diff --git a/examples/paired_reads/paired_read_example.ipynb b/examples/paired_reads/paired_read_example.ipynb index 55f8535..7517d0b 100644 --- a/examples/paired_reads/paired_read_example.ipynb +++ b/examples/paired_reads/paired_read_example.ipynb @@ -2,10 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "7b7f6b80-966a-490d-ab4c-42cb7dd3e078", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/davidfeldman/micromamba/envs/ngs-analysis/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "from ngs_analysis import app\n", "from ngs_analysis.interactive import *\n", @@ -14,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "a3021015-6c0d-47ee-abf8-2f1461226f66", "metadata": {}, "outputs": [], @@ -27,8 +36,7 @@ "cell_type": "markdown", "id": "9d39c94d-7e28-4c12-9324-ad9874de5793", "metadata": { - "tags": [], - "toc-hr-collapsed": true + "tags": [] }, "source": [ "### setup" @@ -36,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 3, "id": "ec59886e-8cd0-4cb1-9ea5-4915ca6a1509", "metadata": {}, "outputs": [ @@ -46,7 +54,7 @@ "text": [ "# 1. match the insert DNA\n", "left_adapter: GATATACC # before MGS\n", - "right_adapter: CTCGAGCA # after barcode\n", + "right_adapter: CTCGAGCACCA # after barcode\n", "\n", "# 2. use regular expression to capture a part from the \n", "# insert DNA (adapters are removed)\n", @@ -67,11 +75,12 @@ " GS_linker: '[GS]{3,}' # 3 or more G or S\n", " minibinder: '.*' # any amino acid sequence\n", "\n", - "# list part names to compare to reference\n", - "# use the \"_aa\" suffix to compare protein translations\n", - "# the format \"barcode -> minibinder_aa\" means first match by\n", + "# 5. Compare listed part names to reference.\n", + "# Use the \"_aa\" suffix to compare protein translations.\n", + "# The format \"barcode -> minibinder_aa\" means first match by\n", "# barcode, then compare the corresponding minibinder in the \n", - "# reference to the parsed minibinder sequence\n", + "# reference to the parsed minibinder sequence at the amino\n", + "# acid level.\n", "compare:\n", " - barcode\n", " - minibinder_aa\n", @@ -85,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 4, "id": "94bef716-63d8-442b-a83e-4bc3ccc4cac0", "metadata": {}, "outputs": [ @@ -96,7 +105,7 @@ "" ] }, - "execution_count": 159, + "execution_count": 4, "metadata": { "image/png": { "width": 800 @@ -112,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 5, "id": "2815e22f-b635-4fab-a933-5cd2aafb7c4b", "metadata": {}, "outputs": [ @@ -123,7 +132,7 @@ "" ] }, - "execution_count": 161, + "execution_count": 5, "metadata": { "image/png": { "width": 800 @@ -139,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 6, "id": "c1c7590e-2052-42d9-a8b3-5116e0b7cea9", "metadata": {}, "outputs": [ @@ -147,8 +156,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'name': 'example_1', 'vector_dna': 'ttgtttaactttaagaaggaGATATACCATGGGCAGCAGCCATCATCACCATCACCACAGCAGTGGCAGTGGATCCGACAAAGAATGGATTTTACAAAAGATCTATGAAATCATGCGCTTACTGGACGAGTTGGGCCATGCAGAAGCAAGTATGCGTGTCTCCGACTTGATCTACGAGTTCATGAAGAAGGGGGACGAGCGCTTATTGGAAGAAGCGGAACGTTTGTTAGAAGAAGTAGAGTAGGTTACCGGTATCTCGCTTGCGGTGAGTTCTCGAGCAccaccataactcgagcacca'}\n", - "{'name': 'example_2', 'vector_dna': 'ttgtttaactttaagaaggaGATATACCATGGGCAGCGACAAAGAATGGATTTACCAAAAGATCTATGAAATCATGCGCTTACTGAGGGAGTTGGGCCATGCAGAAGCAAGTATGCGTGTCTCCGACTTGATCTACGAGGCAATGAAGAAGGGGGACGAGCGCTTATTGGAAGAAGCGGAACGTTTGTTAGAAGAAGTAGAGAGCAGTGGCAGTGGATCCCATCATCACCATCACCACTAGGTTACCGGTATCTCGCTTGCACCGCAGTCTCGAGCAccaccataactcgagcacca'}\n" + "{'source': 'pool1', 'name': 'example_1', 'reference_dna': 'ttgtttaactttaagaaggaGATATACCATGGGCAGCAGCCATCATCACCATCACCACAGCAGTGGCAGTGGATCCGACAAAGAATGGATTTTACAAAAGATCTATGAAATCATGCGCTTACTGGACGAGTTGGGCCATGCAGAAGCAAGTATGCGTGTCTCCGACTTGATCTACGAGTTCATGAAGAAGGGGGACGAGCGCTTATTGGAAGAAGCGGAACGTTTGTTAGAAGAAGTAGAGTAGGTTACCGGTATCTCGCTTGCGGTGAGTTCTCGAGCAccaccataagtcagtcagtc'}\n", + "{'source': 'pool2', 'name': 'example_2', 'reference_dna': 'ttgtttaactttaagaaggaGATATACCATGGGCAGCGACAAAGAATGGATTTACCAAAAGATCTATGAAATCATGCGCTTACTGAGGGAGTTGGGCCATGCAGAAGCAAGTATGCGTGTCTCCGACTTGATCTACGAGGCAATGAAGAAGGGGGACGAGCGCTTATTGGAAGAAGCGGAACGTTTGTTAGAAGAAGTAGAGAGCAGTGGCAGTGGATCCCATCATCACCATCACCACTAGGTTACCGGTATCTCGCTTGCACCGCAGTCTCGAGCAccaccataagtcagtcagtc'}\n", + "{'source': 'pool3', 'name': 'example_1', 'reference_dna': 'ttgtttaactttaagaaggaGATATACCATGGGCAGCAGCCATCATCACCATCACCACAGCAGTGGCAGTGGATCCGACAAAGAATGGATTTTACAAAAGATCTATGAAATCATGCGCTTACTGGACGAGTTGGGCCATGCAGAAGCAAGTATGCGTGTCTCCGACTTGATCTACGAGTTCATGAAGAAGGGGGACGAGCGCTTATTGGAAGAAGCGGAACGTTTGTTAGAAGAAGTAGAGTAGGTTACCGGTATCTCGCTTGCGGTGAGTTCTCGAGCAccaccataagtcagtcagtc'}\n", + "{'source': 'pool3', 'name': 'example_3', 'reference_dna': 'ttgtttaactttaagaaggaGATATACCATGGGCAGCGACAAAGAATGGATTTACCAAAAGATCTATGAAATCATGCGCTTACTGAGGGAGTTGGGCCATGGCGAAGCAAGTATGCGTGTCTTAGACTTGATCTACGAGGCAATGAGGAAGGGGGACGAGCGCTTATTGAGGGAACTCGAACGTTTGTTAGAAGAAGTAGAGAGCAGTGGCAGTGGATCCCATCATCACCATCACCACTAGGTTACCGGTATCTCGCTTGCGTTGTACGCTCGAGCAccaccataagtcagtcagtc'}\n" ] } ], @@ -170,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "id": "4aa67981-0b15-4cd9-a99a-635de9326339", "metadata": {}, "outputs": [ @@ -237,7 +248,7 @@ "3 pool3 example_3 ttgtttaactttaagaaggaGATATACCATGGGCAGCGACAAAGAA..." ] }, - "execution_count": 20, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -248,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "d0442c4d-4743-4211-923d-e910e4216e80", "metadata": {}, "outputs": [ @@ -293,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "id": "ba8b3d65-0743-4904-8d79-af81799cda9a", "metadata": {}, "outputs": [ @@ -390,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "id": "34bb8ba0-be38-418d-85c7-9801cd0f8e48", "metadata": {}, "outputs": [ @@ -398,46 +409,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running ngmerge on sample sample_A... done in 0.00786 seconds\n", + "Running ngmerge on sample sample_A... done in 0.0475 seconds\n", "Wrote output to 1_reads/simulate/sample_A.fastq\n", - "Loading reads... done in 0.000716 seconds\n", - "Parsing 50 reads... done in 0.00359 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.0383 seconds\n", - "Searching for minibinder_aa candidates... done in 0.597 seconds\n", - "Calculating distances for 150 minibinder_aa candidates... done in 0.00306 seconds\n", - "Searching for barcode candidates... done in 0.0533 seconds\n", - "Calculating distances for 50 barcode candidates... done in 0.00287 seconds\n", + "Loading reads... done in 0.000891 seconds\n", + "Parsing 50 reads... done in 0.0152 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.0564 seconds\n", + "Searching for barcode candidates... done in 0.461 seconds\n", + "Calculating distances for 50 barcode candidates... done in 0.00319 seconds\n", + "Searching for minibinder_aa candidates... done in 0.7 seconds\n", + "Calculating distances for 150 minibinder_aa candidates... done in 0.00298 seconds\n", "Wrote match table (50 rows) to 3_mapped/simulate/sample_A.mapped.csv\n", - "Running ngmerge on sample sample_B... done in 0.00683 seconds\n", + "Running ngmerge on sample sample_B... done in 0.00575 seconds\n", "Wrote output to 1_reads/simulate/sample_B.fastq\n", - "Loading reads... done in 0.000441 seconds\n", - "Parsing 50 reads... done in 0.00304 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.0363 seconds\n", - "Searching for minibinder_aa candidates... done in 0.613 seconds\n", - "Calculating distances for 150 minibinder_aa candidates... done in 0.00297 seconds\n", - "Searching for barcode candidates... done in 0.0529 seconds\n", - "Calculating distances for 50 barcode candidates... done in 0.00275 seconds\n", + "Loading reads... done in 0.00051 seconds\n", + "Parsing 50 reads... done in 0.00271 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.0397 seconds\n", + "Searching for barcode candidates... done in 0.0533 seconds\n", + "Calculating distances for 50 barcode candidates... done in 0.00282 seconds\n", + "Searching for minibinder_aa candidates... done in 0.595 seconds\n", + "Calculating distances for 150 minibinder_aa candidates... done in 0.00294 seconds\n", "Wrote match table (50 rows) to 3_mapped/simulate/sample_B.mapped.csv\n", - "Running ngmerge on sample sample_C... done in 0.00552 seconds\n", + "Running ngmerge on sample sample_C... done in 0.00515 seconds\n", "Wrote output to 1_reads/simulate/sample_C.fastq\n", - "Loading reads... done in 0.000367 seconds\n", - "Parsing 20 reads... done in 0.00228 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.0377 seconds\n", - "Searching for minibinder_aa candidates... done in 0.609 seconds\n", - "Calculating distances for 60 minibinder_aa candidates... done in 0.00284 seconds\n", - "Searching for barcode candidates... done in 0.051 seconds\n", - "Calculating distances for 20 barcode candidates... done in 0.00256 seconds\n", + "Loading reads... done in 0.000433 seconds\n", + "Parsing 20 reads... done in 0.00211 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.0354 seconds\n", + "Searching for barcode candidates... done in 0.0902 seconds\n", + "Calculating distances for 20 barcode candidates... done in 0.00361 seconds\n", + "Searching for minibinder_aa candidates... done in 0.624 seconds\n", + "Calculating distances for 60 minibinder_aa candidates... done in 0.00295 seconds\n", "Wrote match table (20 rows) to 3_mapped/simulate/sample_C.mapped.csv\n", - "Running ngmerge on sample sample_D... done in 0.00644 seconds\n", + "Running ngmerge on sample sample_D... done in 0.00623 seconds\n", "Wrote output to 1_reads/simulate/sample_D.fastq\n", - "Loading reads... done in 0.000405 seconds\n", - "Parsing 39 reads... done in 0.00234 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.0339 seconds\n", - "Searching for minibinder_aa candidates... done in 0.617 seconds\n", - "Calculating distances for 117 minibinder_aa candidates... done in 0.0029 seconds\n", - "Searching for barcode candidates... done in 0.0539 seconds\n", - "Calculating distances for 39 barcode candidates... done in 0.00256 seconds\n", - "Wrote match table (39 rows) to 3_mapped/simulate/sample_D.mapped.csv\n" + "Loading reads... done in 0.000445 seconds\n", + "Parsing 53 reads... done in 0.00251 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.036 seconds\n", + "Searching for barcode candidates... done in 0.0531 seconds\n", + "Calculating distances for 53 barcode candidates... done in 0.00283 seconds\n", + "Searching for minibinder_aa candidates... done in 0.595 seconds\n", + "Calculating distances for 159 minibinder_aa candidates... done in 0.00284 seconds\n", + "Wrote match table (53 rows) to 3_mapped/simulate/sample_D.mapped.csv\n" ] } ], @@ -451,14 +462,16 @@ { "cell_type": "markdown", "id": "dbd915f4-08d3-4aac-abdc-a86a45584242", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### inspect results" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "id": "d550c735-675e-4530-a98b-2c729b343c2c", "metadata": {}, "outputs": [ @@ -576,7 +589,7 @@ "4 AGCAGTGGCAGTGGATCC GACAAAGAATGGATTTTACAAAAGATCTATGAAATCATGCGCTTAC... " ] }, - "execution_count": 33, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -588,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "id": "16d36a0c-5c70-4dfa-8d75-2bcea3ca8f61", "metadata": {}, "outputs": [ @@ -614,12 +627,12 @@ " \n", " \n", " read_index\n", - " minibinder_aa_match\n", - " minibinder_aa_distance\n", - " minibinder_aa_match_equidistant\n", " barcode_match\n", " barcode_distance\n", " barcode_match_equidistant\n", + " minibinder_aa_match\n", + " minibinder_aa_distance\n", + " minibinder_aa_match_equidistant\n", " minibinder_aa_from_barcode_match\n", " minibinder_aa_from_barcode_distance\n", " \n", @@ -690,26 +703,26 @@ "" ], "text/plain": [ - " read_index minibinder_aa_match minibinder_aa_distance \\\n", - "0 0 example_1 0 \n", - "1 1 example_1 0 \n", - "2 2 example_1 0 \n", - "3 3 example_1 0 \n", - "4 4 example_1 0 \n", + " read_index barcode_match barcode_distance barcode_match_equidistant \\\n", + "0 0 example_1 0 1 \n", + "1 1 example_1 0 1 \n", + "2 2 example_1 0 1 \n", + "3 3 example_1 0 1 \n", + "4 4 example_1 0 1 \n", "\n", - " minibinder_aa_match_equidistant barcode_match barcode_distance \\\n", - "0 1 example_1 0 \n", - "1 1 example_1 0 \n", - "2 1 example_1 0 \n", - "3 1 example_1 0 \n", - "4 1 example_1 0 \n", + " minibinder_aa_match minibinder_aa_distance \\\n", + "0 example_1 0 \n", + "1 example_1 0 \n", + "2 example_1 0 \n", + "3 example_1 0 \n", + "4 example_1 0 \n", "\n", - " barcode_match_equidistant minibinder_aa_from_barcode_match \\\n", - "0 1 example_1 \n", - "1 1 example_1 \n", - "2 1 example_1 \n", - "3 1 example_1 \n", - "4 1 example_1 \n", + " minibinder_aa_match_equidistant minibinder_aa_from_barcode_match \\\n", + "0 1 example_1 \n", + "1 1 example_1 \n", + "2 1 example_1 \n", + "3 1 example_1 \n", + "4 1 example_1 \n", "\n", " minibinder_aa_from_barcode_distance \n", "0 0 \n", @@ -719,7 +732,7 @@ "4 0 " ] }, - "execution_count": 35, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -732,14 +745,16 @@ { "cell_type": "markdown", "id": "4594e9a1-c5f7-42bc-964b-fbf837f0bfc0", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### repeat simulation with noisy reads" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 13, "id": "7cdff684-bf6b-4768-af52-3b5da4f09283", "metadata": {}, "outputs": [ @@ -761,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 14, "id": "b391887d-b73e-4238-8b51-0f355f542df3", "metadata": {}, "outputs": [ @@ -769,46 +784,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running ngmerge on sample sample_A... done in 0.00909 seconds\n", + "Running ngmerge on sample sample_A... done in 0.00898 seconds\n", "Wrote output to 1_reads/simulate/sample_A.fastq\n", - "Loading reads... done in 0.000726 seconds\n", - "Parsing 50 reads... done in 0.00321 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.0376 seconds\n", - "Searching for minibinder_aa candidates... done in 0.592 seconds\n", - "Calculating distances for 102 minibinder_aa candidates... done in 0.00269 seconds\n", - "Searching for barcode candidates... done in 0.0537 seconds\n", - "Calculating distances for 38 barcode candidates... done in 0.00253 seconds\n", + "Loading reads... done in 0.000796 seconds\n", + "Parsing 50 reads... done in 0.00338 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.0386 seconds\n", + "Searching for barcode candidates... done in 0.0548 seconds\n", + "Calculating distances for 38 barcode candidates... done in 0.00271 seconds\n", + "Searching for minibinder_aa candidates... done in 0.626 seconds\n", + "Calculating distances for 102 minibinder_aa candidates... done in 0.0027 seconds\n", "Wrote match table (29 rows) to 3_mapped/simulate/sample_A.mapped.csv\n", - "Running ngmerge on sample sample_B... done in 0.00573 seconds\n", + "Running ngmerge on sample sample_B... done in 0.00575 seconds\n", "Wrote output to 1_reads/simulate/sample_B.fastq\n", - "Loading reads... done in 0.000508 seconds\n", - "Parsing 50 reads... done in 0.00242 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.0337 seconds\n", - "Searching for minibinder_aa candidates... done in 0.614 seconds\n", - "Calculating distances for 99 minibinder_aa candidates... done in 0.00289 seconds\n", - "Searching for barcode candidates... done in 0.0542 seconds\n", - "Calculating distances for 37 barcode candidates... done in 0.00255 seconds\n", + "Loading reads... done in 0.000505 seconds\n", + "Parsing 50 reads... done in 0.00268 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.0368 seconds\n", + "Searching for barcode candidates... done in 0.0551 seconds\n", + "Calculating distances for 37 barcode candidates... done in 0.00269 seconds\n", + "Searching for minibinder_aa candidates... done in 0.624 seconds\n", + "Calculating distances for 99 minibinder_aa candidates... done in 0.00307 seconds\n", "Wrote match table (27 rows) to 3_mapped/simulate/sample_B.mapped.csv\n", - "Running ngmerge on sample sample_C... done in 0.00565 seconds\n", + "Running ngmerge on sample sample_C... done in 0.005 seconds\n", "Wrote output to 1_reads/simulate/sample_C.fastq\n", - "Loading reads... done in 0.000347 seconds\n", - "Parsing 20 reads... done in 0.00195 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.034 seconds\n", - "Searching for minibinder_aa candidates... done in 0.587 seconds\n", - "Calculating distances for 45 minibinder_aa candidates... done in 0.00274 seconds\n", - "Searching for barcode candidates... done in 0.0535 seconds\n", - "Calculating distances for 17 barcode candidates... done in 0.00256 seconds\n", + "Loading reads... done in 0.000408 seconds\n", + "Parsing 20 reads... done in 0.00215 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.0375 seconds\n", + "Searching for barcode candidates... done in 0.05 seconds\n", + "Calculating distances for 17 barcode candidates... done in 0.00263 seconds\n", + "Searching for minibinder_aa candidates... done in 0.621 seconds\n", + "Calculating distances for 45 minibinder_aa candidates... done in 0.00273 seconds\n", "Wrote match table (14 rows) to 3_mapped/simulate/sample_C.mapped.csv\n", - "Running ngmerge on sample sample_D... done in 0.00746 seconds\n", + "Running ngmerge on sample sample_D... done in 0.00753 seconds\n", "Wrote output to 1_reads/simulate/sample_D.fastq\n", - "Loading reads... done in 0.000564 seconds\n", - "Parsing 56 reads... done in 0.00285 seconds\n", - "Creating mmseqs index for fields: minibinder_aa, barcode done in 0.0351 seconds\n", - "Searching for minibinder_aa candidates... done in 0.602 seconds\n", - "Calculating distances for 111 minibinder_aa candidates... done in 0.00303 seconds\n", - "Searching for barcode candidates... done in 0.0554 seconds\n", - "Calculating distances for 44 barcode candidates... done in 0.00294 seconds\n", - "Wrote match table (33 rows) to 3_mapped/simulate/sample_D.mapped.csv\n" + "Loading reads... done in 0.000785 seconds\n", + "Parsing 57 reads... done in 0.00299 seconds\n", + "Creating mmseqs index for fields: barcode, minibinder_aa done in 0.0373 seconds\n", + "Searching for barcode candidates... done in 0.0552 seconds\n", + "Calculating distances for 47 barcode candidates... done in 0.00299 seconds\n", + "Searching for minibinder_aa candidates... done in 0.654 seconds\n", + "Calculating distances for 117 minibinder_aa candidates... done in 0.0028 seconds\n", + "Wrote match table (34 rows) to 3_mapped/simulate/sample_D.mapped.csv\n" ] } ], @@ -821,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 15, "id": "e1313052-7e4b-4367-83c3-b11980e0d934", "metadata": {}, "outputs": [ @@ -1021,7 +1036,7 @@ "9 CATCATCACCATCACCAC " ] }, - "execution_count": 41, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1033,7 +1048,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 16, "id": "1b55d11a-553c-43b1-a8b9-0349d4bfc205", "metadata": {}, "outputs": [ @@ -1059,12 +1074,12 @@ " \n", " \n", " read_index\n", - " minibinder_aa_match\n", - " minibinder_aa_distance\n", - " minibinder_aa_match_equidistant\n", " barcode_match\n", " barcode_distance\n", " barcode_match_equidistant\n", + " minibinder_aa_match\n", + " minibinder_aa_distance\n", + " minibinder_aa_match_equidistant\n", " minibinder_aa_from_barcode_match\n", " minibinder_aa_from_barcode_distance\n", " \n", @@ -1074,10 +1089,10 @@ " 0\n", " 0\n", " example_2\n", - " 1\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 1\n", " 1\n", " example_2\n", " 1\n", @@ -1098,10 +1113,10 @@ " 2\n", " 3\n", " example_2\n", - " 3\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 3\n", " 1\n", " example_2\n", " 3\n", @@ -1110,10 +1125,10 @@ " 3\n", " 4\n", " example_2\n", - " 1\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 1\n", " 1\n", " example_2\n", " 1\n", @@ -1146,10 +1161,10 @@ " 6\n", " 9\n", " example_2\n", - " 3\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 3\n", " 1\n", " example_2\n", " 3\n", @@ -1158,10 +1173,10 @@ " 7\n", " 10\n", " example_2\n", - " 2\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 2\n", " 1\n", " example_2\n", " 2\n", @@ -1182,10 +1197,10 @@ " 9\n", " 12\n", " example_2\n", - " 3\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 3\n", " 1\n", " example_2\n", " 3\n", @@ -1218,10 +1233,10 @@ " 12\n", " 15\n", " example_2\n", - " 0\n", + " 297\n", " 1\n", " example_2\n", - " 297\n", + " 0\n", " 1\n", " example_2\n", " 0\n", @@ -1230,10 +1245,10 @@ " 13\n", " 17\n", " example_2\n", - " 1\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 1\n", " 1\n", " example_2\n", " 1\n", @@ -1242,10 +1257,10 @@ " 14\n", " 21\n", " example_2\n", - " 1\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 1\n", " 1\n", " example_2\n", " 1\n", @@ -1254,10 +1269,10 @@ " 15\n", " 22\n", " example_2\n", - " 1\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 1\n", " 1\n", " example_2\n", " 1\n", @@ -1278,10 +1293,10 @@ " 17\n", " 25\n", " example_2\n", - " 1\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 1\n", " 1\n", " example_2\n", " 1\n", @@ -1290,10 +1305,10 @@ " 18\n", " 27\n", " example_2\n", - " 1\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 1\n", " 1\n", " example_2\n", " 1\n", @@ -1374,10 +1389,10 @@ " 25\n", " 43\n", " example_2\n", - " 3\n", + " 0\n", " 1\n", " example_2\n", - " 0\n", + " 3\n", " 1\n", " example_2\n", " 3\n", @@ -1399,92 +1414,92 @@ "" ], "text/plain": [ - " read_index minibinder_aa_match minibinder_aa_distance \\\n", - "0 0 example_2 1 \n", - "1 1 example_2 0 \n", - "2 3 example_2 3 \n", - "3 4 example_2 1 \n", - "4 5 example_2 0 \n", - "5 6 example_2 0 \n", - "6 9 example_2 3 \n", - "7 10 example_2 2 \n", - "8 11 example_2 0 \n", - "9 12 example_2 3 \n", - "10 13 example_2 0 \n", - "11 14 example_2 0 \n", - "12 15 example_2 0 \n", - "13 17 example_2 1 \n", - "14 21 example_2 1 \n", - "15 22 example_2 1 \n", - "16 23 example_2 0 \n", - "17 25 example_2 1 \n", - "18 27 example_2 1 \n", - "19 30 example_2 0 \n", - "20 32 example_2 0 \n", - "21 34 example_2 0 \n", - "22 36 example_2 0 \n", - "23 37 example_2 0 \n", - "24 39 example_2 0 \n", - "25 43 example_2 3 \n", - "26 46 example_2 0 \n", + " read_index barcode_match barcode_distance barcode_match_equidistant \\\n", + "0 0 example_2 0 1 \n", + "1 1 example_2 0 1 \n", + "2 3 example_2 0 1 \n", + "3 4 example_2 0 1 \n", + "4 5 example_2 0 1 \n", + "5 6 example_2 0 1 \n", + "6 9 example_2 0 1 \n", + "7 10 example_2 0 1 \n", + "8 11 example_2 0 1 \n", + "9 12 example_2 0 1 \n", + "10 13 example_2 0 1 \n", + "11 14 example_2 0 1 \n", + "12 15 example_2 297 1 \n", + "13 17 example_2 0 1 \n", + "14 21 example_2 0 1 \n", + "15 22 example_2 0 1 \n", + "16 23 example_2 0 1 \n", + "17 25 example_2 0 1 \n", + "18 27 example_2 0 1 \n", + "19 30 example_2 0 1 \n", + "20 32 example_2 0 1 \n", + "21 34 example_2 0 1 \n", + "22 36 example_2 0 1 \n", + "23 37 example_2 0 1 \n", + "24 39 example_2 0 1 \n", + "25 43 example_2 0 1 \n", + "26 46 example_2 0 1 \n", "\n", - " minibinder_aa_match_equidistant barcode_match barcode_distance \\\n", - "0 1 example_2 0 \n", - "1 1 example_2 0 \n", - "2 1 example_2 0 \n", - "3 1 example_2 0 \n", - "4 1 example_2 0 \n", - "5 1 example_2 0 \n", - "6 1 example_2 0 \n", - "7 1 example_2 0 \n", - "8 1 example_2 0 \n", - "9 1 example_2 0 \n", - "10 1 example_2 0 \n", - "11 1 example_2 0 \n", - "12 1 example_2 297 \n", - "13 1 example_2 0 \n", - "14 1 example_2 0 \n", - "15 1 example_2 0 \n", - "16 1 example_2 0 \n", - "17 1 example_2 0 \n", - "18 1 example_2 0 \n", - "19 1 example_2 0 \n", - "20 1 example_2 0 \n", - "21 1 example_2 0 \n", - "22 1 example_2 0 \n", - "23 1 example_2 0 \n", - "24 1 example_2 0 \n", - "25 1 example_2 0 \n", - "26 1 example_2 0 \n", + " minibinder_aa_match minibinder_aa_distance \\\n", + "0 example_2 1 \n", + "1 example_2 0 \n", + "2 example_2 3 \n", + "3 example_2 1 \n", + "4 example_2 0 \n", + "5 example_2 0 \n", + "6 example_2 3 \n", + "7 example_2 2 \n", + "8 example_2 0 \n", + "9 example_2 3 \n", + "10 example_2 0 \n", + "11 example_2 0 \n", + "12 example_2 0 \n", + "13 example_2 1 \n", + "14 example_2 1 \n", + "15 example_2 1 \n", + "16 example_2 0 \n", + "17 example_2 1 \n", + "18 example_2 1 \n", + "19 example_2 0 \n", + "20 example_2 0 \n", + "21 example_2 0 \n", + "22 example_2 0 \n", + "23 example_2 0 \n", + "24 example_2 0 \n", + "25 example_2 3 \n", + "26 example_2 0 \n", "\n", - " barcode_match_equidistant minibinder_aa_from_barcode_match \\\n", - "0 1 example_2 \n", - "1 1 example_2 \n", - "2 1 example_2 \n", - "3 1 example_2 \n", - "4 1 example_2 \n", - "5 1 example_2 \n", - "6 1 example_2 \n", - "7 1 example_2 \n", - "8 1 example_2 \n", - "9 1 example_2 \n", - "10 1 example_2 \n", - "11 1 example_2 \n", - "12 1 example_2 \n", - "13 1 example_2 \n", - "14 1 example_2 \n", - "15 1 example_2 \n", - "16 1 example_2 \n", - "17 1 example_2 \n", - "18 1 example_2 \n", - "19 1 example_2 \n", - "20 1 example_2 \n", - "21 1 example_2 \n", - "22 1 example_2 \n", - "23 1 example_2 \n", - "24 1 example_2 \n", - "25 1 example_2 \n", - "26 1 example_2 \n", + " minibinder_aa_match_equidistant minibinder_aa_from_barcode_match \\\n", + "0 1 example_2 \n", + "1 1 example_2 \n", + "2 1 example_2 \n", + "3 1 example_2 \n", + "4 1 example_2 \n", + "5 1 example_2 \n", + "6 1 example_2 \n", + "7 1 example_2 \n", + "8 1 example_2 \n", + "9 1 example_2 \n", + "10 1 example_2 \n", + "11 1 example_2 \n", + "12 1 example_2 \n", + "13 1 example_2 \n", + "14 1 example_2 \n", + "15 1 example_2 \n", + "16 1 example_2 \n", + "17 1 example_2 \n", + "18 1 example_2 \n", + "19 1 example_2 \n", + "20 1 example_2 \n", + "21 1 example_2 \n", + "22 1 example_2 \n", + "23 1 example_2 \n", + "24 1 example_2 \n", + "25 1 example_2 \n", + "26 1 example_2 \n", "\n", " minibinder_aa_from_barcode_distance \n", "0 1 \n", @@ -1516,7 +1531,7 @@ "26 0 " ] }, - "execution_count": 42, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1525,6 +1540,97 @@ "# edit distance of parsed parts to reference\n", "pd.read_csv('3_mapped/simulate/sample_B.mapped.csv')" ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "946a66f4-b8f8-430b-8088-ada315c777aa", + "metadata": {}, + "outputs": [], + "source": [ + "from ngs_analysis.plot import plot_all" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c4fc347c-8d33-4ee0-af8f-d7f835c80097", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting sample vs. source for barcode... done in 0.15 seconds\n", + "Plotting abundance by sample for barcode... done in 0.567 seconds\n", + "Plotting sample vs. source for minibinder_aa... done in 0.0689 seconds\n", + "Plotting abundance by sample for minibinder_aa... done in 0.516 seconds\n" + ] + } + ], + "source": [ + "plot_all(simulate=True, output_prefix='figures/simulate/')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "aff2f2cf-8a71-4af6-aa02-7b75c0186c08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for image in glob('figures/simulate/heatmap*png'):\n", + " display(Image(image))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49843c6c-5660-49c7-9800-a4e1ebd6f50a", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {