diff --git a/docs/multimodal.ipynb b/docs/multimodal.ipynb index 436364c..b55e283 100644 --- a/docs/multimodal.ipynb +++ b/docs/multimodal.ipynb @@ -14,6 +14,30 @@ "# Validate & register multi-modal data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Background" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "scRNA data has moved beyond just RNA and can also include the measurements of other modalities such as chromatin accessibility, surface proteins or adaptive immune receptors.\n", + "ECCITE-seq is designed to enable interrogation of single-cell transcriptomes together with surface protein markers in the context of CRISPR screens.\n", + "\n", + "Here, we'll showcase how to curate and register ECCITE-seq data from [Papalexi21](https://www.nature.com/articles/s41592-019-0392-0) in the form of [MuData](https://github.com/scverse/mudata) objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, { "cell_type": "code", "execution_count": null, @@ -55,7 +79,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## MuData object" + "## Papalexi21" ] }, { @@ -65,6 +89,13 @@ "Let's use a MuData object:" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transform ![](https://img.shields.io/badge/Transform-10b981)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -75,16 +106,16 @@ }, "outputs": [], "source": [ - "mdata = ln.dev.datasets.mudata_papalexi21_subset()" + "mdata = ln.dev.datasets.mudata_papalexi21_subset()\n", + "mdata" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "mdata" + "MuData objects build on top of AnnData objects to store and serialize multimodal data.\n", + "More information can be found on the [MuData documentation](https://mudata.readthedocs.io/en/latest/)." ] }, { @@ -110,16 +141,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Register features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's register the 3 feature sets this data contains:\n", - "1. rna\n", - "2. adt\n", + "Now let's validate and register the 3 feature sets this data contains:\n", + "1. RNA (gene expression)\n", + "2. ADT (antibody derived tags reflecting surface proteins)\n", "3. obs (metadata)" ] }, @@ -127,14 +151,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### modalities" + "For the two modalities rna and adt, we use bionty tables as the reference:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For the two modalities rna and adt, we use bionty tables as the reference:" + "### Validate ![](https://img.shields.io/badge/Validate-10b981)" ] }, { @@ -253,6 +277,13 @@ "lb.CellMarker.validate(mdata[\"adt\"].var_names, field=lb.CellMarker.name);" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register ![](https://img.shields.io/badge/Register-10b981) " + ] + }, { "cell_type": "code", "execution_count": null, @@ -281,13 +312,6 @@ "file.features.add_feature_set(feature_set_adt, slot=\"adt\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### metadata" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -440,7 +464,7 @@ ], "metadata": { "kernelspec": { - "display_name": "py39", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -454,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.12" }, "nbproject": { "id": "yMWSFirS6qv2", @@ -468,5 +492,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }