update the Colab_Reproducibility：https

Starlitnightly · Nov 19, 2023 · 66d66b3 · 66d66b3
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diff --git a/omicverse_guide/docs/Tutorials-bulk/t_bulk_combat.ipynb b/omicverse_guide/docs/Tutorials-bulk/t_bulk_combat.ipynb
@@ -17,7 +17,9 @@
     "\n",
     "Paper: [pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods](https://doi.org/10.1101/2020.03.17.995431)\n",
     "\n",
-    "Code: https://github.com/epigenelabs/pyComBat"
+    "Code: https://github.com/epigenelabs/pyComBat\n",
+    "\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/121bbIiI3j4pTZ3yA_5p8BRkRyGMMmNAq?usp=sharing"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-bulk/t_deg.ipynb b/omicverse_guide/docs/Tutorials-bulk/t_deg.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "An important task of bulk rna-seq analysis is the different expression , which we can perform with omicverse. For different expression analysis, ov change the `gene_id` to `gene_name` of matrix first. When our dataset existed the batch effect, we can use the SizeFactors of DEseq2 to normalize it, and use `t-test` of `wilcoxon` to calculate the p-value of genes. Here we demonstrate this pipeline with a matrix from `featureCounts`. The same pipeline would generally be used to analyze any collection of RNA-seq tasks. \n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/19MyE4hNu_PRtz7Yvhs3VIOR8VMc9PkyI?usp=sharing"
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1q5lDfJepbtvNtc1TKz-h4wGUifTZ3i0_?usp=sharing"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-bulk/t_deseq2.ipynb b/omicverse_guide/docs/Tutorials-bulk/t_deseq2.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "Code: https://github.com/owkin/PyDESeq2\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1jX73eidBkDqGpd6qLASIjGENQaeZ34EU?usp=sharing"
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1fZS-v0zdIYkXrEoIAM1X5kPoZVfVvY5h?usp=sharing"
    ]
   },
   {
@@ -224,6 +224,16 @@
     "We performed the gene_id mapping by the mapping pair file `GRCm39` downloaded before."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5eda606e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ov.utils.download_geneid_annotation_pair()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,

diff --git a/omicverse_guide/docs/Tutorials-bulk/t_network.ipynb b/omicverse_guide/docs/Tutorials-bulk/t_network.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "Here we produce a tutorial that use python to construct protein-protein interaction network\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1q0_ikTYYscphDW_LUGZkplN94yAQ2CvU?usp=sharing"
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1ReLCFA5cNNcem_WaMXYN9da7W0GN4gzl?usp=sharing"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-bulk/t_wgcna.ipynb b/omicverse_guide/docs/Tutorials-bulk/t_wgcna.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "Code: Reproduce by Python. Raw is http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1XUspbEMX9aOS96RPMkfmb9mi9H7lZl1F?usp=sharing\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1EbP-Tq1IwYO9y1_-zzw23XlPbzrxP0og?usp=sharing\n",
     "\n",
     "Here, you will be briefly guided through the basics of how to use omicverse to perform wgcna anlysis. Once you are set"
    ]

diff --git a/omicverse_guide/docs/Tutorials-bulk2single/t_bulk2single.ipynb b/omicverse_guide/docs/Tutorials-bulk2single/t_bulk2single.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "Code: https://github.com/ZJUFanLab/bulk2space\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1He71hAyeAv1DHQyXUlxtoJ4QvwZwW7I0?usp=sharing\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1ST5tvSH3lqMkXLf8xv8jpPMHCWO8KJOC?usp=sharing\n",
     "\n",
     "This tutorial walks through how to read, set-up and train the model from bulk RNA-seq and reference scRNA-seq data. We use the pdac datasets as example"
    ]
@@ -489,7 +489,7 @@
     }
    ],
    "source": [
-    "GEP,CellFractionPrediction=model.predicted_fraction()"
+    "CellFractionPrediction=model.predicted_fraction()"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-bulk2single/t_bulktrajblend.ipynb b/omicverse_guide/docs/Tutorials-bulk2single/t_bulktrajblend.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "We developed BulkTrajBlend in OmicVerse, which is specifically designed to address cell continuity in single-cell sequencing.BulkTrajBlend first deconvolves single-cell data from Bulk RNA-seq and then uses a GNN-based overlapping community discovery algorithm to identify contiguous cells in the generated single-cell data.\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1HulVXQIlUEcpGRDZo4MxcHYOjnVhuCC-?usp=sharing\n"
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1JChlTjcejvZydZleMGvsJPKgSG4TdrB-?usp=sharing\n"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-bulk2single/t_single2spatial.ipynb b/omicverse_guide/docs/Tutorials-bulk2single/t_single2spatial.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "Code: https://github.com/ZJUFanLab/bulk2space\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1oPvPpNnt2YF803CQNdH_7PdRuPqtKRVb?usp=sharing\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1E3TlGUW2bxEew8JYCLE9qA6saYbIjBnY?usp=sharing\n",
     "\n",
     "This tutorial walks through how to read, set-up and train the model from scRNA-seq and reference stRNA-seq data. We use the pdac datasets as example"
    ]

