{ "cells": [ { "cell_type": "markdown", "id": "61085d1a", "metadata": {}, "source": [ "# oggmap: Step 4 - other evolutionary indices\n", "\n", "This notebook will demonstrate how to to add a other evolutionary indices to scRNA data." ] }, { "cell_type": "markdown", "id": "921e8052", "metadata": {}, "source": [ "## Notebook file\n", "\n", "Notebook file can be obtained here:\n", "\n", "[https://raw.githubusercontent.com/kullrich/oggmap/main/docs/notebooks/evolutionary_indices.ipynb](https://raw.githubusercontent.com/kullrich/oggmap/main/docs/notebooks/evolutionary_indices.ipynb)" ] }, { "cell_type": "markdown", "id": "52bac613", "metadata": {}, "source": [ "## Import libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "24cf6855", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import scanpy as sc\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from statannot import add_stat_annotation\n", "# increase dpi\n", "%matplotlib inline\n", "#plt.rcParams['figure.dpi'] = 300\n", "#plt.rcParams['savefig.dpi'] = 300\n", "plt.rcParams['figure.figsize'] = [6, 4.5]\n", "#plt.rcParams['figure.figsize'] = [4.4, 3.3]" ] }, { "cell_type": "markdown", "id": "dfcf5fed", "metadata": {}, "source": [ "## Import oggmap python package submodules" ] }, { "cell_type": "code", "execution_count": 2, "id": "20cab03e", "metadata": {}, "outputs": [], "source": [ "# import submodules\n", "from oggmap import qlin, gtf2t2g, of2orthomap, orthomap2tei, datasets, ncbitax" ] }, { "cell_type": "markdown", "id": "aaa06d2e", "metadata": {}, "source": [ "## Step 0, Step 1, Step 2 and Step 3" ] }, { "cell_type": "markdown", "id": "a5ef1a54", "metadata": {}, "source": [ "In order to come to Step 4, TEI calculation, one needs to have the results from Step 0, Step 1, Step 2 and Step 3.\n", "\n", "The query species in this part is: __*Caenorhabditis elegans*__ (nematode).\n", "\n", "**Note:** In this tutorial Step 0 and Step 2 are different since other evolutionary indices will be used to weight gene expression. It does not need to be a gene age class but can be any discrete or continuous gene based measurement.\n", "\n", "Other evolutionary indices can be e.g.:\n", "\n", "- Tajima'sD\n", "- Nucleotide diversity (within species)\n", "- Nucleotide divergence (between species)\n", "- F-statistics\n", "\n", "Please have a look at the documentation of [Step 0 - run OrthoFinder](https://oggmap.readthedocs.io/en/latest/tutorials/orthofinder.html) to get to know what information and files are mandatory to extract gene age classes from [OrthoFinder](https://oggmap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) results.\n", "\n", "In [Step 1 - get taxonomic information](https://oggmap.readthedocs.io/en/latest/tutorials/query_lineage.html) you have already been introduced how to extract query lineage information with `oggmap` and the `qlin.get_qlin()` function.\n", "\n", "In [Step 2 - gene age class assignment](https://oggmap.readthedocs.io/en/latest/tutorials/get_orthomap.html) you have already been introduced how to extract an orthomap (gene age class) from [OrthoFinder](https://oggmap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) results with `oggmap` and the `of2orthomap.get_orthomap()` function or how to import pre-calculated orthomaps with the `orthomap2tei.read_orthomap()` function.\n", "\n", "In [Step 3 - map gene/transcript IDs](https://oggmap.readthedocs.io/en/latest/tutorials/geneset_overlap.html) you have already been introduced how to extract gene IDs from `GTF` file with `orthoamp` and the `gtf2t2g.parse_gtf()` function. You have also been introduced how to use the `orthomap2tei.geneset_overlap()` function to check the overlap between the gene IDs and have learned how to use the `orthomap2tei.replace_by()` function to e.g. reduce isoform gene IDs to gene IDs." ] }, { "cell_type": "markdown", "id": "9a708305", "metadata": {}, "source": [ "## Step 0 - Use different pre-calculated evolutionary indices\n", "\n", "Diversity parameter were pre-calculated ([Ma et al., 2021](https://doi.org/10.1093/gbe/evab048)) and is available here:\n", "\n", "[https://doi.org/10.5281/zenodo.7242263](https://doi.org/10.5281/zenodo.7242263)\n", "\n", "or can be accessed with the `dataset` submodule of `oggmap`\n", "\n", "`datasets.ma21_fst(datapath='data')` (download folder set to `'data'`)." ] }, { "cell_type": "code", "execution_count": 3, "id": "9c44e81f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100% [..........................................................................] 1049100 / 1049100" ] }, { "data": { "text/plain": [ "'data/Ma2021_Fst.tsv'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datasets.ma21_fst(datapath='data')" ] }, { "cell_type": "markdown", "id": "a54cf9a1", "metadata": {}, "source": [ "### Step 1 - get taxonomic information\n", "\n", "Please have a look at the documentation of [Step 1 - get taxonomic information](https://oggmap.readthedocs.io/en/latest/tutorials/query_lineage.html) to get further insides." ] }, { "cell_type": "code", "execution_count": 4, "id": "cbc0127d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "query name: Caenorhabditis elegans\n", "query taxID: 6239\n", "query kingdom: Eukaryota\n", "query lineage names: \n", "['root(1)', 'cellular organisms(131567)', 'Eukaryota(2759)', 'Opisthokonta(33154)', 'Metazoa(33208)', 'Eumetazoa(6072)', 'Bilateria(33213)', 'Protostomia(33317)', 'Ecdysozoa(1206794)', 'Nematoda(6231)', 'Chromadorea(119089)', 'Rhabditida(6236)', 'Rhabditina(2301116)', 'Rhabditomorpha(2301119)', 'Rhabditoidea(55879)', 'Rhabditidae(6243)', 'Peloderinae(55885)', 'Caenorhabditis(6237)', 'Caenorhabditis elegans(6239)']\n", "query lineage: \n", "[1, 131567, 2759, 33154, 33208, 6072, 33213, 33317, 1206794, 6231, 119089, 6236, 2301116, 2301119, 55879, 6243, 55885, 6237, 6239]\n" ] } ], "source": [ "# get query species taxonomic lineage information\n", "query_lineage = qlin.get_qlin(q='Caenorhabditis elegans', dbname='data/taxadb.sqlite')" ] }, { "cell_type": "markdown", "id": "04f326a4", "metadata": {}, "source": [ "## Step 2 - gene based measurement (query species evolutionary index)\n", "\n", "Here, an other evolutionary index will be used to weight gene expression. It does not need to be a gene age class but can be any discrete or continuous gene based measurement. Continuous values can be binned first and\n", "used as gene groups to weigh expression.\n", "\n", "Other evolutionary indices can be e.g.:\n", "\n", "- Tajima'sD\n", "- Nucleotide diversity (within species)\n", "- Nucleotide divergence (between species)\n", "- F-statistics" ] }, { "cell_type": "code", "execution_count": 5, "id": "03210014", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WormBase_IDChrGeneTajimaDNormalizedPiFayWuFST
0WBGene00000001Iaap-1-0.69570.0002-1.25750.8062
1WBGene00000002IVaat-1-0.47240.0001-1.46280.8846
2WBGene00000003Vaat-2-1.52660.00010.08160.1691
3WBGene00000004Xaat-3-1.64010.0003-4.76850.8129
4WBGene00000005IVaat-4-1.21370.0006-0.76170.3725
........................
20217WBGene00271701XF10D7.10-0.74280.0000-1.93080.1111
20218WBGene00271703IIIZK1010.12-1.33860.0008-3.25280.7683
20219WBGene00271706IID2089.8-0.83120.00120.44890.5551
20220WBGene00271707VZK105.14-1.07480.0004-3.20690.6433
20221WBGene00271715IIIB0244.17-0.66170.0010-1.18700.6556
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20222 rows × 7 columns

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" ], "text/plain": [ " WormBase_ID Chr Gene TajimaD NormalizedPi FayWu FST\n", "0 WBGene00000001 I aap-1 -0.6957 0.0002 -1.2575 0.8062\n", "1 WBGene00000002 IV aat-1 -0.4724 0.0001 -1.4628 0.8846\n", "2 WBGene00000003 V aat-2 -1.5266 0.0001 0.0816 0.1691\n", "3 WBGene00000004 X aat-3 -1.6401 0.0003 -4.7685 0.8129\n", "4 WBGene00000005 IV aat-4 -1.2137 0.0006 -0.7617 0.3725\n", "... ... ... ... ... ... ... ...\n", "20217 WBGene00271701 X F10D7.10 -0.7428 0.0000 -1.9308 0.1111\n", "20218 WBGene00271703 III ZK1010.12 -1.3386 0.0008 -3.2528 0.7683\n", "20219 WBGene00271706 II D2089.8 -0.8312 0.0012 0.4489 0.5551\n", "20220 WBGene00271707 V ZK105.14 -1.0748 0.0004 -3.2069 0.6433\n", "20221 WBGene00271715 III B0244.17 -0.6617 0.0010 -1.1870 0.6556\n", "\n", "[20222 rows x 7 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get query species Fst values\n", "\n", "# download pre-calculated Fst values here: https://doi.org/10.5281/zenodo.7242263\n", "# or download with datasets.ma21_fst(datapath='data')\n", "query_fst = pd.read_csv('data/Ma2021_Fst.tsv', delimiter='\\t')\n", "query_fst" ] }, { "cell_type": "markdown", "id": "6689d2a5", "metadata": {}, "source": [ "### Group evolutionary indices into bins" ] }, { "cell_type": "code", "execution_count": 6, "id": "49893c46", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WormBase_IDChrGeneTajimaDNormalizedPiFayWuFSTTajimaD_binnedTajimaD_binsNormalizedPi_binnedNormalizedPi_binsFayWu_binnedFayWu_binsFST_binnedFST_bins
0WBGene00000001Iaap-1-0.69570.0002-1.25750.80628.0-0.84 >= x < -0.673683.00.0002 >= x < 0.00045.0-1.68402 >= x < -0.991355.00.6286 < x
1WBGene00000002IVaat-1-0.47240.0001-1.46280.88469.0-0.67368 >= x < -0.208419999999999722.00.0001 >= x < 0.00025.0-1.68402 >= x < -0.991355.00.6286 < x
2WBGene00000003Vaat-2-1.52660.00010.08160.16913.0-1.6187 >= x < -1.46024666666666672.00.0001 >= x < 0.00028.00.03219333333333343 >= x < 0.101820000000000083.00.1347 >= x < 0.3595
3WBGene00000004Xaat-3-1.64010.0003-4.76850.81292.0-1.8383 >= x < -1.61873.00.0002 >= x < 0.00042.0-9.455993333333332 >= x < -3.95129999999999945.00.6286 < x
4WBGene00000005IVaat-4-1.21370.0006-0.76170.37255.0-1.3196 >= x < -1.16564.00.0004 >= x < 0.00116.0-0.99135 >= x < 0.04.00.3595 >= x < 0.6286
................................................
20217WBGene00271701XF10D7.10-0.74280.0000-1.93080.11118.0-0.84 >= x < -0.673681.0x < 0.00014.0-2.277410000000001 >= x < -1.684022.00.0 >= x < 0.1347
20218WBGene00271703IIIZK1010.12-1.33860.0008-3.25280.76834.0-1.4602466666666667 >= x < -1.31964.00.0004 >= x < 0.00113.0-3.9512999999999994 >= x < -2.2774100000000015.00.6286 < x
20219WBGene00271706IID2089.8-0.83120.00120.44890.55518.0-0.84 >= x < -0.673685.00.0011 < x10.00.2674900000000009 < x4.00.3595 >= x < 0.6286
20220WBGene00271707VZK105.14-1.07480.0004-3.20690.64336.0-1.1656 >= x < -1.04164.00.0004 >= x < 0.00113.0-3.9512999999999994 >= x < -2.2774100000000015.00.6286 < x
20221WBGene00271715IIIB0244.17-0.66170.0010-1.18700.65569.0-0.67368 >= x < -0.208419999999999724.00.0004 >= x < 0.00115.0-1.68402 >= x < -0.991355.00.6286 < x
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20222 rows × 15 columns

\n", "
" ], "text/plain": [ " WormBase_ID Chr Gene TajimaD NormalizedPi FayWu FST \\\n", "0 WBGene00000001 I aap-1 -0.6957 0.0002 -1.2575 0.8062 \n", "1 WBGene00000002 IV aat-1 -0.4724 0.0001 -1.4628 0.8846 \n", "2 WBGene00000003 V aat-2 -1.5266 0.0001 0.0816 0.1691 \n", "3 WBGene00000004 X aat-3 -1.6401 0.0003 -4.7685 0.8129 \n", "4 WBGene00000005 IV aat-4 -1.2137 0.0006 -0.7617 0.3725 \n", "... ... ... ... ... ... ... ... \n", "20217 WBGene00271701 X F10D7.10 -0.7428 0.0000 -1.9308 0.1111 \n", "20218 WBGene00271703 III ZK1010.12 -1.3386 0.0008 -3.2528 0.7683 \n", "20219 WBGene00271706 II D2089.8 -0.8312 0.0012 0.4489 0.5551 \n", "20220 WBGene00271707 V ZK105.14 -1.0748 0.0004 -3.2069 0.6433 \n", "20221 WBGene00271715 III B0244.17 -0.6617 0.0010 -1.1870 0.6556 \n", "\n", " TajimaD_binned TajimaD_bins \\\n", "0 8.0 -0.84 >= x < -0.67368 \n", "1 9.0 -0.67368 >= x < -0.20841999999999972 \n", "2 3.0 -1.6187 >= x < -1.4602466666666667 \n", "3 2.0 -1.8383 >= x < -1.6187 \n", "4 5.0 -1.3196 >= x < -1.1656 \n", "... ... ... \n", "20217 8.0 -0.84 >= x < -0.67368 \n", "20218 4.0 -1.4602466666666667 >= x < -1.3196 \n", "20219 8.0 -0.84 >= x < -0.67368 \n", "20220 6.0 -1.1656 >= x < -1.0416 \n", "20221 9.0 -0.67368 >= x < -0.20841999999999972 \n", "\n", " NormalizedPi_binned NormalizedPi_bins FayWu_binned \\\n", "0 3.0 0.0002 >= x < 0.0004 5.0 \n", "1 2.0 0.0001 >= x < 0.0002 5.0 \n", "2 2.0 0.0001 >= x < 0.0002 8.0 \n", "3 3.0 0.0002 >= x < 0.0004 2.0 \n", "4 4.0 0.0004 >= x < 0.0011 6.0 \n", "... ... ... ... \n", "20217 1.0 x < 0.0001 4.0 \n", "20218 4.0 0.0004 >= x < 0.0011 3.0 \n", "20219 5.0 0.0011 < x 10.0 \n", "20220 4.0 0.0004 >= x < 0.0011 3.0 \n", "20221 4.0 0.0004 >= x < 0.0011 5.0 \n", "\n", " FayWu_bins FST_binned \\\n", "0 -1.68402 >= x < -0.99135 5.0 \n", "1 -1.68402 >= x < -0.99135 5.0 \n", "2 0.03219333333333343 >= x < 0.10182000000000008 3.0 \n", "3 -9.455993333333332 >= x < -3.9512999999999994 5.0 \n", "4 -0.99135 >= x < 0.0 4.0 \n", "... ... ... \n", "20217 -2.277410000000001 >= x < -1.68402 2.0 \n", "20218 -3.9512999999999994 >= x < -2.277410000000001 5.0 \n", "20219 0.2674900000000009 < x 4.0 \n", "20220 -3.9512999999999994 >= x < -2.277410000000001 5.0 \n", "20221 -1.68402 >= x < -0.99135 5.0 \n", "\n", " FST_bins \n", "0 0.6286 < x \n", "1 0.6286 < x \n", "2 0.1347 >= x < 0.3595 \n", "3 0.6286 < x \n", "4 0.3595 >= x < 0.6286 \n", "... ... \n", "20217 0.0 >= x < 0.1347 \n", "20218 0.6286 < x \n", "20219 0.3595 >= x < 0.6286 \n", "20220 0.6286 < x \n", "20221 0.6286 < x \n", "\n", "[20222 rows x 15 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# see here for additional quantile methods: https://numpy.org/doc/stable/reference/generated/numpy.nanquantile.html\n", "\n", "orthomap2tei.get_bins(tobin_df=query_fst,\n", " bincol='TajimaD',\n", " q=[.1, .2, .3, .4, .5, .6, .7, .8, .9],\n", " method='median_unbiased')\n", "orthomap2tei.get_bins(tobin_df=query_fst,\n", " bincol='NormalizedPi',\n", " q=[.2, .4, .6, .8],\n", " method='median_unbiased')\n", "orthomap2tei.get_bins(tobin_df=query_fst,\n", " bincol='FayWu',\n", " q=[.1, .2, .3, .4, .5, .6, .7, .8, .9],\n", " method='median_unbiased')\n", "orthomap2tei.get_bins(tobin_df=query_fst,\n", " bincol='FST',\n", " q=[.2, .4, .6, .8],\n", " method='median_unbiased')" ] }, { "cell_type": "markdown", "id": "6a90f68a", "metadata": {}, "source": [ "### Gene assignments per query species evolutionary index\n", "\n", "Given an orthomap, one can get an overview of the gene assignments per query species lineage node.\n", "\n", "The `oggmap` submodule `of2orhomap` and the `of2orthomap.get_counts_per_ps()` function will show the distribution of the gene age classes and can be further visualized as follows:" ] }, { "cell_type": "code", "execution_count": 7, "id": "322d4493", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# show count per TajimaD group (TajimaD_binned)\n", "of2orthomap.get_counts_per_ps(omap_df=query_fst,\n", " psnum_col='TajimaD_binned',\n", " pstaxid_col=None,\n", " psname_col=None)\n", "\n", "# bar plot count per taxonomic group (PSname)\n", "ax = of2orthomap.get_counts_per_ps(omap_df=query_fst,\n", " psnum_col='TajimaD_binned',\n", " pstaxid_col=None,\n", " psname_col=None).plot.bar(y='counts', x='TajimaD_binned')\n", "ax.set_title('C. elegans - Number of genes per TajimaD class')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "07fbb3ef", "metadata": {}, "source": [ "## Step 3 - map OrthoFinder gene names and scRNA gene/transcript names\n", "\n", "To be able to link gene ages assignments from an orthomap and gene or transcript of scRNA dataset, one needs to check the overlap of the annotated gene names. With the `gtf2t2g` submodule of `oggmap` and the `gtf2t2g.parse_gtf()` function, one can extract gene and transcript names from a given gene feature file (`GTF`).\n", "\n", "If in your case gene or transcript IDs between an orthomap and scRNA data do not match directly, please have a look at a detailed how-to to match them:\n", "\n", "https://oggmap.readthedocs.io/en/latest/tutorials/geneset_overlap.html\n", "\n", "Here, pre-calculated diversity parameter gene names already overlap, so no `GTF` import is necessary ([Ma et al., 2021](https://doi.org/10.1093/gbe/evab048))." ] }, { "cell_type": "markdown", "id": "49f5222c", "metadata": {}, "source": [ "### Import now, the scRNA dataset of the query species\n", "\n", "Here, data is used, like in the publication ([Packer and Zhu al., 2019](https://doi.org/10.1126/science.aax1971)).\n", "\n", "scRNA data was downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126954 converted into Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy or `oggmap` package and is available here:\n", "\n", "https://doi.org/10.5281/zenodo.7245547\n", "\n", "or can be accessed with the `dataset` submodule of `oggmap`:\n", "\n", "`datasets.packer19(datapath='data')` (download folder set to `'data'`).\n", "\n", "__Note:__ A smaller scRNA data set for the same data exist and can be obtained via:\n", "\n", "`datasets.packer19_small(datapath='data')` (download folder set to `'data'`)." ] }, { "cell_type": "code", "execution_count": 8, "id": "05c4035a", "metadata": {}, "outputs": [], "source": [ "# load scRNA data\n", "\n", "# download zebrafish scRNA data here: https://doi.org/10.5281/zenodo.7245547\n", "# or download with datasets.packer19(datapath='data')\n", "\n", "#celegans_data = datasets.packer19(datapath='data')\n", "celegans_data = sc.read('data/GSE126954.h5ad')" ] }, { "cell_type": "markdown", "id": "462b0ab5", "metadata": {}, "source": [ "### Get an overview of observations" ] }, { "cell_type": "code", "execution_count": 9, "id": "f67c1782", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 89701 × 20222\n", " obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'cell', 'n.umi', 'time.point', 'batch', 'Size_Factor', 'cell.type', 'cell.subtype', 'plot.cell.type', 'raw.embryo.time', 'embryo.time', 'embryo.time.bin', 'raw.embryo.time.bin', 'lineage', 'passed_initial_QC_or_later_whitelisted'\n", " var: 'features', 'genes'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "celegans_data" ] }, { "cell_type": "code", "execution_count": 10, "id": "9061dd92", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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orig.identnCount_RNAnFeature_RNAcelln.umitime.pointbatchSize_Factorcell.typecell.subtypeplot.cell.typeraw.embryo.timeembryo.timeembryo.time.binraw.embryo.time.binlineagepassed_initial_QC_or_later_whitelisted
AAACCTGAGACAATAC-300.1.101630.0781AAACCTGAGACAATAC-300.1.11630300_minutesWaterston_300_minutes1.023195Body_wall_muscleBWM_head_row_1BWM_head_row_1360380.0330-390330-390MSxpappp1
AAACCTGAGGGCTCTC-300.1.102323.01116AAACCTGAGGGCTCTC-300.1.12319300_minutesWaterston_300_minutes1.458210NANANA260220.0210-270210-270MSxapaap1
AAACCTGAGTGCGTGA-300.1.103725.01322AAACCTGAGTGCGTGA-300.1.13719300_minutesWaterston_300_minutes2.338283NANANA270230.0210-270270-330NA1
AAACCTGAGTTGAGTA-300.1.104236.01747AAACCTGAGTTGAGTA-300.1.14251300_minutesWaterston_300_minutes2.659051Body_wall_muscleBWM_anteriorBWM_anterior260280.0270-330210-270Dxap1
AAACCTGCAAGACGTG-300.1.101003.0621AAACCTGCAAGACGTG-300.1.11003300_minutesWaterston_300_minutes0.629610Ciliated_amphid_neuronAFDAFD350350.0330-390330-390ABalpppapav/ABpraaaapav1
......................................................
TCTGAGACATGTCGAT-b020581.0361TCTGAGACATGTCGAT-b02585mixedMurray_b020.364709Rectal_glandRectal_glandRectal_gland390700.0> 650390-450NA1
TCTGAGACATGTCTCC-b020516.0327TCTGAGACATGTCTCC-b02510mixedMurray_b020.323907NANANA510470.0450-510510-580NA1
TGGCCAGCACGAAGCA-b020843.0543TGGCCAGCACGAAGCA-b02843mixedMurray_b020.529174NANANA400470.0450-510390-450NA1
TGGCGCACAGGCAGTA-b020634.0397TGGCGCACAGGCAGTA-b02636mixedMurray_b020.397979NANANA330350.0330-390330-390NA1
TGGGCGTTCAGGCCCA-b0201126.0702TGGGCGTTCAGGCCCA-b021132mixedMurray_b020.706820NANANA260265.0210-270210-270NA1
\n", "

89701 rows × 17 columns

\n", "
" ], "text/plain": [ " orig.ident nCount_RNA nFeature_RNA \\\n", "AAACCTGAGACAATAC-300.1.1 0 1630.0 781 \n", "AAACCTGAGGGCTCTC-300.1.1 0 2323.0 1116 \n", "AAACCTGAGTGCGTGA-300.1.1 0 3725.0 1322 \n", "AAACCTGAGTTGAGTA-300.1.1 0 4236.0 1747 \n", "AAACCTGCAAGACGTG-300.1.1 0 1003.0 621 \n", "... ... ... ... \n", "TCTGAGACATGTCGAT-b02 0 581.0 361 \n", "TCTGAGACATGTCTCC-b02 0 516.0 327 \n", "TGGCCAGCACGAAGCA-b02 0 843.0 543 \n", "TGGCGCACAGGCAGTA-b02 0 634.0 397 \n", "TGGGCGTTCAGGCCCA-b02 0 1126.0 702 \n", "\n", " cell n.umi time.point \\\n", "AAACCTGAGACAATAC-300.1.1 AAACCTGAGACAATAC-300.1.1 1630 300_minutes \n", "AAACCTGAGGGCTCTC-300.1.1 AAACCTGAGGGCTCTC-300.1.1 2319 300_minutes \n", "AAACCTGAGTGCGTGA-300.1.1 AAACCTGAGTGCGTGA-300.1.1 3719 300_minutes \n", "AAACCTGAGTTGAGTA-300.1.1 AAACCTGAGTTGAGTA-300.1.1 4251 300_minutes \n", "AAACCTGCAAGACGTG-300.1.1 AAACCTGCAAGACGTG-300.1.1 1003 300_minutes \n", "... ... ... ... \n", "TCTGAGACATGTCGAT-b02 TCTGAGACATGTCGAT-b02 585 mixed \n", "TCTGAGACATGTCTCC-b02 TCTGAGACATGTCTCC-b02 510 mixed \n", "TGGCCAGCACGAAGCA-b02 TGGCCAGCACGAAGCA-b02 843 mixed \n", "TGGCGCACAGGCAGTA-b02 TGGCGCACAGGCAGTA-b02 636 mixed \n", "TGGGCGTTCAGGCCCA-b02 TGGGCGTTCAGGCCCA-b02 1132 mixed \n", "\n", " batch Size_Factor \\\n", "AAACCTGAGACAATAC-300.1.1 Waterston_300_minutes 1.023195 \n", "AAACCTGAGGGCTCTC-300.1.1 Waterston_300_minutes 1.458210 \n", "AAACCTGAGTGCGTGA-300.1.1 Waterston_300_minutes 2.338283 \n", "AAACCTGAGTTGAGTA-300.1.1 Waterston_300_minutes 2.659051 \n", "AAACCTGCAAGACGTG-300.1.1 Waterston_300_minutes 0.629610 \n", "... ... ... \n", "TCTGAGACATGTCGAT-b02 Murray_b02 0.364709 \n", "TCTGAGACATGTCTCC-b02 Murray_b02 0.323907 \n", "TGGCCAGCACGAAGCA-b02 Murray_b02 0.529174 \n", "TGGCGCACAGGCAGTA-b02 Murray_b02 0.397979 \n", "TGGGCGTTCAGGCCCA-b02 Murray_b02 0.706820 \n", "\n", " cell.type cell.subtype \\\n", "AAACCTGAGACAATAC-300.1.1 Body_wall_muscle BWM_head_row_1 \n", "AAACCTGAGGGCTCTC-300.1.1 NA NA \n", "AAACCTGAGTGCGTGA-300.1.1 NA NA \n", "AAACCTGAGTTGAGTA-300.1.1 Body_wall_muscle BWM_anterior \n", "AAACCTGCAAGACGTG-300.1.1 Ciliated_amphid_neuron AFD \n", "... ... ... \n", "TCTGAGACATGTCGAT-b02 Rectal_gland Rectal_gland \n", "TCTGAGACATGTCTCC-b02 NA NA \n", "TGGCCAGCACGAAGCA-b02 NA NA \n", "TGGCGCACAGGCAGTA-b02 NA NA \n", "TGGGCGTTCAGGCCCA-b02 NA NA \n", "\n", " plot.cell.type raw.embryo.time embryo.time \\\n", "AAACCTGAGACAATAC-300.1.1 BWM_head_row_1 360 380.0 \n", "AAACCTGAGGGCTCTC-300.1.1 NA 260 220.0 \n", "AAACCTGAGTGCGTGA-300.1.1 NA 270 230.0 \n", "AAACCTGAGTTGAGTA-300.1.1 BWM_anterior 260 280.0 \n", "AAACCTGCAAGACGTG-300.1.1 AFD 350 350.0 \n", "... ... ... ... \n", "TCTGAGACATGTCGAT-b02 Rectal_gland 390 700.0 \n", "TCTGAGACATGTCTCC-b02 NA 510 470.0 \n", "TGGCCAGCACGAAGCA-b02 NA 400 470.0 \n", "TGGCGCACAGGCAGTA-b02 NA 330 350.0 \n", "TGGGCGTTCAGGCCCA-b02 NA 260 265.0 \n", "\n", " embryo.time.bin raw.embryo.time.bin \\\n", "AAACCTGAGACAATAC-300.1.1 330-390 330-390 \n", "AAACCTGAGGGCTCTC-300.1.1 210-270 210-270 \n", "AAACCTGAGTGCGTGA-300.1.1 210-270 270-330 \n", "AAACCTGAGTTGAGTA-300.1.1 270-330 210-270 \n", "AAACCTGCAAGACGTG-300.1.1 330-390 330-390 \n", "... ... ... \n", "TCTGAGACATGTCGAT-b02 > 650 390-450 \n", "TCTGAGACATGTCTCC-b02 450-510 510-580 \n", "TGGCCAGCACGAAGCA-b02 450-510 390-450 \n", "TGGCGCACAGGCAGTA-b02 330-390 330-390 \n", "TGGGCGTTCAGGCCCA-b02 210-270 210-270 \n", "\n", " lineage \\\n", "AAACCTGAGACAATAC-300.1.1 MSxpappp \n", "AAACCTGAGGGCTCTC-300.1.1 MSxapaap \n", "AAACCTGAGTGCGTGA-300.1.1 NA \n", "AAACCTGAGTTGAGTA-300.1.1 Dxap \n", "AAACCTGCAAGACGTG-300.1.1 ABalpppapav/ABpraaaapav \n", "... ... \n", "TCTGAGACATGTCGAT-b02 NA \n", "TCTGAGACATGTCTCC-b02 NA \n", "TGGCCAGCACGAAGCA-b02 NA \n", "TGGCGCACAGGCAGTA-b02 NA \n", "TGGGCGTTCAGGCCCA-b02 NA \n", "\n", " passed_initial_QC_or_later_whitelisted \n", "AAACCTGAGACAATAC-300.1.1 1 \n", "AAACCTGAGGGCTCTC-300.1.1 1 \n", "AAACCTGAGTGCGTGA-300.1.1 1 \n", "AAACCTGAGTTGAGTA-300.1.1 1 \n", "AAACCTGCAAGACGTG-300.1.1 1 \n", "... ... \n", "TCTGAGACATGTCGAT-b02 1 \n", "TCTGAGACATGTCTCC-b02 1 \n", "TGGCCAGCACGAAGCA-b02 1 \n", "TGGCGCACAGGCAGTA-b02 1 \n", "TGGGCGTTCAGGCCCA-b02 1 \n", "\n", "[89701 rows x 17 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "celegans_data.obs" ] }, { "cell_type": "code", "execution_count": 11, "id": "e9d67ecd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "orig.ident int32\n", "nCount_RNA float64\n", "nFeature_RNA int32\n", "cell object\n", "n.umi int32\n", "time.point category\n", "batch category\n", "Size_Factor float64\n", "cell.type category\n", "cell.subtype category\n", "plot.cell.type category\n", "raw.embryo.time int32\n", "embryo.time float64\n", "embryo.time.bin category\n", "raw.embryo.time.bin category\n", "lineage category\n", "passed_initial_QC_or_later_whitelisted int32\n", "dtype: object" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "celegans_data.obs.dtypes" ] }, { "cell_type": "markdown", "id": "a694094c", "metadata": {}, "source": [ "Prior any analysis the observations `'embryo.time.bin'` and `'batch'` will be converted into the `'category'` type. In addition a new observation `'cell.type.per.embryo.time.bin.cat'` will be created that combines sample timepoint and assigned cell type." ] }, { "cell_type": "code", "execution_count": 12, "id": "78840632", "metadata": {}, "outputs": [], "source": [ "# add embryo.time.bin as category\n", "celegans_data.obs['embryo.time.bin.cat'] = celegans_data.obs['embryo.time.bin'].astype('category')\n", "celegans_data.obs['embryo.time.bin.cat'] = celegans_data.obs['embryo.time.bin.cat'].cat.reorder_categories(['< 100',\n", " '100-130','130-170','170-210','210-270','270-330','330-390','390-450','450-510','510-580','580-650','> 650'])\n", "celegans_data.obs['batch.cat'] = celegans_data.obs['batch'].astype('category')" ] }, { "cell_type": "code", "execution_count": 13, "id": "61c22ae5", "metadata": {}, "outputs": [], "source": [ "celegans_data.obs['cell.type.per.embryo.time.bin.cat'] =\\\n", " (celegans_data.obs['cell.type'].astype('string') +\\\n", " '-' +\\\n", " celegans_data.obs['embryo.time.bin.cat'].astype('string')).astype('category')" ] }, { "cell_type": "markdown", "id": "8572de91", "metadata": {}, "source": [ "### Helper functions to match gene names\n", "\n", "The `orthomap2tei` submodule contains the `orthomap2tei.geneset_overlap()` helper function to check for gene name overlap between the constructed orthomap from `OrthoFinder` results and a given scRNA dataset." ] }, { "cell_type": "code", "execution_count": 14, "id": "eafa3c52", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
g1_g2_overlapg1_ratiog2_ratio
0202221.01.0
\n", "
" ], "text/plain": [ " g1_g2_overlap g1_ratio g2_ratio\n", "0 20222 1.0 1.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check overlap of orthomap and scRNA data \n", "orthomap2tei.geneset_overlap(celegans_data.var_names, query_fst['WormBase_ID'])" ] }, { "cell_type": "markdown", "id": "c5589347", "metadata": {}, "source": [ "## Step 4 - Get TEI values and add them to scRNA dataset\n", "\n", "Since now the gene names correspond to each other in the orthomap and the scRNA `adata` object, one can calculate the transcriptome evolutionary index (`TEI`) and add them to the scRNA dataset (`adata` object).\n", "\n", "The `TEI` measure represents the weighted arithmetic mean (expression levels as weights for the gene based measurement) over all categories.\n", "\n", "${TEI_s = \\sum (e_{is} * m_i) / \\sum e_{is}}$\n", "\n", ", where ${TEI_s}$ denotes the `TEI` value in developmental stage ${s, e_{is}}$ denotes the gene expression level of gene ${i}$ in stage ${s}$, and ${m_i}$ denotes the corresponding measurement of gene ${i, i = 1,...,N}$ and ${N = total\\ number\\ of\\ genes}$.\n", "\n", "Note: If e.g. two different isoforms would fall into two different categories, their gene measurement might differ based on the underlying calculation. However, both isoforms share the same gene name and their gene measurement would clash. In this case one can decide either to use the `keep='min'` or `keep='max'` gene measurement to be kept by the `get_tei` function, which defaults to keep in this cases the `keep='min'` or in other words the 'minimal' gene measurement." ] }, { "cell_type": "markdown", "id": "696f5ce3", "metadata": {}, "source": [ "To be able to re-use the original `count` data, they are added as a new `layer` to the `adata` object. This is useful because later on the `count` data can be used to extract either the relative expression per gene age class or re-calculate other metrics. \n", "\n", "This can be done either on un-normalized `counts`, on `normalized` and `log-transformed` data." ] }, { "cell_type": "code", "execution_count": 15, "id": "d5e91a58", "metadata": {}, "outputs": [], "source": [ "celegans_data.layers['counts'] = celegans_data.X" ] }, { "cell_type": "markdown", "id": "3b07d802", "metadata": {}, "source": [ "### add TEI to adata object\n", "\n", "Using the submodule `orthomap2tei` from `oggmap` and the `orthomap2tei.get_tei()` function, transcriptome evolutionary index (`TEI`) values are calculated and directyl added to the existing `adata` object (`add_obs=True`).