{ "cells": [ { "cell_type": "markdown", "id": "61085d1a", "metadata": {}, "source": [ "# orthomap: 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/orthomap/main/docs/notebooks/evolutionary_indices.ipynb](https://raw.githubusercontent.com/kullrich/orthomap/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 orthomap python package submodules" ] }, { "cell_type": "code", "execution_count": 2, "id": "20cab03e", "metadata": {}, "outputs": [], "source": [ "# import submodules\n", "from orthomap import qlin, gtf2t2g, of2orthomap, orthomap2tei, datasets" ] }, { "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://orthomap.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://orthomap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) results.\n", "\n", "In [Step 1 - get taxonomic information](https://orthomap.readthedocs.io/en/latest/tutorials/query_lineage.html) you have already been introduced how to extract query lineage information with `orthomap` and the `qlin.get_qlin()` function.\n", "\n", "In [Step 2 - gene age class assignment](https://orthomap.readthedocs.io/en/latest/tutorials/get_orthomap.html) you have already been introduced how to extract an orthomap (gene age class) from [OrthoFinder](https://orthomap.readthedocs.io/en/latest/tutorials/https://github.com/davidemms/OrthoFinder) results with `orthomap` 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://orthomap.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 `orthomap`\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://orthomap.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')" ] }, { "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

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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", " 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 `orthomap` 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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\n", 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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 `orthomap` 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://orthomap.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 orthomap 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 `orthomap`:\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 `orthomap` 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": 19, "id": "45691ae8", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "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", "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": 20, "id": "d180caff", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "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", "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": 21, "id": "efed5b36", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "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", "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://orthomap.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://orthomap.readthedocs.io/en/latest/tutorials/index.html#case-studies)." ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:scanpy]", "language": "python", "name": "conda-env-scanpy-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": "3.8.15" } }, "nbformat": 4, "nbformat_minor": 5 }