From cd1610321016781fecce18da32faac4222b75c60 Mon Sep 17 00:00:00 2001 From: Jayoung Kim Ryu Date: Mon, 10 Jun 2024 16:44:33 -0400 Subject: [PATCH] Created using Colab --- docs/ReporterScreen_api.ipynb | 3802 +++++++++++++++++++++++---------- 1 file changed, 2698 insertions(+), 1104 deletions(-) mode change 100755 => 100644 docs/ReporterScreen_api.ipynb diff --git a/docs/ReporterScreen_api.ipynb b/docs/ReporterScreen_api.ipynb old mode 100755 new mode 100644 index 7d62ba5..13efd8f --- a/docs/ReporterScreen_api.ipynb +++ b/docs/ReporterScreen_api.ipynb @@ -3,10 +3,11 @@ { "cell_type": "markdown", "metadata": { - "id": "OhCQ5Qon-b8L" + "id": "view-in-github", + "colab_type": "text" }, "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tsbLl_yB9lcC_lo4sjwEoI0r1KAjPkoB)" + "\"Open" ] }, { @@ -34,270 +35,2162 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "metadata": { + "id": "b_HqduZRy3DY" + }, + "outputs": [], + "source": [ + "! pip install -q crispr-bean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "q4U4twaAzLPs" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import bean as be\n", + "import logging\n", + "\n", + "logging.getLogger('matplotlib.font_manager').disabled = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "colab": { - 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‘var_mini_screen.h5ad.1’ saved [1452304/1452304]\n", + "\n" ] + } + ], + "source": [ + "!wget https://github.com/pinellolab/crispr-bean/raw/main/tests/data/var_mini_screen.h5ad" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yBRxkKbLbD4F" + }, + "outputs": [], + "source": [ + "bdata = be.read_h5ad(\"var_mini_screen.h5ad\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P19G6BY2CODb" + }, + "source": [ + "## Data Structure" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lNOwgB5LCODb" + }, + "source": [ + "ReporterScreen object is a shallow wrapper around [AnnData](https://anndata.readthedocs.io/en/latest/). More comprehensive data wrangling documentation can be found in their documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-mRAscHoCODb", + "outputId": "a2532377-9d2e-4cb2-f4bd-0675b639137a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Genome Editing Screen comprised of n_guides x n_conditions = 30 x 10\n", + " guides: 'Unnamed: 0', 'Target gene/variant', 'Target descriptor', 'Arbitrary number', 'gRNA position category', 'Target base position in gRNA', 'Target base position in reporter', 'BE', 'target_group', 'sequence', 'reporter', 'barcode', '5-nt PAM', 'offset', 'target', 'target_pos', 'Group2', 'masked_sequence', 'masked_barcode', 'chrom', 'genomic_pos'\n", + " samples: 'condition', 'replicate', 'lower_quantile', 'upper_quantile'\n", + " samples_m: \n", + " samples_p: \n", + " layers: 'X_bcmatch', 'edits'\n", + " uns: 'allele_counts', 'edit_counts', 'target_base_changes', 'tiling'" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "bdata" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pxt9EHSeCODc" + }, + "source": [ + "* `ReporterScreen.X`: guide count\n", + "* `ReporterScreen.guides`: guide metadata. Reference to `AnnData.obs`\n", + "* `ReporterScreen.samples`: sample/condition metadata. Reference to `AnnData.var`.\n", + "* `ReporterScreen.layers[\"X_bcmatch\"]`: barcode-matched guide counts\n", + "* `ReporterScreen.layers[\"edits\"]`: edit counts\n", + "* `ReporterScreen.uns[\"allele_counts\"]`: allele counts per guide and condition\n", + "* `ReporterScreen.uns[\"edit_counts\"]`: edit counts per guide and condition" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4d8WpxcuCODc" + }, + "source": [ + "`.guides` attribute contains the information about each guide." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 356 }, + "id": "fQBYVENBCODd", + "outputId": "c7cbbda1-c71a-418f-ce5e-20b0a7461617" + }, + "outputs": [ { + "output_type": "execute_result", "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "matplotlib", - "mpl_toolkits" - ] - } + "text/plain": [ + " Unnamed: 0 Target gene/variant Target descriptor \\\n", + "name \n", + "CONTROL_8_g1 90 CONTROL NaN \n", + "CONTROL_8_g2 91 CONTROL NaN \n", + "CONTROL_8_g3 92 CONTROL NaN \n", + "CONTROL_8_g4 93 CONTROL NaN \n", + "CONTROL_8_g5 94 CONTROL NaN \n", + "\n", + " Arbitrary number gRNA position category \\\n", + "name \n", + "CONTROL_8_g1 8 g1 \n", + "CONTROL_8_g2 8 g2 \n", + "CONTROL_8_g3 8 g3 \n", + "CONTROL_8_g4 8 g4 \n", + "CONTROL_8_g5 8 g5 \n", + "\n", + " Target base position in gRNA Target base position in reporter \\\n", + "name \n", + "CONTROL_8_g1 4 10 \n", + "CONTROL_8_g2 5 11 \n", + "CONTROL_8_g3 5 12 \n", + "CONTROL_8_g4 7 13 \n", + "CONTROL_8_g5 8 14 \n", + "\n", + " BE target_group sequence ... barcode 5-nt PAM \\\n", + "name ... \n", + "CONTROL_8_g1 ABE NegCtrl AAAATTATCGGAAACGGTAG ... GAAC AATCT \n", + "CONTROL_8_g2 ABE NegCtrl AAAAATTATCGGAAACGGTA ... CGTG GAATC \n", + "CONTROL_8_g3 ABE NegCtrl AAAAATTATCGGAAACGGT ... ATCA AGAAT \n", + "CONTROL_8_g4 ABE NegCtrl CGAAAAATTATCGGAAACGG ... CAAG TAGAA \n", + "CONTROL_8_g5 ABE NegCtrl TCGAAAAATTATCGGAAACG ... TTCA GTAGA \n", + "\n", + " offset target target_pos Group2 masked_sequence \\\n", + "name \n", + "CONTROL_8_g1 -10 CONTROL_8 9 NegCtrl GGGGTTGTCGGGGGCGGTGG \n", + "CONTROL_8_g2 -11 CONTROL_8 10 NegCtrl GGGGGTTGTCGGGGGCGGTG \n", + "CONTROL_8_g3 -12 CONTROL_8 11 NegCtrl GGGGGTTGTCGGGGGCGGT \n", + "CONTROL_8_g4 -13 CONTROL_8 12 NegCtrl CGGGGGGTTGTCGGGGGCGG \n", + "CONTROL_8_g5 -14 CONTROL_8 13 NegCtrl TCGGGGGGTTGTCGGGGGCG \n", + "\n", + " masked_barcode chrom genomic_pos \n", + "name \n", + "CONTROL_8_g1 GGGC NaN NaN \n", + "CONTROL_8_g2 CGTG NaN NaN \n", + "CONTROL_8_g3 GTCG NaN NaN \n", + "CONTROL_8_g4 CGGG NaN NaN \n", + "CONTROL_8_g5 TTCG NaN NaN \n", + "\n", + "[5 rows x 21 columns]" + ], + "text/html": [ + "\n", + "
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Unnamed: 0Target gene/variantTarget descriptorArbitrary numbergRNA position categoryTarget base position in gRNATarget base position in reporterBEtarget_groupsequence...barcode5-nt PAMoffsettargettarget_posGroup2masked_sequencemasked_barcodechromgenomic_pos
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"bdata\",\n \"rows\": 1766,\n \"fields\": [\n {\n \"column\": \"guide\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 30,\n \"samples\": [\n \"CONTROL_9_g3\",\n \"ACAT2_SA_47_g1\",\n \"CONTROL_8_g4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"edit\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1183,\n \"samples\": [\n \"-7:5:+:C>A\",\n \"-4:6:+:G>A\",\n \"17:29:+:T>C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_top\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 32,\n \"min\": 0,\n \"max\": 356,\n \"num_unique_values\": 101,\n \"samples\": [\n 3,\n 52,\n 7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13,\n \"min\": 0,\n \"max\": 139,\n \"num_unique_values\": 71,\n \"samples\": [\n 10,\n 53,\n 15\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_bulk\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 24,\n \"min\": 0,\n \"max\": 354,\n \"num_unique_values\": 85,\n \"samples\": [\n 88,\n 32,\n 23\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 46,\n \"min\": 0,\n \"max\": 622,\n \"num_unique_values\": 131,\n \"samples\": [\n 41,\n 194,\n 243\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_bot\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 0,\n \"max\": 54,\n \"num_unique_values\": 38,\n \"samples\": [\n 4,\n 19,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_top\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 46,\n \"min\": 0,\n \"max\": 1295,\n \"num_unique_values\": 87,\n \"samples\": [\n 136,\n 85,\n 15\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 151,\n \"min\": 0,\n \"max\": 2927,\n \"num_unique_values\": 119,\n \"samples\": [\n 3,\n 60,\n 1381\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_bulk\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100,\n \"min\": 0,\n \"max\": 2411,\n \"num_unique_values\": 101,\n \"samples\": [\n 104,\n 46,\n 414\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 117,\n \"min\": 0,\n \"max\": 3100,\n \"num_unique_values\": 94,\n \"samples\": [\n 916,\n 73,\n 7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_bot\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 62,\n \"min\": 0,\n \"max\": 1563,\n \"num_unique_values\": 92,\n \"samples\": [\n 24,\n 67,\n 69\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, - "output_type": "display_data" + "execution_count": 9 } ], "source": [ - "! pip install -q crispr-bean" + "bdata.uns[\"edit_counts\"]" ] }, { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": { - "id": "q4U4twaAzLPs" + "id": "Hv1DMzijCODf" }, - "outputs": [], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import bean as be" + "## Changing column names" ] }, { - "cell_type": "code", - "execution_count": 33, + "cell_type": "markdown", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8KhlwSn_2x9P", - "outputId": "1fc11a18-6e01-403d-9e08-73da37eee332" + "id": "8VFtHH4ECODf" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading...\n", - "From: https://drive.google.com/uc?id=18Azb8YmmMvFZo9urc2TxZr540xXWOv_v\n", - "To: /content/bean_count_072121_ABE_topbot_LDLvar.h5ad\n", - "100% 40.2M/40.2M [00:00<00:00, 160MB/s]\n" - ] - } - ], "source": [ - "!gdown 18Azb8YmmMvFZo9urc2TxZr540xXWOv_v" + "`ReporterScreen.guides` and `ReporterScreen.var` are equivalent to `AnnData.obs` and `AnnData.var`, which are Pandas DataFrames and can be [manipulated as the DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html). For example, column names can be changed as in Pandas:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "id": "yBRxkKbLbD4F" + "id": "mu-7UmyCCODf" }, "outputs": [], "source": [ - "bdata = be.read_h5ad(\"bean_count_072121_ABE_topbot_LDLvar.h5ad\")" + "bdata.guides = bdata.guides.rename(columns={\"Reporter\":\"reporter\"})" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "A_1ERHsEbD4R" + }, "source": [ - "## Data Structure" + "## Subsetting & addition\n", + "\n" ] }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "ReporterScreen object is a shallow wrapper around [AnnData](https://anndata.readthedocs.io/en/latest/). More comprehensive data wrangling documentation can be found in their documentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "id": "WCCNOXtQbD4S" + }, "source": [ - "bdata" + "Works as anndata, supports allele & edit count operations.\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "wLQd6h_ubD4S" + }, "source": [ - "* `ReporterScreen.X`: guide count\n", - "* `ReporterScreen.guides`: guide metadata. Reference to `AnnData.obs`\n", - "* `ReporterScreen.samples`: sample/condition metadata. Reference to `AnnData.var`.\n", - "* `ReporterScreen.layers[\"X_bcmatch\"]`: barcode-matched guide counts\n", - "* `ReporterScreen.layers[\"edits\"]`: edit counts\n", - "* `ReporterScreen.uns[\"allele_counts\"]`: allele counts per guide and condition\n", - "* `ReporterScreen.uns[\"edit_counts\"]`: edit counts per guide and condition" + "### Subsetting & selection\n" ] }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "`.guides` attribute contains the information about each guide." - ] + "ReporterScreen can be subsetted for rows (guides) / selected for columns (samples) [as in AnnData](https://anndata.readthedocs.io/en/latest/tutorials/notebooks/getting-started.html#Subsetting-AnnData)." + ], + "metadata": { + "id": "4y95aTXRF6LG" + } }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "DDVh1WtrbD4T" + }, "outputs": [], "source": [ - "bdata.guides" + "bdata_subset = bdata[:10,bdata.samples.condition == \"bulk\"]" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", "source": [ - "`.samples` attribute contains the sample and condition specific information." + "bdata_subset" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iXyQLIqlF25z", + "outputId": "c0fcaebc-b785-42ec-cf68-6fc1a902bc39" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Genome Editing Screen comprised of n_guides x n_conditions = 10 x 2\n", + " guides: 'Unnamed: 0', 'Target gene/variant', 'Target descriptor', 'Arbitrary number', 'gRNA position category', 'Target base position in gRNA', 'Target base position in reporter', 'BE', 'target_group', 'sequence', 'reporter', 'barcode', '5-nt PAM', 'offset', 'target', 'target_pos', 'Group2', 'masked_sequence', 'masked_barcode', 'chrom', 'genomic_pos'\n", + " samples: 'condition', 'replicate', 'lower_quantile', 'upper_quantile'\n", + " samples_m: \n", + " samples_p: \n", + " layers: 'X_bcmatch', 'edits'\n", + " uns: 'allele_counts', 'edit_counts', 'target_base_changes', 'tiling'" + ] + }, + "metadata": {}, + "execution_count": 12 + } ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "id": "6wzebZzsbD4a" + }, "source": [ - "bdata.samples" + "## Getting edit rates from allele counts\n", + "\n" ] }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "Per-guide allele count information is stored in `.uns['allele_counts']`." - ] + "`ReporterScreen.uns[\"edit_counts\"]` is derived from `ReporterScreen.uns[\"allele_counts\"]`, then used to generated per-guide or per-window editing rate." + ], + "metadata": { + "id": "qKEu9Jb_GZxZ" + } }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 488 + "height": 444 }, - "id": "4Q6jF9DgbD4K", - "outputId": "7abf9b29-2755-456e-fd62-5cb69a4fc27c" + "id": "GMV9enV35HKu", + "outputId": "6f36f0a6-e30d-4822-d261-5ca99a08fca2" }, "outputs": [ { + "output_type": "execute_result", "data": { + "text/plain": [ + " guide allele \\\n", + "0 ACAT2_SA_45_g4 1:12:+:A>T \n", + "1 ACAT2_SA_45_g4 12:23:+:A>G \n", + "2 ACAT2_SA_45_g4 1:12:+:A>G \n", + "3 ACAT2_SA_45_g4 14:25:+:A>G \n", + "4 ACAT2_SA_45_g4 1:12:+:A>G,12:23:+:A>G,15:26:+:A>G \n", + "... ... ... \n", + "4921 ACAT2_SA_47_g2 1:10:+:A>G,6:15:+:A>G,18:27:+:A>T,20:29:+:A>G \n", + "4922 ACAT2_SA_47_g2 16:25:+:C>T \n", + "4923 ACAT2_SA_47_g1 -8:0:+:T>-,7:15:+:A>G,8:16:+:A>G,9:17:+:A>G,14... \n", + "4924 ACAT2_SA_47_g1 1:9:+:A>G,6:14:+:A>G,9:17:+:A>G,13:21:+:A>G,17... \n", + "4925 CONTROL_8_g4 -11:0:+:A>G,0:11:+:A>G,4:15:+:A>G,16:27:+:A>G,... \n", + "\n", + " rep5_top rep5_high rep5_bulk rep5_low rep5_bot rep6_top rep6_high \\\n", + "0 0 0 0 0 0 0 0 \n", + "1 2 19 26 13 2 40 203 \n", + "2 30 21 23 73 6 6 18 \n", + "3 4 1 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 \n", + "... ... ... ... ... ... ... ... \n", + "4921 0 0 0 0 1 0 0 \n", + "4922 0 0 0 0 1 0 0 \n", + "4923 0 0 0 0 1 0 0 \n", + "4924 0 0 0 0 1 0 0 \n", + "4925 0 0 0 0 1 0 0 \n", + "\n", + " rep6_bulk rep6_low rep6_bot \n", + "0 0 0 0 \n", + "1 43 210 67 \n", + "2 0 1 4 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "... ... ... ... \n", + "4921 0 0 0 \n", + "4922 0 0 0 \n", + "4923 0 0 0 \n", + "4924 0 0 0 \n", + "4925 0 0 0 \n", + "\n", + "[4926 rows x 12 columns]" + ], "text/html": [ "\n", - "
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"bdata\",\n \"rows\": 4926,\n \"fields\": [\n {\n \"column\": \"guide\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 30,\n \"samples\": [\n \"ACAT2_SA_45_g1\",\n \"ACAT2_SA_45_g3\",\n \"ACAT2_SA_47_g4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"allele\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4705,\n \"samples\": [\n \"-4:7:+:A>G,10:21:+:C>G,11:22:+:A>G\",\n \"-4:7:+:A>G,-2:9:+:A>G,7:18:+:A>G\",\n \"-2:8:+:A>G,0:10:+:A>G,11:21:+:A>G\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_top\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8,\n \"min\": 0,\n \"max\": 180,\n \"num_unique_values\": 68,\n \"samples\": [\n 60,\n 25,\n 21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3,\n \"min\": 0,\n \"max\": 120,\n \"num_unique_values\": 39,\n \"samples\": [\n 13,\n 24,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_bulk\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 0,\n \"max\": 214,\n \"num_unique_values\": 59,\n \"samples\": [\n 0,\n 5,\n 47\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11,\n \"min\": 0,\n \"max\": 309,\n \"num_unique_values\": 84,\n \"samples\": [\n 81,\n 0,\n 19\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_bot\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 35,\n \"num_unique_values\": 18,\n \"samples\": [\n 0,\n 2,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_top\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19,\n \"min\": 0,\n \"max\": 1061,\n \"num_unique_values\": 60,\n \"samples\": [\n 0,\n 5,\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 51,\n \"min\": 0,\n \"max\": 2446,\n \"num_unique_values\": 99,\n \"samples\": [\n 67,\n 241,\n 198\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_bulk\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35,\n \"min\": 0,\n \"max\": 1854,\n \"num_unique_values\": 79,\n \"samples\": [\n 618,\n 0,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 47,\n \"min\": 0,\n \"max\": 2507,\n \"num_unique_values\": 72,\n \"samples\": [\n 6,\n 159,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_bot\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 26,\n \"min\": 0,\n \"max\": 1446,\n \"num_unique_values\": 64,\n \"samples\": [\n 47,\n 250,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 13 + } + ], "source": [ - "New columns can be assigned likewise:" + "bdata.uns['allele_counts']" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bdata.samples[\"lower_quantile\"] = [\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.7,\n", - " 0.7,\n", - " 0.7,\n", - " 0.7,\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "A_1ERHsEbD4R" - }, - "source": [ - "## Subsetting & addition\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WCCNOXtQbD4S" - }, - "source": [ - "Works as anndata, supports allele & edit count operations.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wLQd6h_ubD4S" - }, - "source": [ - "### Subsetting & selection\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "jaUnqqXt3G2P" - }, - "outputs": [], - "source": [ - "bdata.samples[\"replicate\"], bdata.samples[\"sort\"] = zip(*bdata.samples.index.map(lambda s: s.rsplit(\"_\", 1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, "metadata": { - "id": "DDVh1WtrbD4T" + "id": "pEX2eOem4uka" }, "outputs": [], "source": [ - "bdata_subset = bdata[:10,bdata.samples.sort == \"bulk\"]" + "bdata.get_edit_from_allele()" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "Z_s5M7L1bD4U" - }, - "outputs": [], - "source": [ - "bdata.uns[\"allele_counts\"] = bdata.uns['allele_counts'].loc[bdata.uns['allele_counts'].allele.map(str) != \"\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6wzebZzsbD4a" - }, "source": [ - "## Getting edit rates from allele counts\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, + "bdata.uns[\"edit_counts\"]" + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 488 + "height": 444 }, - "id": "GMV9enV35HKu", - "outputId": "e8259a37-98f1-408c-9e16-d5315c2ce06c" + "id": "l-QOC0pXGzZ-", + "outputId": "7028aaf1-cd78-4f17-f585-b872297ce000" }, + "execution_count": null, "outputs": [ { + "output_type": "execute_result", "data": { + "text/plain": [ + " guide edit rep5_top rep5_high rep5_bulk rep5_low \\\n", + "0 ACAT2_SA_44_g1 -2:6:+:T>A 0 0 0 0 \n", + "1 ACAT2_SA_44_g1 -2:6:+:T>C 0 0 0 0 \n", + "2 ACAT2_SA_44_g1 -3:5:+:T>A 1 0 0 0 \n", + "3 ACAT2_SA_44_g1 -3:5:+:T>C 0 0 0 0 \n", + "4 ACAT2_SA_44_g1 -3:5:+:T>G 0 0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1761 CONTROL_9_g5 7:19:+:A>T 0 0 0 0 \n", + "1762 CONTROL_9_g5 8:20:+:C>A 0 0 0 0 \n", + "1763 CONTROL_9_g5 8:20:+:C>T 0 0 0 0 \n", + "1764 CONTROL_9_g5 9:21:+:C>- 0 0 0 0 \n", + "1765 CONTROL_9_g5 9:21:+:C>A 0 0 0 0 \n", + "\n", + " rep5_bot rep6_top rep6_high rep6_bulk rep6_low rep6_bot \n", + "0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 0 0 0 0 1 0 \n", + "4 0 0 1 0 0 0 \n", + "... ... ... ... ... ... ... \n", + "1761 0 0 1 0 0 0 \n", + "1762 0 0 0 0 0 0 \n", + "1763 0 0 1 0 0 0 \n", + "1764 0 0 0 0 0 0 \n", + "1765 0 0 0 0 0 0 \n", + "\n", + "[1766 rows x 12 columns]" + ], "text/html": [ "\n", - 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\n" ], - "text/plain": [ - " guide \\\n", - "1 LDLR_SA_3_g5 \n", - "2 LDLR_SA_3_g5 \n", - "3 LDLR_SA_3_g5 \n", - "4 LDLR_SA_3_g5 \n", - "5 LDLR_SA_3_g5 \n", - "... ... \n", - "165506 2:164588224GAG_Maj_ABE_10_g3 \n", - "165507 2:164588224GAG_Maj_ABE_10_g3 \n", - "165508 rs4921914_Min_ABE_501_g4 \n", - "165509 rs191388787_Maj_ABE_121_g2 \n", - "165510 rs113408797_Maj_ABE_41_g1 \n", - "\n", - " allele rep1_bot rep2_bot \\\n", - "1 0:13:+:A>G 6 16 \n", - "2 -12:1:+:A>G,0:13:+:A>G 2 16 \n", - "3 0:13:+:A>G,7:20:+:A>G 2 0 \n", - "4 -12:1:+:A>G,-8:5:+:A>G,0:13:+:A>G,10:23:+:A>G 1 0 \n", - "5 -12:1:+:A>G,0:13:+:A>G,7:20:+:A>G 1 0 \n", - "... ... ... ... \n", - "165506 -10:1:+:C>T,-8:3:+:A>T 0 0 \n", - "165507 -8:3:+:A>T,-3:8:+:C>A,-1:10:+:G>A 0 0 \n", - "165508 -9:3:+:A>G,-8:4:+:A>G,-3:9:+:A>G,2:14:+:A>G,4:... 0 0 \n", - "165509 -7:3:+:A>G,-5:5:+:A>G,-3:7:+:A>G 0 0 \n", - "165510 -8:1:+:A>G,-1:8:+:A>G,3:12:+:A>G,5:14:+:A>G,17... 0 0 \n", - "\n", - " rep3_VPA_bot rep4_VPA_bot rep1_bulk rep2_bulk rep3_VPA_bulk \\\n", - "1 11 24 29 21 28 \n", - "2 1 24 16 5 9 \n", - "3 0 0 0 0 0 \n", - "4 0 0 0 0 0 \n", - "5 0 0 0 2 2 \n", - "... ... ... ... ... ... \n", - "165506 0 0 0 0 0 \n", - "165507 0 0 0 0 0 \n", - "165508 0 0 0 0 0 \n", - "165509 0 0 0 0 0 \n", - "165510 0 0 0 0 0 \n", - "\n", - " rep4_VPA_bulk rep1_top rep2_top rep3_VPA_top rep4_VPA_top \n", - "1 22 27 11 20 13 \n", - "2 4 17 12 7 3 \n", - "3 9 0 0 0 0 \n", - "4 0 0 0 0 0 \n", - "5 0 1 0 0 0 \n", - "... ... ... ... ... ... \n", - "165506 0 0 0 0 1 \n", - "165507 0 0 0 0 1 \n", - "165508 0 0 0 0 1 \n", - "165509 0 0 0 0 1 \n", - "165510 0 0 0 0 1 \n", - "\n", - "[162065 rows x 14 columns]" - ] + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"bdata\",\n \"rows\": 1766,\n \"fields\": [\n {\n \"column\": \"guide\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 30,\n \"samples\": [\n \"CONTROL_9_g3\",\n \"ACAT2_SA_47_g1\",\n \"CONTROL_8_g4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"edit\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1183,\n \"samples\": [\n \"13:22:+:A>G\",\n \"-10:1:+:G>C\",\n \"7:18:+:G>T\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_top\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 32,\n \"min\": 0,\n \"max\": 356,\n \"num_unique_values\": 101,\n \"samples\": [\n 5,\n 13,\n 43\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 13,\n \"min\": 0,\n \"max\": 139,\n \"num_unique_values\": 71,\n \"samples\": [\n 13,\n 0,\n 46\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_bulk\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 24,\n \"min\": 0,\n \"max\": 354,\n \"num_unique_values\": 85,\n \"samples\": [\n 92,\n 0,\n 35\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 46,\n \"min\": 0,\n \"max\": 622,\n \"num_unique_values\": 131,\n \"samples\": [\n 127,\n 285,\n 68\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep5_bot\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 0,\n \"max\": 54,\n \"num_unique_values\": 38,\n \"samples\": [\n 4,\n 19,\n 18\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_top\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 46,\n \"min\": 0,\n \"max\": 1295,\n \"num_unique_values\": 87,\n \"samples\": [\n 67,\n 0,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 151,\n \"min\": 0,\n \"max\": 2927,\n \"num_unique_values\": 119,\n \"samples\": [\n 62,\n 838,\n 42\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_bulk\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 100,\n \"min\": 0,\n \"max\": 2411,\n \"num_unique_values\": 101,\n \"samples\": [\n 222,\n 198,\n 231\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 117,\n \"min\": 0,\n \"max\": 3100,\n \"num_unique_values\": 94,\n \"samples\": [\n 23,\n 77,\n 126\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rep6_bot\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 62,\n \"min\": 0,\n \"max\": 1563,\n \"num_unique_values\": 92,\n \"samples\": [\n 97,\n 197,\n 48\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } }, - "execution_count": 42, "metadata": {}, - "output_type": "execute_result" + "execution_count": 15 } - ], - "source": [ - "bdata.uns['allele_counts']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "pEX2eOem4uka" - }, - "outputs": [], - "source": [ - "bdata.uns[\"edit_counts\"] = bdata.get_edit_from_allele()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FwKNCaIm7JSH", - "outputId": "f874adbf-6286-46a5-8b6b-5f80a204da58" + "outputId": "a5011cfa-9f61-43c8-9b20-4b684648ed68" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "New edit matrix saved in .layers['edits']. Returning old edits.\n" ] }, { + "output_type": "execute_result", "data": { "text/plain": [ - "array([[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]])" + "array([[1.900e+01, 2.500e+01, 6.200e+01, 8.400e+01, 7.000e+00, 1.500e+01,\n", + " 1.120e+02, 1.190e+02, 9.200e+01, 4.300e+01],\n", + " [8.000e+00, 2.000e+00, 0.000e+00, 4.700e+01, 5.000e+00, 1.900e+01,\n", + " 2.410e+02, 1.800e+01, 3.100e+01, 4.800e+01],\n", + " [3.700e+01, 2.000e+01, 3.500e+01, 6.300e+01, 1.000e+00, 2.470e+02,\n", + " 8.260e+02, 4.150e+02, 5.800e+02, 5.040e+02],\n", + " [2.000e+00, 2.500e+01, 4.300e+01, 6.200e+01, 8.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", + " 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1.306e+03, 5.160e+02],\n", + " [8.700e+01, 3.700e+01, 1.200e+02, 2.040e+02, 1.200e+01, 8.900e+01,\n", + " 5.960e+02, 1.100e+02, 8.400e+01, 7.700e+01],\n", + " [2.320e+02, 2.300e+01, 1.040e+02, 2.430e+02, 9.000e+00, 3.610e+02,\n", + " 9.270e+02, 4.930e+02, 2.250e+02, 1.970e+02],\n", + " [1.120e+02, 3.700e+01, 6.100e+01, 1.630e+02, 1.700e+01, 3.800e+01,\n", + " 7.600e+01, 0.000e+00, 9.200e+01, 2.000e+01],\n", + " [9.800e+01, 2.700e+01, 6.500e+01, 4.700e+01, 9.000e+00, 5.600e+01,\n", + " 2.510e+02, 1.240e+02, 7.300e+01, 1.090e+02],\n", + " [6.200e+01, 2.100e+01, 7.500e+01, 1.930e+02, 5.000e+00, 1.120e+02,\n", + " 1.420e+02, 1.540e+02, 1.990e+02, 1.750e+02],\n", + " [4.600e+01, 4.400e+01, 2.700e+01, 1.030e+02, 1.800e+01, 1.500e+01,\n", + " 2.300e+01, 0.000e+00, 6.000e+00, 5.000e+00],\n", + " [1.110e+02, 1.220e+02, 2.160e+02, 3.150e+02, 3.600e+01, 1.280e+02,\n", + " 4.190e+02, 1.960e+02, 7.200e+01, 1.120e+02],\n", + " [3.370e+02, 9.200e+01, 1.710e+02, 2.730e+02, 4.500e+01, 1.295e+03,\n", + " 2.927e+03, 2.411e+03, 3.100e+03, 1.563e+03],\n", + " [2.630e+02, 8.300e+01, 4.500e+01, 1.890e+02, 1.600e+01, 3.600e+01,\n", + " 2.930e+02, 2.870e+02, 7.700e+01, 9.700e+01],\n", + " [1.300e+01, 5.900e+01, 7.700e+01, 1.940e+02, 1.600e+01, 2.400e+01,\n", + " 1.500e+02, 1.000e+00, 3.100e+01, 1.700e+01],\n", + " [1.590e+02, 7.100e+01, 1.000e+02, 2.680e+02, 2.100e+01, 3.070e+02,\n", + " 8.190e+02, 6.040e+02, 9.160e+02, 3.740e+02],\n", + " [8.300e+01, 4.600e+01, 4.800e+01, 1.260e+02, 6.000e+00, 2.400e+01,\n", + " 1.330e+02, 0.000e+00, 0.000e+00, 2.000e+00],\n", + " [5.500e+01, 1.400e+01, 0.000e+00, 5.600e+01, 7.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 4.000e+00, 1.000e+00],\n", + " [3.700e+01, 4.900e+01, 7.300e+01, 1.400e+02, 2.200e+01, 1.600e+01,\n", + " 3.200e+01, 1.850e+02, 2.500e+01, 2.100e+01],\n", + " [5.500e+01, 3.100e+01, 0.000e+00, 1.600e+02, 1.500e+01, 1.300e+01,\n", + " 3.600e+01, 0.000e+00, 4.000e+00, 2.500e+01],\n", + " [7.000e+00, 1.500e+01, 0.000e+00, 1.500e+01, 2.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", + " [9.900e+01, 0.000e+00, 0.000e+00, 2.000e+01, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00]])" ] }, - "execution_count": 12, "metadata": {}, - "output_type": "execute_result" + "execution_count": 16 } ], "source": [ - "bdata.get_edit_mat_from_uns(\"A\", \"G\", target_pos_col = \"target_pos\")" + "bdata.get_edit_mat_from_uns(target_pos_col = \"target_pos\")" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { - "id": "aJXnxwbb4F3G" + "id": "aJXnxwbb4F3G", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "286999e5-e2e4-4ec8-a525-b378788df340" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "normalize by editable counts\n" + ] + } + ], "source": [ "window_edit_rate= bdata.get_guide_edit_rate(normalize_by_editable_base = True,\n", - " edited_base = \"A\",\n", " editable_base_start = 3,\n", " editable_base_end = 8,\n", - " bcmatch_thres = 5,\n", + " bcmatch_thres = 5, # Ignore samples with X_bcmatch < 5 for stability\n", " prior_weight = 1,\n", " return_result = True)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 490 + "height": 449 }, "id": "dgM4MptLbD4c", - "outputId": "94f97a73-0e8e-4ffc-9833-8634053c3f42" + "outputId": "6b700a1e-2e45-4fef-c3c9-88945d6f4948" }, "outputs": [ { + "output_type": "display_data", "data": { "text/plain": [ - "(array([579., 364., 299., 249., 221., 214., 191., 134., 159., 133., 97.,\n", - " 90., 83., 76., 70., 67., 48., 53., 45., 56., 38., 37.,\n", - " 32., 21., 24., 15., 8., 7., 1., 2.]),\n", - " array([4.13052458e-04, 2.89016995e-02, 5.73903465e-02, 8.58789936e-02,\n", - " 1.14367641e-01, 1.42856288e-01, 1.71344935e-01, 1.99833582e-01,\n", - " 2.28322229e-01, 2.56810876e-01, 2.85299523e-01, 3.13788170e-01,\n", - " 3.42276817e-01, 3.70765464e-01, 3.99254111e-01, 4.27742758e-01,\n", - " 4.56231405e-01, 4.84720052e-01, 5.13208699e-01, 5.41697346e-01,\n", - " 5.70185993e-01, 5.98674640e-01, 6.27163287e-01, 6.55651934e-01,\n", - " 6.84140582e-01, 7.12629229e-01, 7.41117876e-01, 7.69606523e-01,\n", - " 7.98095170e-01, 8.26583817e-01, 8.55072464e-01]),\n", - " )" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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