isin(data.gene_names, np.array(residuals.columns))
     data.genes = data.genes.sub_set(gene_index)
-    vst_out['filtered_corrected_counts'] = corrected_counts.loc[:, gene_index]
+    vst_out['filtered_corrected_counts'] = corrected_counts.loc[:, residuals.columns]
     vst_out['filtered_normalized_counts'] = np.log1p(vst_out['filtered_corrected_counts'])
     return data, vst_out