diff --git a/omicverse_guide/docs/Tutorials-single/t_anno_trans.ipynb b/omicverse_guide/docs/Tutorials-single/t_anno_trans.ipynb
diff --git a/omicverse_guide/docs/Tutorials-single/t_aucell.ipynb b/omicverse_guide/docs/Tutorials-single/t_aucell.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "Code: https://github.com/aertslab/AUCell\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1d0nvAyP6Jo2Be7NdX00aHD39YZubDdeX?usp=sharing\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1Rk7Zopil-Ve1WFRQLV_AZOwCHRyhsOXk?usp=sharing\n",
     "\n",
     "<div class=\"admonition warning\">\n",
     "  <p class=\"admonition-title\">Warning</p>\n",

diff --git a/omicverse_guide/docs/Tutorials-single/t_cellanno.ipynb b/omicverse_guide/docs/Tutorials-single/t_cellanno.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "Code: https://github.com/bioinfo-ibms-pumc/SCSA\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1RO6giAKFEsleaGAN8YIiA6M0QPJlhJuY?usp=sharing\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1BC6hPS0CyBhNu0BYk8evu57-ua1bAS0T?usp=sharing\n",
     "\n",
     "<div class=\"admonition warning\">\n",
     "  <p class=\"admonition-title\">Note</p>\n",

diff --git a/omicverse_guide/docs/Tutorials-single/t_cellfate_gene.ipynb b/omicverse_guide/docs/Tutorials-single/t_cellfate_gene.ipynb
diff --git a/omicverse_guide/docs/Tutorials-single/t_cellfate_genesets.ipynb b/omicverse_guide/docs/Tutorials-single/t_cellfate_genesets.ipynb
diff --git a/omicverse_guide/docs/Tutorials-single/t_cluster.ipynb b/omicverse_guide/docs/Tutorials-single/t_cluster.ipynb
@@ -17,7 +17,9 @@
     "\n",
     "## Preprocess data\n",
     "\n",
-    "As an example, we apply differential kinetic analysis to dentate gyrus neurogenesis, which comprises multiple heterogeneous subpopulations."
+    "As an example, we apply differential kinetic analysis to dentate gyrus neurogenesis, which comprises multiple heterogeneous subpopulations.\n",
+    "\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1d_szq-y-g7O0C5rJgK22XC7uWTUNrYpK?usp=sharing"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-single/t_metatime.ipynb b/omicverse_guide/docs/Tutorials-single/t_metatime.ipynb
@@ -15,6 +15,8 @@
     "\n",
     "Code: https://github.com/yi-zhang/MetaTiME\n",
     "\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1isvjTfSFM2cy6GzHWAwbuvSjveEJijzP?usp=sharing\n",
+    "\n",
     "![metatime](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-023-38333-8/MediaObjects/41467_2023_38333_Fig1_HTML.png)"
    ]
   },

diff --git a/omicverse_guide/docs/Tutorials-single/t_mofa.ipynb b/omicverse_guide/docs/Tutorials-single/t_mofa.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "Code: https://github.com/bioFAM/mofapy2\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1Grp-r-fQ3LHO7myJt4r_fPX7T3uJ-vBy?usp=sharing"
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1UPGQA3BenrC-eLIGVtdKVftSnOKIwNrP?usp=sharing"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-single/t_mofa_glue.ipynb b/omicverse_guide/docs/Tutorials-single/t_mofa_glue.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "Code: https://github.com/bioFAM/mofapy2 and https://github.com/gao-lab/GLUE\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1_rickQz7irTremCZytnnNQTQLyhgBn0O?usp=sharing\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1zlakFf20IoBdyuOQDocwFQHu8XOVizRL?usp=sharing\n",
     "\n",
     "We used the result anndata object `rna-emb.h5ad` and `atac.emb.h5ad` from [GLUE'tutorial](https://scglue.readthedocs.io/en/latest/training.html)"
    ]

diff --git a/omicverse_guide/docs/Tutorials-single/t_nocd.ipynb b/omicverse_guide/docs/Tutorials-single/t_nocd.ipynb
@@ -11,6 +11,8 @@
     "\n",
     "This tutorial focuses on how to cluster the cell with overlapping and identify the cell with multi-fate\n",
     "\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1l7iHdVmTQcv9YmpIjhK_UzLuHbJ1Jv9E?usp=sharing\n",
+    "\n",
     "<div class=\"admonition warning\">\n",
     "  <p class=\"admonition-title\">Warning</p>\n",
     "  <p>\n",

diff --git a/omicverse_guide/docs/Tutorials-single/t_preprocess.ipynb b/omicverse_guide/docs/Tutorials-single/t_preprocess.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "User can compare our tutorial with [scanpy'tutorial](https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html) to learn how to use omicverse well\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/18a6vsvdRdWVPCP-KSaiznou7PBVtNVXw?usp=sharing"
+    "Colab_Reproducibility：https://colab.research.google.com/drive/1DXLSls_ppgJmAaZTUvqazNC_E7EDCxUe?usp=sharing"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-single/t_scdeg.ipynb b/omicverse_guide/docs/Tutorials-single/t_scdeg.ipynb
@@ -11,7 +11,7 @@
     "Sometimes we need to compare differentially expressed genes or differentially expressed features between two cell types on single cell data, but existing methods focus more on cell-specific gene analysis. Researchers need to transfer bulk RNA-seq analysis to single-cell analysis, which involves interaction between different programming languages or programming tools, adding significantly to the workload of the researcher.\n",
     "Here, we use omicverse's bulk RNA-seq pyDEG method to complete differential expression analysis at the single cell level.\n",
     "\n",
-    "Colab_Reproducibility：https://colab.research.google.com/drive/1u6digKEpN7m88iA0_668UZLvlqYwypny?usp=sharing"
+    "Colab_Reproducibility：https://colab.research.google.com/drive/12faBRh0xT7v6KSy8NCSRqbegF_AEoDXr?usp=sharing"
    ]
   },
   {

diff --git a/omicverse_guide/docs/Tutorials-single/t_traj.ipynb b/omicverse_guide/docs/Tutorials-single/t_traj.ipynb
@@ -16,7 +16,9 @@
     "\n",
     "## Preprocess data\n",
     "\n",
-    "As an example, we apply differential kinetic analysis to dentate gyrus neurogenesis, which comprises multiple heterogeneous subpopulations."
+    "As an example, we apply differential kinetic analysis to dentate gyrus neurogenesis, which comprises multiple heterogeneous subpopulations.\n",
+    "\n",
+    "Colab_Reproducibility：https://colab.research.google.com/drive/18aFCEmuiYxH5BDS3H7SkemmL2isG7iL2?usp=sharing"
    ]
   },
   {
-Original file line number
+Diff line change
@@ Expand Up / @@ -10,7 +10,7 @@ @@
         "\n",
         "An important task of bulk rna-seq analysis is the different expression , which we can perform with omicverse. For different expression analysis, ov change the `gene_id` to `gene_name` of matrix first. When our dataset existed the batch effect, we can use the SizeFactors of DEseq2 to normalize it, and use `t-test` of `wilcoxon` to calculate the p-value of genes. Here we demonstrate this pipeline with a matrix from `featureCounts`. The same pipeline would generally be used to analyze any collection of RNA-seq tasks. \n",
         "\n",
-        "Colab_Reproducibility：https://colab.research.google.com/drive/19MyE4hNu_PRtz7Yvhs3VIOR8VMc9PkyI?usp=sharing"
+        "Colab_Reproducibility：https://colab.research.google.com/drive/1q5lDfJepbtvNtc1TKz-h4wGUifTZ3i0_?usp=sharing"
        ]
       },
       {
@@ Expand Down @@