diff --git a/docs/user-guide/doc-odm-user-guide/doc-odm-user-guide/notebooks/sc_rnaseq_demo.ipynb b/docs/user-guide/doc-odm-user-guide/doc-odm-user-guide/notebooks/sc_rnaseq_demo.ipynb index 3ea5bb9..cd9cf0f 100644 --- a/docs/user-guide/doc-odm-user-guide/doc-odm-user-guide/notebooks/sc_rnaseq_demo.ipynb +++ b/docs/user-guide/doc-odm-user-guide/doc-odm-user-guide/notebooks/sc_rnaseq_demo.ipynb @@ -7,8 +7,7 @@ "id": "21f19756" }, "source": [ - "\n", - "\n", + "\n", "# Single-Cell RNA-Seq: Cohort Selection and Data Retrieval\n", "\n", "**Contact:** support@genestack.com \n", @@ -309,7 +308,7 @@ " Desired color\n", " \"\"\"\n", " \n", - " return go.Scatter(\n", + " return go.Scattergl(\n", " x=df[\"log2FC\"],\n", " y=df[\"-log10(pvalue)\"],\n", " mode=\"markers\",\n", @@ -504,7 +503,7 @@ "source": [ "### 3.1 Exploring Omics Query Endpoints\n", "\n", - "In ODM, each entity—such as samples, datasets, or assays—can be accessed through dedicated API endpoints. Omics query endpoints provide access to quantitative datasets such as gene expression, variant, or flow cytometry data, along with their associated metadata. These endpoints extend integration capabilities, enabling direct retrieval and filtering of omics measurements linked to specific samples or studies.\n", + "In ODM, each object can be accessed through dedicated API endpoints. Omics query endpoints provide access to quantitative datasets such as gene expression, variant, or flow cytometry data, along with their associated metadata - such as study, samples, cells. These endpoints extend integration capabilities, enabling direct retrieval and filtering of omics measurements linked to specific samples or studies.\n", "\n", "In this step, the `OmicsQueries` interface is initialized, and its available methods are listed to illustrate the range of omics data types accessible through the ODM API." ] @@ -540,7 +539,7 @@ "# initialize API class\n", "omics_api = odm_api.OmicsQueriesAsUserApi(api_client)\n", "\n", - "# list all available sample_api endpoints\n", + "# list all available omics_api endpoints\n", "for item in [item for item in dir(omics_api) if item.endswith(\"_as_user\")]:\n", " print(item)" ] @@ -577,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "ee41a848", "metadata": { "id": "ee41a848", @@ -594,7 +593,7 @@ " \"Found 5 samples matched the samples/study query (total number of found samples is 5 now)\"\n", "]\n", "\n", - "Elapsed time: 0.52 seconds\n" + "Elapsed time: 0.56 seconds\n" ] } ], @@ -602,8 +601,6 @@ "# define search parameters\n", "study_query = \"single-nuclei\"\n", "\n", - "# if you are not using the provided template, remove the range filter \"Donor Age\"\n", - "# for the following command to work\n", "sample_filter = \" AND \".join([\n", " 'Species=\"Homo sapiens\"',\n", " 'Tissue=\"cardiac muscle tissue\"',\n", @@ -676,7 +673,7 @@ "type": "string" } ], - "ref": "a04033a0-118a-47ef-99b3-a50461422e39", + "ref": "dd19093f-fdeb-4ac9-aad6-3eb89580d4e1", "rows": [ [ "Posterior Wall Thickness Value", @@ -1298,11 +1295,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 10548 cells in sample GSF044556 in 1.66 seconds\n", - "Found 7658 cells in sample GSF044576 in 0.62 seconds\n", - "Found 10367 cells in sample GSF044582 in 2.74 seconds\n", - "Found 9778 cells in sample GSF044583 in 3.2 seconds\n", - "Found 7676 cells in sample GSF044590 in 1.29 seconds\n" + "Found 10548 cells in sample GSF044556 in 4.18 seconds\n", + "Found 7658 cells in sample GSF044576 in 3.18 seconds\n", + "Found 10367 cells in sample GSF044582 in 3.12 seconds\n", + "Found 9778 cells in sample GSF044583 in 2.81 seconds\n", + "Found 7676 cells in sample GSF044590 in 2.27 seconds\n" ] } ], @@ -1421,16 +1418,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 17822 cells with gene TNNT2 in 16.18 seconds\n", - "Found 13991 cells with gene DCN in 7.92 seconds\n", - "Found 6593 cells with gene PECAM1 in 4.67 seconds\n", - "Found 1968 cells with gene CD163 in 4.34 seconds\n", - "Found 5693 cells with gene PDGFRB in 4.71 seconds\n", - "Found 1139 cells with gene FABP4 in 2.88 seconds\n", - "Found 6643 cells with gene MAP2 in 4.88 seconds\n", - "Found 806 cells with gene CD4 in 2.71 seconds\n", - "Found 140 cells with gene CD8A in 2.57 seconds\n", - "Found 140 cells with gene CPA3 in 2.47 seconds\n" + "Found 17822 cells with gene TNNT2 in 24.16 seconds\n", + "Found 13991 cells with gene DCN in 16.32 seconds\n", + "Found 6593 cells with gene PECAM1 in 9.97 seconds\n", + "Found 1968 cells with gene CD163 in 4.35 seconds\n", + "Found 5693 cells with gene PDGFRB in 7.91 seconds\n", + "Found 1139 cells with gene FABP4 in 3.51 seconds\n", + "Found 6643 cells with gene MAP2 in 8.32 seconds\n", + "Found 806 cells with gene CD4 in 3.31 seconds\n", + "Found 140 cells with gene CD8A in 3.52 seconds\n", + "Found 140 cells with gene CPA3 in 4.34 seconds\n" ] } ], @@ -1477,7 +1474,7 @@ "metadata": {}, "source": [ "#### 3.4.2 Visualization\n", - "The purpose of this section is to validate the cell type assignment by visualizing the expression of selected cell type-specific markers. We will create an adata object with both cells metadata and expression data, and visualize the expression of the selected genes using the `pl.umap` and `pl.violin` plot functions from `Scanpy` package." + "The purpose of this section is to validate the cell type assignment by visualizing the expression of selected cell type-specific markers. We will create an `AnnData` object with both cells metadata and expression data, and visualize the expression of the selected genes using the `pl.umap` and `pl.violin` plot functions from `Scanpy` package." ] }, { @@ -1517,8 +1514,7 @@ " color=genes,\n", " ncols=2,\n", " size=50,\n", - " color_map='viridis',\n", - " show=True\n", + " color_map='viridis'\n", ")" ] }, @@ -1637,8 +1633,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Retrieved cell ratio statistics for Activated_fibroblast cell_type in 0.52 seconds\n", - "Retrieved cell ratio statistics for Fibroblast_I cell_type in 0.45 seconds\n" + "Retrieved cell ratio statistics for Activated_fibroblast cell_type in 0.51 seconds\n", + "Retrieved cell ratio statistics for Fibroblast_I cell_type in 0.47 seconds\n" ] }, { @@ -1671,7 +1667,7 @@ "type": "string" } ], - "ref": "ac4a0f99-ad7a-4e6a-ba90-18b6b651361d", + "ref": "ffe63472-a947-4fec-bc99-67da510bf865", "rows": [ [ "1", @@ -1831,16 +1827,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Retrieved differential gene expression for 28879 genes in 86.06 seconds\n" + "Retrieved differential gene expression for 28879 genes in 88.38 seconds\n" ] }, { "data": { "text/html": [ "\n", - "
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