Updating versions

.gitignore

@@ -0,0 +1,6 @@
+__pycache__/
+*.py[cod]
+*.egg-info/
+dist/
+build/
+.eggs/

README.md

@@ -3,6 +3,8 @@
 # NicheSphere
 
 [![DOI](https://zenodo.org/badge/685916590.svg)](https://doi.org/10.5281/zenodo.18756710)
+![PyPI - Version](https://img.shields.io/pypi/v/NicheSphere)
+
 
 NicheSphere is an sc-verse compatible Python library which allows the user to find differential co-localization domains / niches based on cell type pair co-localization probabilities in different conditions. Cell type pair co-localization probabilities can be obtained in different ways, for example, through deconvolution of spatial transcriptomics / PIC-seq data (getting the probabilities of finding each cell type in each spot / multiplet) ; or counting nearest neighbors of each type for each cell in single cell spatial data like MERFISH or CODEX.
 

pyproject.toml

@@ -13,11 +13,13 @@ readme = "README.md"
 where = ["src"]
 
 [tool.poetry.dependencies]
-python = "^3.10"
-scanpy = "1.10.4"
+python = "^3.11"
+scanpy = ">=1.10.4,<1.12"
 leidenalg = "^0.10.2"
 pot = "^0.9.5"
-anndata = "0.11.4"
+anndata = ">=0.12.11,<0.13"
+zarr = ">=3.1.6,<4"
+pillow = ">=12.2.0"
 
 [build-system]
 requires = ["poetry-core"]

src/nichesphere/coloc.py

@@ -387,7 +387,7 @@ def colocNW(x_diff,adj, cell_group, group=None, group_cmap='tab20', ncols=20, cl
     graycmp = ListedColormap(graycmp)
 
     #cell groups cmap
-    cmap = plt.cm.get_cmap(group_cmap, ncols)
+    cmap = plt.colormaps[group_cmap].resampled(ncols)
     if clist == None:
         cgroup_cmap=[mcolors.rgb2hex(cmap(i)[:3]) for i in range(cmap.N)]
     else:

src/nichesphere/comm.py

@@ -238,12 +238,12 @@ def getDiffComm(diffCommTbl, pairCatDF, ncells, cat):
         cells x cells or groups x groups dataframe of differential communication scores for a specific LR category
     """
     x=pd.DataFrame(pairCatDF.cell_pairs)
-    x['wilcoxStat']=0
+    x['wilcoxStat']=0.0
     x.index=pairCatDF.cell_pairs
 
 
     for i in diffCommTbl.columns:
-        x.wilcoxStat[i]=diffCommTbl[i][cat]
+        x.loc[i, 'wilcoxStat']=diffCommTbl[i][cat]
 
 
     x=pd.Series(x.wilcoxStat)
@@ -342,7 +342,7 @@ def catNW(x_chem,colocNW, cell_group, group=None, group_cmap='tab20', ncols=20,
     graycmp = ListedColormap(graycmp)
 
     #cell group cmap
-    cmap = plt.cm.get_cmap(group_cmap, ncols)
+    cmap = plt.colormaps[group_cmap].resampled(ncols)
     if clist == None:
         cgroup_cmap=[mcolors.rgb2hex(cmap(i)[:3]) for i in range(cmap.N)]
     else:

src/nichesphere/tl.py

@@ -73,8 +73,8 @@ def cells_niche_colors(CTs, niche_colors, niche_dict):
     niche_df['niche']=niche_colors.index[0]
     niche_df['color']=niche_colors[0]
     for key in list(niche_dict.keys()):
-        niche_df['niche'][[c in niche_dict[key] for c in niche_df.cell]]=key
-        niche_df['color'][niche_df['niche']==key]=niche_colors[key]
+        niche_df.loc[[c in niche_dict[key] for c in niche_df.cell], 'niche']=key
+        niche_df.loc[niche_df['niche']==key, 'color']=niche_colors[key]
     niche_df.index=niche_df.cell
     niche_df.niche=niche_df.niche.astype('category')
     return niche_df
@@ -151,7 +151,7 @@ def PIC_BGdoubletsOEratios(adata_singlets, annot_col):
     ## Get random singlets pairs
     pairNums=[i for i in range(int(np.round(adata_singlets.obs.shape[0]//2))) for _ in range(2)]
     pairNumsIdx=random.sample(list(adata_singlets.obs.index), len(pairNums))
-    rdf.pair[pairNumsIdx]=pairNums
+    rdf.loc[pairNumsIdx, 'pair']=pairNums
 
     pairCounts=[rdf.annot[rdf.pair==i][0]+'-'+rdf.annot[rdf.pair==i][1] for i in rdf.pair.value_counts().index[rdf.pair.value_counts()==2]]
 
@@ -234,10 +234,10 @@ def get_pairCatDFdir(niches_df):
 
     pairCatDFdir['niche_pairs']=''
     for clust in np.sort(niches_df.niche.unique()):
-        pairCatDFdir['niche_pairs'][[cellCatContained(pair=p, cellCat=niches_df.cell[niches_df.niche==clust]) for p in pairCatDFdir.cell_pairs]]=clust+'->'+clust
+        pairCatDFdir.loc[[cellCatContained(pair=p, cellCat=niches_df.cell[niches_df.niche==clust]) for p in pairCatDFdir.cell_pairs], 'niche_pairs']=clust+'->'+clust
 
     for comb in list(itertools.permutations(list(niches_df.niche.unique().sort_values()), 2)):
-        pairCatDFdir['niche_pairs'][[(p.split('->')[0] in niches_df.cell[niches_df.niche==comb[0]]) & (p.split('->')[1] in niches_df.cell[niches_df.niche==comb[1]]) for p in pairCatDFdir.cell_pairs]]=comb[0]+'->'+comb[1]
+        pairCatDFdir.loc[[(p.split('->')[0] in niches_df.cell[niches_df.niche==comb[0]]) & (p.split('->')[1] in niches_df.cell[niches_df.niche==comb[1]]) for p in pairCatDFdir.cell_pairs], 'niche_pairs']=comb[0]+'->'+comb[1]
 
     return pairCatDFdir
 #%%
@@ -288,10 +288,10 @@ def getColocFilter(pairCatDF, adj, oneCTints):
     colocFilt['filter']=0
 
     for i in pairCatDF.cell_pairs:
-        colocFilt['filter'][i]=adj.loc[i.split('->')[1],i.split('->')[0]]
+        colocFilt.loc[i, 'filter']=adj.loc[i.split('->')[1],i.split('->')[0]]
 
-    colocFilt['filter'][oneCTints]=1
-    colocFilt['filter'][colocFilt['filter']>0]=1
+    colocFilt.loc[oneCTints, 'filter']=1
+    colocFilt.loc[colocFilt['filter']>0, 'filter']=1
     colocFilt=pd.DataFrame(colocFilt['filter'], index=colocFilt.index, columns=['filter'])
     return colocFilt