\n", "\n", "There are other options to e.g. not start from the `adata.X` `counts` but from another `layer` from the `adata` object, the default is to use the `adata.X` (`layer=None`). The values can be pre-processed by the `normalize_total` option and the `log1p` option.\n", "\n", "If `add_obs=True` the resulting `TEI` values are added to the existing `adata` object as a new observation with the name set with the `obs_name` option.\n", "\n", "If `add_var=True` the gene age values are added to the existing `adata` object as a new variable with the name set with the `var_name` option.\n", "\n", "__Note:__ Genes not assigned to any gene class will get a missing assignment.\n", "\n", "If one wants to calculate bootstrap `TEI` values per cell, the `boot` option can be set to `boot=True` and gene age classes will be randomly chosen prior calculating `TEI` values `bt=10` times." ] }, { "cell_type": "markdown", "id": "0e523bb7", "metadata": {}, "source": [ "### add TajimaD, Fst and NormalizedPi to adata object" ] }, { "cell_type": "code", "execution_count": 16, "id": "bacdd642", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TajimaD
AAACCTGAGACAATAC-300.1.15.769133
AAACCTGAGGGCTCTC-300.1.15.898489
AAACCTGAGTGCGTGA-300.1.15.736505
AAACCTGAGTTGAGTA-300.1.15.795881
AAACCTGCAAGACGTG-300.1.15.746057
......
TCTGAGACATGTCGAT-b025.465485
TCTGAGACATGTCTCC-b025.543912
TGGCCAGCACGAAGCA-b025.524013
TGGCGCACAGGCAGTA-b025.652469
TGGGCGTTCAGGCCCA-b025.634361
\n", "

89701 rows × 1 columns

\n", "
" ], "text/plain": [ " TajimaD\n", "AAACCTGAGACAATAC-300.1.1 5.769133\n", "AAACCTGAGGGCTCTC-300.1.1 5.898489\n", "AAACCTGAGTGCGTGA-300.1.1 5.736505\n", "AAACCTGAGTTGAGTA-300.1.1 5.795881\n", "AAACCTGCAAGACGTG-300.1.1 5.746057\n", "... ...\n", "TCTGAGACATGTCGAT-b02 5.465485\n", "TCTGAGACATGTCTCC-b02 5.543912\n", "TGGCCAGCACGAAGCA-b02 5.524013\n", "TGGCGCACAGGCAGTA-b02 5.652469\n", "TGGGCGTTCAGGCCCA-b02 5.634361\n", "\n", "[89701 rows x 1 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add TajimaD binned values to existing adata object\n", "orthomap2tei.get_tei(adata=celegans_data,\n", " gene_id=query_fst['WormBase_ID'],\n", " gene_age=query_fst['TajimaD_binned'],\n", " keep='min',\n", " layer=None,\n", " add_var=True,\n", " var_name='TajimaD_bin',\n", " add_obs=True,\n", " obs_name='TajimaD',\n", " boot=False,\n", " bt=10,\n", " normalize_total=True,\n", " log1p=True,\n", " target_sum=1e6)" ] }, { "cell_type": "code", "execution_count": 17, "id": "33026d25", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Fst
AAACCTGAGACAATAC-300.1.12.997840
AAACCTGAGGGCTCTC-300.1.13.103704
AAACCTGAGTGCGTGA-300.1.13.100244
AAACCTGAGTTGAGTA-300.1.13.105081
AAACCTGCAAGACGTG-300.1.13.011721
......
TCTGAGACATGTCGAT-b023.011302
TCTGAGACATGTCTCC-b023.134352
TGGCCAGCACGAAGCA-b023.054067
TGGCGCACAGGCAGTA-b023.117862
TGGGCGTTCAGGCCCA-b023.149005
\n", "

89701 rows × 1 columns

\n", "
" ], "text/plain": [ " Fst\n", "AAACCTGAGACAATAC-300.1.1 2.997840\n", "AAACCTGAGGGCTCTC-300.1.1 3.103704\n", "AAACCTGAGTGCGTGA-300.1.1 3.100244\n", "AAACCTGAGTTGAGTA-300.1.1 3.105081\n", "AAACCTGCAAGACGTG-300.1.1 3.011721\n", "... ...\n", "TCTGAGACATGTCGAT-b02 3.011302\n", "TCTGAGACATGTCTCC-b02 3.134352\n", "TGGCCAGCACGAAGCA-b02 3.054067\n", "TGGCGCACAGGCAGTA-b02 3.117862\n", "TGGGCGTTCAGGCCCA-b02 3.149005\n", "\n", "[89701 rows x 1 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add Fst binned values to existing adata object\n", "orthomap2tei.get_tei(adata=celegans_data,\n", " gene_id=query_fst['WormBase_ID'],\n", " gene_age=query_fst['FST_binned'],\n", " keep='min',\n", " layer=None,\n", " add_var=True,\n", " var_name='FST_bin',\n", " add_obs=True,\n", " obs_name='Fst',\n", " boot=False,\n", " bt=10,\n", " normalize_total=True,\n", " log1p=True,\n", " target_sum=1e6)" ] }, { "cell_type": "code", "execution_count": 18, "id": "1a301756", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NormalizedPi
AAACCTGAGACAATAC-300.1.12.321107
AAACCTGAGGGCTCTC-300.1.12.527517
AAACCTGAGTGCGTGA-300.1.12.445354
AAACCTGAGTTGAGTA-300.1.12.507382
AAACCTGCAAGACGTG-300.1.12.655676
......
TCTGAGACATGTCGAT-b022.267350
TCTGAGACATGTCTCC-b022.484896
TGGCCAGCACGAAGCA-b022.378922
TGGCGCACAGGCAGTA-b022.483049
TGGGCGTTCAGGCCCA-b022.470968
\n", "

89701 rows × 1 columns

\n", "
" ], "text/plain": [ " NormalizedPi\n", "AAACCTGAGACAATAC-300.1.1 2.321107\n", "AAACCTGAGGGCTCTC-300.1.1 2.527517\n", "AAACCTGAGTGCGTGA-300.1.1 2.445354\n", "AAACCTGAGTTGAGTA-300.1.1 2.507382\n", "AAACCTGCAAGACGTG-300.1.1 2.655676\n", "... ...\n", "TCTGAGACATGTCGAT-b02 2.267350\n", "TCTGAGACATGTCTCC-b02 2.484896\n", "TGGCCAGCACGAAGCA-b02 2.378922\n", "TGGCGCACAGGCAGTA-b02 2.483049\n", "TGGGCGTTCAGGCCCA-b02 2.470968\n", "\n", "[89701 rows x 1 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add NormalizedPi binned values to existing adata object\n", "orthomap2tei.get_tei(adata=celegans_data,\n", " gene_id=query_fst['WormBase_ID'],\n", " gene_age=query_fst['NormalizedPi_binned'],\n", " keep='min',\n", " layer=None,\n", " add_var=True,\n", " var_name='NormalizedPi_bin',\n", " add_obs=True,\n", " obs_name='NormalizedPi',\n", " boot=False,\n", " bt=10,\n", " normalize_total=True,\n", " log1p=True,\n", " target_sum=1e6)" ] }, { "cell_type": "markdown", "id": "dbb627c6", "metadata": {}, "source": [ "## Step 5 - downstream analysis\n", "\n", "Once the gene age data has been added to the scRNA dataset, one can e.g. plot the corresponding transcriptome evolutionary index (`TEI`) values by any given observation pre-defined in the scRNA dataset.\n", "\n", "Here, we plot them against the assigned embryo stage and against assigned cell types of the zebrafish using the `scanpy` `sc.pl.violin()` function as follows:" ] }, { "cell_type": "markdown", "id": "4771644e", "metadata": {}, "source": [ "### Boxplot TajimaD class per sample timepoint" ] }, { "cell_type": "code", "execution_count": 20, "id": "45691ae8", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sc.pl.violin(adata=celegans_data,\n", " keys=['TajimaD'],\n", " groupby='embryo.time.bin.cat',\n", " rotation=90,\n", " palette='Paired',\n", " stripplot=False,\n", " inner='box',\n", " order=['< 100', '100-130', '130-170', '170-210',\n", " '210-270', '270-330', '330-390', '450-510',\n", " '510-580', '580-650', '> 650'],\n", " show=False,\n", " hue='embryo.time.bin.cat',\n", " legend=False)\n", "ax.set_title('C. elegans - TajimaD distribution per embryo time')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "64b28d1a", "metadata": {}, "source": [ "### Boxplot Fst class per sample timepoint" ] }, { "cell_type": "code", "execution_count": 21, "id": "d180caff", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sc.pl.violin(adata=celegans_data,\n", " keys=['Fst'],\n", " groupby='embryo.time.bin.cat',\n", " rotation=90,\n", " palette='Paired',\n", " stripplot=False,\n", " inner='box',\n", " order=['< 100', '100-130', '130-170', '170-210',\n", " '210-270', '270-330', '330-390', '450-510',\n", " '510-580', '580-650', '> 650'],\n", " show=False,\n", " hue='embryo.time.bin.cat',\n", " legend=False)\n", "ax.set_title('C. elegans - Fst distribution per embryo time')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3101c426", "metadata": {}, "source": [ "### Boxplot NormalizedPi class per sample timepoint" ] }, { "cell_type": "code", "execution_count": 22, "id": "efed5b36", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sc.pl.violin(adata=celegans_data,\n", " keys=['NormalizedPi'],\n", " groupby='embryo.time.bin.cat',\n", " rotation=90,\n", " palette='Paired',\n", " stripplot=False,\n", " inner='box',\n", " order=['< 100', '100-130', '130-170', '170-210',\n", " '210-270', '270-330', '330-390', '450-510',\n", " '510-580', '580-650', '> 650'],\n", " show=False,\n", " hue='embryo.time.bin.cat',\n", " legend=False)\n", "ax.set_title('C. elegans - NormalizedPi distribution per embryo time')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d8774430", "metadata": {}, "source": [ "Please have a look at the documentation of the [nematode example](https://oggmap.readthedocs.io/en/latest/tutorials/nematode_example.html) to see more downstream analysis e.g. how to compare gene age and other evolutionary indices of the same scRNA data or have a look at the documentation for other [case studies](https://oggmap.readthedocs.io/en/latest/tutorials/index.html#case-studies)." ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:oggmap_env]", "language": "python", "name": "conda-env-oggmap_env-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }