update the url of demo data to tutorials.

STOmics · Jul 12, 2024 · dacfa06 · dacfa06
1 parent e231468
commit dacfa06
Show file tree

Hide file tree

Showing 25 changed files with 43 additions and 453 deletions.
diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -7,7 +7,7 @@ pandas>=1.5.3
 scipy==1.10.1
 seaborn==0.12.2
 h5py>=3.7.0
-gefpy>=1.1.6
+gefpy>=1.1.9
 # setuptools>=41.0.0,<60.0.0
 setuptools==68.2.2
 #todo python3.8/site-packages/umap/distances.py:1053: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator.
@@ -60,14 +60,14 @@ opencv-python>=4.8.0.76
 # protobuf<3.21.0a0,>=3.20.1
 # protobuf>=3.20.1
 hotspotsc==1.1.1
-# git+https://github.com/YosefLab/Hotspot.git
+# git+https://github.com/YosefLab/Hotspot.git@v1.1.2
 distributed>=2023.3.2.1
 # keras==2.7.0
 pyarrow>=10.0.1
 tables>=3.6.1
 spatialpandas>=0.4.4
 # selenium>=4.4.3
-loompy==3.0.7
+loompy==3.0.6
 gtfparse==1.2.1
 harmonypy==0.0.6
 SQLAlchemy==1.3.24
@@ -77,7 +77,7 @@ pyscenic>=0.12.1
 onnxruntime==1.15.1
 POT==0.9.1
 distinctipy>=1.2.2
-joypy>=0.2.6
+# joypy>=0.2.4
 lxml>=4.8.0
 fastcluster>=1.2.6
 pycirclize>=0.5.0

diff --git a/docs/source/Tutorials(Multi-sample)/3D_Cell_Cell_Communication.ipynb b/docs/source/Tutorials(Multi-sample)/3D_Cell_Cell_Communication.ipynb
@@ -39,7 +39,7 @@
     }
    },
    "source": [
-    "The demo data used here is 59 samples of 2D mouse embryo heart stereo-seq data. Download [example data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f) and load them as a MSData."
+    "The demo data used here is 59 samples of 2D mouse embryo heart stereo-seq data. Download [example data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc) and load them as a MSData."
    ]
   },
   {
@@ -3438,7 +3438,7 @@
     "\n",
     "The parameter `regulons` specifies the path of file saving regulons which is output of function of [3D Gene Regulatory Network](https://stereopy.readthedocs.io/en/latest/Tutorials/3D_Gene_Regulatory_Network.html).\n",
     "\n",
-    "You need to specify the path of file which contains the weighted network infomation by parameter `weighted_network_path` . By default, you can download the NicheNet-V2 weighted network files from [here](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f).\n",
+    "You need to specify the path of file which contains the weighted network infomation by parameter `weighted_network_path` . By default, you can download the NicheNet-V2 weighted network files from [here](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc).\n",
     "\n",
     "There are two files about weighted network infomations:\n",
     "\n",

diff --git a/docs/source/Tutorials(Multi-sample)/3D_Gene_Regulatory_Network.ipynb b/docs/source/Tutorials(Multi-sample)/3D_Gene_Regulatory_Network.ipynb
@@ -40,7 +40,7 @@
    "id": "96736dad",
    "metadata": {},
    "source": [
-    "We use *Drosophila melanogaster* data to simulate a 3D model. Download the [example data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f) first."
+    "We use *Drosophila melanogaster* data to simulate a 3D model. Download the [example data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc) first."
    ]
   },
   {
@@ -531,7 +531,7 @@
     "Before performing the function, make sure the preparation of following input files: \n",
     "\n",
     "1. Spatially resolved transcriptomics data: the matrix of gene expression for each single cell. Each column represents a gene, and each row represents a SRT sample. Values in the matrix are typically gene expression levels, either raw counts or normalized expression (raw counts prefered). SRT expression matrix of bin200 in the mouse brain generated by Stereo-seq is used here, through \n",
-    "    **[example data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f)**.\n",
+    "    **[example data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc)**.\n",
     "\n",
     "2. Transcription factor gene list: a list of transcription factors of interest. This list is requried for the network inference step. Data can be downloaded through\n",
     "    **[pySCENIC_TF_list](https://github.com/aertslab/pySCENIC/tree/master/resources)** and\n",

diff --git a/docs/source/Tutorials(Multi-sample)/Batch_Effect.ipynb b/docs/source/Tutorials(Multi-sample)/Batch_Effect.ipynb
diff --git a/docs/source/Tutorials(Multi-sample)/Batch_QC.ipynb b/docs/source/Tutorials(Multi-sample)/Batch_QC.ipynb
@@ -35,7 +35,7 @@
    "id": "80bf0bab",
    "metadata": {},
    "source": [
-    "Here we use demo data of olfactory bulb. Download the [example data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f) first."
+    "Here we use demo data of olfactory bulb. Download the [example data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc) first."
    ]
   },
   {

diff --git a/docs/source/Tutorials(Multi-sample)/Comparative_Analysis.ipynb b/docs/source/Tutorials(Multi-sample)/Comparative_Analysis.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "In scientific research, comparing disturbed or disease samples with control samples allows for the exploration of changes in functional mechanisms at both global and local levels. Stereopy is specifically designed to identify global and local diversities in comparative samples by employing cell-level and gene-level analysis modules, supported by novel algorithms. The cell-level analysis focuses on cell diversity in terms of cell type, cell-cell occurrence, and cell community via multi-sample comparisons. At gene level, Stereopy investigates gene diversity within cell types and cell communities, proposing the concept of constant and conditional markers [[Fang23]](https://doi.org/10.1101/2023.12.04.569485).\n",
     "\n",
-    "Here we use Slide-seq mouse kidney samples to test comparative analysis [[Marshall22](https://pubmed.ncbi.nlm.nih.gov/35372810/)]. Please download [test data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f), and here also offers the access to [full data](https://cellxgene.cziscience.com/collections/8e880741-bf9a-4c8e-9227-934204631d2a) from the paper."
+    "Here we use Slide-seq mouse kidney samples to test comparative analysis [[Marshall22](https://pubmed.ncbi.nlm.nih.gov/35372810/)]. Please download [test data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc), and here also offers the access to [full data](https://cellxgene.cziscience.com/collections/8e880741-bf9a-4c8e-9227-934204631d2a) from the paper."
    ]
   },
   {

diff --git a/docs/source/Tutorials(Multi-sample)/MSData_Clustering.ipynb b/docs/source/Tutorials(Multi-sample)/MSData_Clustering.ipynb
@@ -37,7 +37,7 @@
    "id": "e1667ddb",
    "metadata": {},
    "source": [
-    "Please download the [example data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f) first. Here we use drosophila data for demo. Main input file formats are GEM/GEF (from Stereo-seq), H5ad (from Scanpy). Add your dataset as below:"
+    "Please download the [example data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc) first. Here we use drosophila data for demo. Main input file formats are GEM/GEF (from Stereo-seq), H5ad (from Scanpy). Add your dataset as below:"
    ]
   },
   {

diff --git a/docs/source/Tutorials(Multi-sample)/ST_Gears.ipynb b/docs/source/Tutorials(Multi-sample)/ST_Gears.ipynb
@@ -85,7 +85,7 @@
    "source": [
     "## Reading Data\n",
     "\n",
-    "Download our [example data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f)"
+    "Download our [example data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc)"
    ]
   },
   {
-Original file line number
+Diff line change
@@ Expand Up / @@ -9,7 +9,7 @@ @@
         "\n",
         "In scientific research, comparing disturbed or disease samples with control samples allows for the exploration of changes in functional mechanisms at both global and local levels. Stereopy is specifically designed to identify global and local diversities in comparative samples by employing cell-level and gene-level analysis modules, supported by novel algorithms. The cell-level analysis focuses on cell diversity in terms of cell type, cell-cell occurrence, and cell community via multi-sample comparisons. At gene level, Stereopy investigates gene diversity within cell types and cell communities, proposing the concept of constant and conditional markers [[Fang23]](https://doi.org/10.1101/2023.12.04.569485).\n",
         "\n",
-        "Here we use Slide-seq mouse kidney samples to test comparative analysis [[Marshall22](https://pubmed.ncbi.nlm.nih.gov/35372810/)]. Please download [test data](http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f), and here also offers the access to [full data](https://cellxgene.cziscience.com/collections/8e880741-bf9a-4c8e-9227-934204631d2a) from the paper."
+        "Here we use Slide-seq mouse kidney samples to test comparative analysis [[Marshall22](https://pubmed.ncbi.nlm.nih.gov/35372810/)]. Please download [test data](http://116.6.21.110:8090/share/c5d9e7f3-7d66-4154-87e1-a740f4bb4dbc), and here also offers the access to [full data](https://cellxgene.cziscience.com/collections/8e880741-bf9a-4c8e-9227-934204631d2a) from the paper."
        ]
       },
       {
@@ Expand Down @@