correct naming in tutorials

Author: MayraRuizT

docs/notebooks/Nichesphere_tutorial_MIvisium_coloc.ipynb

@@ -5,11 +5,11 @@
    "id": "ecc6a985-29a7-44e4-8f27-c6a8883f3a41",
    "metadata": {},
    "source": [
-    "# Nichesphere differential co-localization tutorial\n",
+    "# NicheSphere differential co-localization tutorial\n",
     "\n",
-    "Nichesphere is an sc-verse compatible Python library which allows the user to find differentially co-localized cellular niches and biological processes involved in their interactions based on cell type pairs co-localization probabilities in different conditions. Cell type pair co-localization probabilities are obtained in different ways: from deconvoluted Visium 10x / PIC-seq data (probabilities of finding each cell type in each spot / multiplet), or counting cell boundaries overlaps for each cell type pair in single cell spatial data (MERFISH , CODEX ...). This tutorial focuses on defining groups of cells that converge or split in disease (Ischemia) based on differential co-localization. \n",
+    "NicheSphere is an sc-verse compatible Python library which allows the user to find differentially co-localized cellular niches and biological processes involved in their interactions based on cell type pairs co-localization probabilities in different conditions. Cell type pair co-localization probabilities are obtained in different ways: from deconvoluted Visium 10x / PIC-seq data (probabilities of finding each cell type in each spot / multiplet), or counting cell boundaries overlaps for each cell type pair in single cell spatial data (MERFISH , CODEX ...). This tutorial focuses on defining groups of cells that converge or split in disease (Ischemia) based on differential co-localization. \n",
     "\n",
-    "Nichesphere also offers the possibility to look at localized differential cell - cell communication based on Ligand-Receptor pairs expression data, such as results from CrossTalkeR [ref]. This is addressed in the localized differential communication tutorial.\n"
+    "NicheSphere also offers the possibility to look at localized differential cell - cell communication based on Ligand-Receptor pairs expression data. This is addressed in the localized differential communication tutorial.\n"
    ]
   },
   {
@@ -1558,7 +1558,7 @@
    "id": "78eb6625-c82e-482e-80a1-6eddfcfe92b9",
    "metadata": {},
    "source": [
-    "Now we can plot the differential co-localization network using the **colocNW** function from Nichesphere. This function has many parameters that can be tuned: \n",
+    "Now we can plot the differential co-localization network using the **colocNW** function from NicheSphere. This function has many parameters that can be tuned: \n",
     "\n",
     "**nodeSize** for example, defines how the size of the nodes will be calculated. Options are 'betweeness', 'pagerank' (both network statistics) and None (all nodes have the same size).\n",
     "**alpha** indicates the transparency of the edges and in goes from 0 (completely transparent) to 1 (opaque)\n",
@@ -1851,7 +1851,7 @@
    "id": "c832201e-b235-4314-a694-c5d4b7f53ede",
    "metadata": {},
    "source": [
-    "Then we can get the node positions to input them to the nichesphere **colocNW** function through the **pos** parameter"
+    "Then we can get the node positions to input them to the NicheSphere **colocNW** function through the **pos** parameter"
    ]
   },
   {
@@ -2242,7 +2242,7 @@
    "id": "a1ef977c-ac57-4131-8e2a-92a5e42bd69e",
    "metadata": {},
    "source": [
-    "For further analysis, like differential communication: https://nichesphere.readthedocs.io/en/latest/notebooks/Nichesphere_tutorial_MIvisium_comm.html \n",
+    "For further analysis, like differential communication: https://nichesphere.readthedocs.io/en/latest/tutorials.html \n",
     "\n",
     ", we will need the correspondence data between cell pairs and niche pairs"
    ]

docs/notebooks/Nichesphere_tutorial_MIvisium_comm.ipynb

@@ -7,15 +7,15 @@
     "tags": []
    },
    "source": [
-    "# Nichesphere differential communication tutorial"
+    "# NicheSphere differential communication tutorial"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "43f6b026-a482-4bb1-92f8-47ac2cfbbbbe",
    "metadata": {},
    "source": [
-    "Nichesphere also offers the possibility to look at localized process based differential cell - cell communication based on Ligand-Receptor pairs expression data, such as results from LIANA and CrossTalkeR(Nagai et. al., 2021). This improves the interpretability of cell communication analysis as we will find changes in biological processes related to the changes in physical cell interactions. For this tutorial, we will use our self compiled database which classifies ligands according to extracellular matrix (ECM) or immune cell recruitment related processes: https://github.com/CostaLab/Nichesphere/tree/main/nichesphere/db"
+    "NicheSphere also offers the possibility to look at localized process based differential cell - cell communication based on Ligand-Receptor pairs expression data, such as results from LIANA and CrossTalkeR (Nagai et. al., 2021). This improves the interpretability of cell communication analysis as we will find changes in biological processes related to the changes in physical cell interactions. For this tutorial, we will use our self compiled database which classifies ligands according to extracellular matrix (ECM) or immune cell recruitment related processes: https://github.com/CostaLab/NicheSphere/tree/main/db"
    ]
   },
   {
@@ -74,7 +74,7 @@
    "id": "dd39150c-a214-4b87-90bf-dbdec2b0b565",
    "metadata": {},
    "source": [
-    "One of the advantages of Nichesphere is that it allows process based localized differential communication testing by looking at changes in LR interactions involved in specific processes between niches. To do this, we will need the correspondence data between cell type pairs and niche pairs, which we got from the **differential co-localization analysis**. You can check the differential co-localization tutorial for more details: https://nichesphere.readthedocs.io/en/latest/notebooks/Nichesphere_tutorial_MIvisium_coloc.html "
+    "One of the advantages of NicheSphere is that it allows process based localized differential communication testing by looking at changes in LR interactions involved in specific processes between niches. To do this, we will need the correspondence data between cell type pairs and niche pairs, which we got from the **differential co-localization analysis**. You can check the differential co-localization tutorial for more details: https://nichesphere.readthedocs.io/en/latest/tutorials.html"
    ]
   },
   {
@@ -601,7 +601,7 @@
    "id": "7952fa4f-03fd-4cd3-a00d-ff878d670e2f",
    "metadata": {},
    "source": [
-    "Nichesphere is suited to work with CrossTalkeR output tables which contain columns called **cellpair**, indicating the cell types involved in an interaction separated by '@'; and **allpair**, indicating cell types, ligand and receptor involved. Additionally, input tables for Nichesphere should contain columns indicating the **ligand** (gene_A), **receptor** (gene_B) and **communication score** (MeanLR). The names for these last 3 columns can be indicated in Nichesphere functions."
+    "NicheSphere is suited to work with CrossTalkeR output tables which contain columns called **cellpair**, indicating the cell types involved in an interaction separated by '@'; and **allpair**, indicating cell types, ligand and receptor involved. Additionally, input tables for NicheSphere should contain columns indicating the **ligand** (gene_A), **receptor** (gene_B) and **communication score** (MeanLR). The names for these last 3 columns can be indicated in NicheSphere functions."
    ]
   },
   {
@@ -1227,7 +1227,7 @@
    "id": "b75e023e-4593-40be-9800-60cb0a0b1140",
    "metadata": {},
    "source": [
-    "And now we can **plot differential cell communication** scores per process on the edges of the co-localization network using the **catNW** function from Nichesphere, whose parameters are quite similar to the ones of the **colocMW** function"
+    "And now we can **plot differential cell communication** scores per process on the edges of the co-localization network using the **catNW** function from NicheSphere, whose parameters are quite similar to the ones of the **colocMW** function"
    ]
   },
   {

docs/notebooks/Nichesphere_tutorial_MIvisium_preprocessing.ipynb

@@ -5,11 +5,11 @@
    "id": "ecc6a985-29a7-44e4-8f27-c6a8883f3a41",
    "metadata": {},
    "source": [
-    "# Nichesphere data preprocessing tutorial\n",
+    "# NicheSphere data preprocessing tutorial\n",
     "\n",
-    "Nichesphere is an sc-verse compatible Python library which allows the user to find differentially co-localized cellular niches and biological processes involved in their interactions based on cell type pairs co-localization probabilities in different conditions. Cell type pair co-localization probabilities are obtained in different ways: from deconvoluted Visium 10x / PIC-seq data (probabilities of finding each cell type in each spot / multiplet), or counting cell boundaries overlaps for each cell type pair in single cell spatial data (MERFISH , CODEX ...). This is addressed in the differential co-localization tutorial.\n",
+    "NicheSphere is an sc-verse compatible Python library which allows the user to find differentially co-localized cellular niches and biological processes involved in their interactions based on cell type pairs co-localization probabilities in different conditions. Cell type pair co-localization probabilities are obtained in different ways: from deconvoluted Visium 10x / PIC-seq data (probabilities of finding each cell type in each spot / multiplet), or counting cell boundaries overlaps for each cell type pair in single cell spatial data (MERFISH , CODEX ...). This is addressed in the differential co-localization tutorial.\n",
     "\n",
-    "Nichesphere also offers the possibility to look at localized differential cell - cell communication based on Ligand-Receptor pairs expression data, such as results from CrossTalkeR [ref]. This is addressed in the localized differential communication tutorial.\n",
+    "NicheSphere also offers the possibility to look at localized differential cell - cell communication based on Ligand-Receptor pairs expression data. This is addressed in the localized differential communication tutorial.\n",
     "\n",
     "This tutorial focuses on the data **pre-processing** needed before doing the differential co-localization and localized process based differential communication analyses. "
    ]
@@ -158,7 +158,7 @@
    "source": [
     "In this case, we will get cell type co-localization probabilities from **deconvoluted Visium** data (Cell type probabilities per spot): \n",
     "\n",
-    "In a previous step, we used MOSCOT(Klein et. al., 2025) to deconvolute cell subtypes in visium slices from the same 23 samples , getting matrices of probabilities of each cell being in each spot. Now we will get cell type probabilities per spot summing the probabilities of cells of the same kind in each spot; thus getting cell type probability matrices for all spots per sample. This can be done with the **get_spot_ct_props** function from Nichesphere. As an example, we will do this for two samples: control sample **control_P1** and ischemic sample **GT_IZ_P9**"
+    "In a previous step, we used MOSCOT(Klein et. al., 2025) to deconvolute cell subtypes in visium slices from the same 23 samples , getting matrices of probabilities of each cell being in each spot. Now we will get cell type probabilities per spot summing the probabilities of cells of the same kind in each spot; thus getting cell type probability matrices for all spots per sample. This can be done with the **get_spot_ct_props** function from NicheSphere. As an example, we will do this for two samples: control sample **control_P1** and ischemic sample **GT_IZ_P9**"
    ]
   },
   {
@@ -1775,7 +1775,7 @@
    "source": [
     "## 4. For differential communication\n",
     "\n",
-    "Nichesphere is suited to work with CrossTalkeR output tables which contain columns called **cellpair**, indicating the cell types involved in an interaction separated by '@'; and **allpair**, indicating cell types, ligand and receptor involved. Additionally, input tables for Nichesphere should contain columns indicating the **ligand** (gene_A), **receptor** (gene_B) and **communication score** (MeanLR). The names for these last 3 columns can be indicated in Nichesphere functions.\n",
+    "NicheSphere is suited to work with CrossTalkeR output tables which contain columns called **cellpair**, indicating the cell types involved in an interaction separated by '@'; and **allpair**, indicating cell types, ligand and receptor involved. Additionally, input tables for NicheSphere should contain columns indicating the **ligand** (gene_A), **receptor** (gene_B) and **communication score** (MeanLR). The names for these last 3 columns can be indicated in NicheSphere functions.\n",
     "\n",
     "The following tables come from a CrossTalkeR run comparing **myogenic** and **ischemic** samples. They are the condition specific tables in the **LR_data_final** object:"
    ]