{ "cells": [ { "cell_type": "markdown", "id": "a4cdce42", "metadata": {}, "source": [ "# NAME\n", "\n", "InferenceUsingTFHubEnformerGeneExprPredModel - Using TensorFlow to do gene expression prediction using a pre-trained model" ] }, { "cell_type": "markdown", "id": "d74d6520", "metadata": {}, "source": [ "# SYNOPSIS\n", "\n", "The following tutorial is based on the [Enformer usage notebook](https://github.com/deepmind/deepmind-research/blob/master/enformer/enformer-usage.ipynb). It uses a pre-trained model based on a transformer architecture trained as described in Avsec et al (2021) and introduced in Avsec's DeepMind blog post [Predicting gene expression with AI](https://www.deepmind.com/blog/predicting-gene-expression-with-ai).\n", "\n", "Running the code requires an Internet connection to download the model (from Google servers) and datasets (from GitHub, UCSC, and NIH).\n", "\n", "Some of this code is identical to that of `InferenceUsingTFHubMobileNetV2Model` notebook. Please look there for explanation for that code. As stated there, this will later be wrapped up into a high-level library to hide the details behind an API.\n", "\n", "**NOTE**: If running this model, please be aware that\n", "\n", "* the Docker image takes 3 GB or more of disk space;\n", "* the model and data takes 5 GB or more of disk space.\n", "\n", "meaning that you will need a total of **8 GB** of disk space. You may need at least **4 GB** of free memory to run the model.\n", "\n", "# COLOPHON\n", "\n", "The following document is either a POD file which can additionally be run as a Perl script or a Jupyter Notebook which can be run in [IPerl](https://p3rl.org/Devel::IPerl) (viewable online at [nbviewer](https://nbviewer.org/github/EntropyOrg/perl-AI-TensorFlow-Libtensorflow/blob/master/notebook/InferenceUsingTFHubEnformerGeneExprPredModel.ipynb)). If you are reading this as POD, there should be a generated list of Perl dependencies in the [CPANFILE](#CPANFILE) section.\n", "\n", "You will also need the executables `gunzip`, `bgzip`, and `samtools`. Furthermore,\n", "\n", " * `Bio::DB::HTS` requires `libhts` and\n", " * `PDL::Graphics::Gnuplot` requires `gnuplot`.\n", "\n", "If you are running the code, you may optionally install the [`tensorflow` Python package](https://www.tensorflow.org/install/pip) in order to access the `saved_model_cli` command, but this is only used for informational purposes." ] }, { "cell_type": "markdown", "id": "bb098c5b", "metadata": {}, "source": [ "# TUTORIAL\n", "\n", "## Load the library\n", "\n", "First, we need to load the `AI::TensorFlow::Libtensorflow` library and more helpers. We then create an `AI::TensorFlow::Libtensorflow::Status` object and helper function to make sure that the calls to the `libtensorflow` C library are working properly." ] }, { "cell_type": "code", "execution_count": 1, "id": "81eaec3a", "metadata": {}, "outputs": [], "source": [ "use strict;\n", "use warnings;\n", "use utf8;\n", "use constant IN_IPERL => !! $ENV{PERL_IPERL_RUNNING};\n", "no if IN_IPERL, warnings => 'redefine'; # fewer messages when re-running cells\n", "\n", "use feature qw(say);\n", "use Syntax::Construct qw( // );\n", "\n", "use lib::projectroot qw(lib);\n", "\n", "BEGIN {\n", " if( IN_IPERL ) {\n", " $ENV{TF_CPP_MIN_LOG_LEVEL} = 3;\n", " }\n", " require AI::TensorFlow::Libtensorflow;\n", "}\n", "\n", "use URI ();\n", "use HTTP::Tiny ();\n", "use Path::Tiny qw(path);\n", "\n", "use File::Which ();\n", "\n", "use List::Util ();\n", "\n", "use Data::Printer ( output => 'stderr', return_value => 'void', filters => ['PDL'] );\n", "use Data::Printer::Filter::PDL ();\n", "use Text::Table::Tiny qw(generate_table);\n", "\n", "my $s = AI::TensorFlow::Libtensorflow::Status->New;\n", "sub AssertOK {\n", " die \"Status $_[0]: \" . $_[0]->Message\n", " unless $_[0]->GetCode == AI::TensorFlow::Libtensorflow::Status::OK;\n", " return;\n", "}\n", "AssertOK($s);" ] }, { "cell_type": "markdown", "id": "e0262c6a", "metadata": {}, "source": [ "And create helpers for converting between `PDL` ndarrays and `TFTensor` ndarrays." ] }, { "cell_type": "code", "execution_count": 2, "id": "486e655b", "metadata": {}, "outputs": [], "source": [ "use PDL;\n", "use AI::TensorFlow::Libtensorflow::DataType qw(FLOAT);\n", "\n", "use FFI::Platypus::Memory qw(memcpy);\n", "use FFI::Platypus::Buffer qw(scalar_to_pointer);\n", "\n", "sub FloatPDLTOTFTensor {\n", " my ($p) = @_;\n", " return AI::TensorFlow::Libtensorflow::Tensor->New(\n", " FLOAT, [ reverse $p->dims ], $p->get_dataref, sub { undef $p }\n", " );\n", "}\n", "\n", "sub FloatTFTensorToPDL {\n", " my ($t) = @_;\n", "\n", " my $pdl = zeros(float,reverse( map $t->Dim($_), 0..$t->NumDims-1 ) );\n", "\n", " memcpy scalar_to_pointer( ${$pdl->get_dataref} ),\n", " scalar_to_pointer( ${$t->Data} ),\n", " $t->ByteSize;\n", " $pdl->upd_data;\n", "\n", " $pdl;\n", "}" ] }, { "cell_type": "markdown", "id": "e6186600", "metadata": {}, "source": [ "## Download model and data\n", "\n", "\n", "* [Enformer model](https://tfhub.dev/deepmind/enformer/1) from\n", "\n", " > Avsec Ž, Agarwal V, Visentin D, Ledsam JR, Grabska-Barwinska A, Taylor KR, Assael Y, Jumper J, Kohli P, Kelley DR. Effective gene expression prediction from sequence by integrating long-range interactions. *Nat Methods*. 2021 Oct;**18(10)**:1196-1203. doi: [10.1038/s41592-021-01252-x](https://doi.org/10.1038/s41592-021-01252-x). Epub 2021 Oct 4. PMID: [34608324](https://pubmed.ncbi.nlm.nih.gov/34608324); PMCID: [PMC8490152](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490152/).\n", "\n", "\n", "* [Human target dataset](https://github.com/calico/basenji/tree/master/manuscripts/cross2020) from\n", "\n", " > Kelley DR. Cross-species regulatory sequence activity prediction. *PLoS Comput Biol*. 2020 Jul 20;**16(7)**:e1008050. doi: [10.1371/journal.pcbi.1008050](https://doi.org/10.1371/journal.pcbi.1008050). PMID: [32687525](https://pubmed.ncbi.nlm.nih.gov/32687525); PMCID: [PMC7392335](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392335/).\n", "\n", "* [UCSC hg38 genome](https://www.ncbi.nlm.nih.gov/assembly/GCA_000001405.15). More info at ; [Genome Reference Consortium Human Build 38](https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/) from [Genome Reference Consortium](https://www.ncbi.nlm.nih.gov/grc).\n", "\n", " > Schneider VA, Graves-Lindsay T, Howe K, Bouk N, Chen HC, Kitts PA, Murphy TD, Pruitt KD, Thibaud-Nissen F, Albracht D, Fulton RS, Kremitzki M, Magrini V, Markovic C, McGrath S, Steinberg KM, Auger K, Chow W, Collins J, Harden G, Hubbard T, Pelan S, Simpson JT, Threadgold G, Torrance J, Wood JM, Clarke L, Koren S, Boitano M, Peluso P, Li H, Chin CS, Phillippy AM, Durbin R, Wilson RK, Flicek P, Eichler EE, Church DM. Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. *Genome Res.* 2017 May;**27(5)**:849-864. doi: [10.1101/gr.213611.116](https://doi.org/10.1101/gr.213611.116). Epub 2017 Apr 10. PMID: [28396521](https://pubmed.ncbi.nlm.nih.gov/28396521); PMCID: [PMC5411779](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411779/).\n", "\n", "* [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar/) file\n", "\n", " > Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: improving access to variant interpretations and supporting evidence. *Nucleic Acids Res.* 2018 Jan 4;**46(D1)**:D1062-D1067. doi: [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153). PMID: [29165669](https://pubmed.ncbi.nlm.nih.gov/29165669); PMCID: [PMC5753237](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753237/).\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "544a8780", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://tfhub.dev/deepmind/enformer/1?tf-hub-format=compressed to deepmind_enformer_1.tar.gz\n", "Downloading https://raw.githubusercontent.com/calico/basenji/master/manuscripts/cross2020/targets_human.txt to targets_human.txt\n", "Downloading http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz to hg38.fa.gz\n", "Downloading https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz to clinvar.vcf.gz\n" ] } ], "source": [ "# Model handle\n", "my $model_uri = URI->new( 'https://tfhub.dev/deepmind/enformer/1' );\n", "$model_uri->query_form( 'tf-hub-format' => 'compressed' );\n", "my $model_base = substr( $model_uri->path, 1 ) =~ s,/,_,gr;\n", "my $model_archive_path = \"${model_base}.tar.gz\";\n", "my $model_sequence_length = 393_216; # bp\n", "\n", "# Human targets from Basenji2 dataset\n", "my $targets_uri = URI->new('https://raw.githubusercontent.com/calico/basenji/master/manuscripts/cross2020/targets_human.txt');\n", "my $targets_path = 'targets_human.txt';\n", "\n", "# Human reference genome\n", "my $hg_uri = URI->new(\"http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz\");\n", "my $hg_gz_path = \"hg38.fa.gz\";\n", "# From http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/md5sum.txt\n", "my $hg_md5_digest = \"1c9dcaddfa41027f17cd8f7a82c7293b\";\n", "\n", "my $clinvar_uri = URI->new('https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz');\n", "my $clinvar_path = 'clinvar.vcf.gz';\n", "\n", "my $http = HTTP::Tiny->new;\n", "\n", "for my $download ( [ $model_uri => $model_archive_path ],\n", " [ $targets_uri => $targets_path ],\n", " [ $hg_uri => $hg_gz_path ],\n", " [ $clinvar_uri => $clinvar_path ],) {\n", " my ($uri, $path) = @$download;\n", " say \"Downloading $uri to $path\";\n", " next if -e $path;\n", " $http->mirror( $uri, $path );\n", "}" ] }, { "cell_type": "markdown", "id": "66efe3c4", "metadata": {}, "source": [ "Now we\n", "\n", "1. extract the saved model so that we can load it and\n", "2. check the MD5 sum of the reference genome to make sure it was downloaded correctly." ] }, { "cell_type": "code", "execution_count": 4, "id": "bae1ac83", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MD5 sum for hg38.fa.gz OK\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "use Archive::Extract;\n", "$Archive::Extract::DEBUG = 1;\n", "$Archive::Extract::PREFER_BIN = 1; # for the larger model, prefer bin\n", "if( ! -e $model_base ) {\n", " my $ae = Archive::Extract->new( archive => $model_archive_path );\n", " die \"Could not extract archive\" unless $ae->extract( to => $model_base );\n", "}\n", "\n", "use Digest::file qw(digest_file_hex);\n", "if( digest_file_hex( $hg_gz_path, \"MD5\" ) eq $hg_md5_digest ) {\n", " say \"MD5 sum for $hg_gz_path OK\";\n", "} else {\n", " die \"Digest for $hg_gz_path failed\";\n", "}" ] }, { "cell_type": "markdown", "id": "963170f4", "metadata": {}, "source": [ "In order to quickly seek for sequences in the reference genome FASTA, we\n", "\n", "1. convert the gzip'd file into a block gzip'd file and\n", "2. index that `.bgz` file using `faidx` from `samtools`." ] }, { "cell_type": "code", "execution_count": 5, "id": "d01b28e3", "metadata": {}, "outputs": [], "source": [ "(my $hg_uncompressed_path = $hg_gz_path) =~ s/\\.gz$//;\n", "my $hg_bgz_path = \"${hg_uncompressed_path}.bgz\";\n", "\n", "use IPC::Run;\n", "\n", "if( ! -e $hg_bgz_path ) {\n", " IPC::Run::run(\n", " [ qw(gunzip -c) ], '<', $hg_gz_path,\n", " '|',\n", " [ qw(bgzip -c) ], '>', $hg_bgz_path\n", " );\n", "}\n", "\n", "use Bio::Tools::Run::Samtools;\n", "\n", "my $hg_bgz_fai_path = \"${hg_bgz_path}.fai\";\n", "if( ! -e $hg_bgz_fai_path ) {\n", " my $faidx_tool = Bio::Tools::Run::Samtools->new( -command => 'faidx' );\n", " $faidx_tool->run( -fas => $hg_bgz_path )\n", " or die \"Could not index FASTA file $hg_bgz_path: \" . $faidx_tool->error_string;\n", "}" ] }, { "cell_type": "markdown", "id": "ef2d8929", "metadata": {}, "source": [ "## Model input and output specification\n", "\n", "Now we create a helper to call `saved_model_cli` and called `saved_model_cli scan` to ensure that the model is I/O-free for security reasons." ] }, { "cell_type": "code", "execution_count": 6, "id": "7e71c582", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking with saved_model_cli scan:\n", "MetaGraph with tag set ['serve'] does not contain the default denylisted ops: {'ReadFile', 'PrintV2', 'WriteFile'}\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sub saved_model_cli {\n", " my (@rest) = @_;\n", " if( File::Which::which('saved_model_cli')) {\n", " system(qw(saved_model_cli), @rest ) == 0\n", " or die \"Could not run saved_model_cli\";\n", " } else {\n", " warn \"saved_model_cli(): Install the tensorflow Python package to get the `saved_model_cli` command.\\n\";\n", " return -1;\n", " }\n", "}\n", "\n", "say \"Checking with saved_model_cli scan:\";\n", "saved_model_cli( qw(scan),\n", " qw(--dir) => $model_base,\n", ");" ] }, { "cell_type": "markdown", "id": "2d572b9e", "metadata": {}, "source": [ "We need to see what the inputs and outputs of this model are so `saved_model_cli show` should show us that:" ] }, { "cell_type": "code", "execution_count": 7, "id": "195ea68d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n", "\n", "signature_def['__saved_model_init_op']:\n", " The given SavedModel SignatureDef contains the following input(s):\n", " The given SavedModel SignatureDef contains the following output(s):\n", " outputs['__saved_model_init_op'] tensor_info:\n", " dtype: DT_INVALID\n", " shape: unknown_rank\n", " name: NoOp\n", " Method name is: \n", "\n", "Concrete Functions:" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "saved_model_cli( qw(show),\n", " qw(--dir) => $model_base,\n", " qw(--all),\n", ");" ] }, { "cell_type": "markdown", "id": "9358fcbc", "metadata": {}, "source": [ "It appears that it does not! What we can do is load the model using `tensorflow` in Python and then save it to a new path. Now when we run `saved_model_cli show` on this new model path, it shows the correct inputs and outputs." ] }, { "cell_type": "code", "execution_count": 8, "id": "2eabaa84", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n", "\n", "signature_def['__saved_model_init_op']:\n", " The given SavedModel SignatureDef contains the following input(s):\n", " The given SavedModel SignatureDef contains the following output(s):\n", " outputs['__saved_model_init_op'] tensor_info:\n", " dtype: DT_INVALID\n", " shape: unknown_rank\n", " name: NoOp\n", " Method name is: \n", "\n", "signature_def['serving_default']:\n", " The given SavedModel SignatureDef contains the following input(s):\n", " inputs['args_0'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 393216, 4)\n", " name: serving_default_args_0:0\n", " The given SavedModel SignatureDef contains the following output(s):\n", " outputs['human'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 896, 5313)\n", " name: StatefulPartitionedCall:0\n", " outputs['mouse'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 896, 1643)\n", " name: StatefulPartitionedCall:1\n", " Method name is: tensorflow/serving/predict\n", "\n", "Concrete Functions:\n", " Function Name: 'predict_on_batch'\n", " Option #1\n", " Callable with:\n", " Argument #1\n", " args_0: TensorSpec(shape=(None, 393216, 4), dtype=tf.float32, name='args_0')\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my $new_model_base = \"${model_base}_new\";\n", "\n", "system( qw(python3), qw(-c) => < $new_model_base,\n", " qw(--all),\n", ");" ] }, { "cell_type": "markdown", "id": "70e831e0", "metadata": {}, "source": [ "We want to use the `serve` tag-set and\n", "\n", "* the input `args_0` which has the name `serving_default_args_0:0` and\n", "* the output `human` which has the name `StatefulPartitionedCall:0`.\n", "\n", "all of which are `DT_FLOAT`.\n", "\n", "Make note of the shapes that those take. Per the [model description](https://tfhub.dev/deepmind/enformer/1) at TensorFlow Hub:\n", "\n", "> The input sequence length is 393,216 with the prediction corresponding to 128 base pair windows for the center 114,688 base pairs. The input sequence is one hot encoded using the order of indices corresponding to 'ACGT' with N values being all zeros.\n", "\n", "The input shape `(-1, 393216, 4)` thus represents dimensions `[batch size] x [sequence length] x [one-hot encoding of ACGT]`.\n", "\n", "The output shape `(-1, 896, 5313)` represents dimensions `[batch size] x [ predictions along 114,688 base pairs / 128 base pair windows ] x [ human target by index ]`. We can confirm this by doing some calculations:" ] }, { "cell_type": "code", "execution_count": 9, "id": "95cd13d7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of predictions: 896\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my $model_central_base_pairs_length = 114_688; # bp\n", "my $model_central_base_pair_window_size = 128; # bp / prediction\n", "\n", "say \"Number of predictions: \", $model_central_base_pairs_length / $model_central_base_pair_window_size;" ] }, { "cell_type": "markdown", "id": "59881dd3", "metadata": {}, "source": [ "and by looking at the targets file:" ] }, { "cell_type": "code", "execution_count": 10, "id": "97ec413e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of targets: 5313\n", "\n", "First 5:\n", "------------------------------------------------------------------------------------------------------------------------------------------------\n", " index genome identifier file clip scale sum_stat description \n", "------------------------------------------------------------------------------------------------------------------------------------------------\n", " 0 0 0 ENCFF833POA encode/ENCSR000EIJ 32 2 mean DNASE:cerebellum male adult (27 years) and male adult (35 years) \n", " 1 1 0 ENCFF110QGM encode/ENCSR000EIK 32 2 mean DNASE:frontal cortex male adult (27 years) and male adult (35 years) \n", " 2 2 0 ENCFF880MKD encode/ENCSR000EIL 32 2 mean DNASE:chorion \n", " 3 3 0 ENCFF463ZLQ encode/ENCSR000EIP 32 2 mean DNASE:Ishikawa treated with 0.02% dimethyl sulfoxide for 1 hour \n", " 4 4 0 ENCFF890OGQ encode/ENCSR000EIS 32 2 mean DNASE:GM03348 \n", "------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "use Data::Frame;\n", "\n", "my $df = Data::Frame->from_csv( $targets_path, sep => \"\\t\" )\n", " ->transform({\n", " file => sub {\n", " my ($col, $df) = @_;\n", " # clean up the paths in 'file' column\n", " [map { join \"/\", (split('/', $_))[7..8] } $col->list];\n", " }\n", " });\n", "\n", "say \"Number of targets: \", $df->nrow;\n", "\n", "say \"\";\n", "\n", "say \"First 5:\";\n", "say $df->head(5);" ] }, { "cell_type": "markdown", "id": "66d7ef6b", "metadata": {}, "source": [ "## Load the model\n", "\n", "Let's now load the model in Perl and get the inputs and outputs into a data structure by name." ] }, { "cell_type": "code", "execution_count": 11, "id": "e35c3294", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104minputs_args_0\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;176mAI::TensorFlow::Libtensorflow::Output\u001b[m \u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104mindex\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m0\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104moper\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;176mAI::TensorFlow::Libtensorflow::Operation\u001b[m \u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104mName\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;107mserving_default_args_0\u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mNumInputs\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m0\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mNumOutputs\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m1\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mOpType\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;107mPlaceholder\u001b[m\u001b[0;38;5;81m\"\u001b[m\n", " \u001b[0;38;5;81m}\u001b[m\n", " \u001b[0;38;5;81m}\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104moutputs_human\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;176mAI::TensorFlow::Libtensorflow::Output\u001b[m \u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104mindex\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m0\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104moper\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;176mAI::TensorFlow::Libtensorflow::Operation\u001b[m \u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104mName\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;107mStatefulPartitionedCall\u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mNumInputs\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m274\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mNumOutputs\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m2\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mOpType\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;107mStatefulPartitionedCall\u001b[m\u001b[0;38;5;81m\"\u001b[m\n", " \u001b[0;38;5;81m}\u001b[m\n", " \u001b[0;38;5;81m}\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104moutputs_mouse\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;176mAI::TensorFlow::Libtensorflow::Output\u001b[m \u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104mindex\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m1\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104moper\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;176mAI::TensorFlow::Libtensorflow::Operation\u001b[m \u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104mName\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;107mStatefulPartitionedCall\u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mNumInputs\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m274\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mNumOutputs\u001b[m\u001b[0;38;5;81m \u001b[m\u001b[0;38;5;209m2\u001b[m\u001b[0;38;5;81m,\u001b[m\n", " \u001b[0;38;5;104mOpType\u001b[m \u001b[0;38;5;81m \u001b[m\u001b[0;38;5;81m\"\u001b[m\u001b[0;38;5;107mStatefulPartitionedCall\u001b[m\u001b[0;38;5;81m\"\u001b[m\n", " \u001b[0;38;5;81m}\u001b[m\n", " \u001b[0;38;5;81m}\u001b[m\n", "\u001b[0;38;5;81m}\u001b[m\n" ] } ], "source": [ "my $opt = AI::TensorFlow::Libtensorflow::SessionOptions->New;\n", "\n", "my @tags = ( 'serve' );\n", "my $graph = AI::TensorFlow::Libtensorflow::Graph->New;\n", "my $session = AI::TensorFlow::Libtensorflow::Session->LoadFromSavedModel(\n", " $opt, undef, $new_model_base, \\@tags, $graph, undef, $s\n", ");\n", "AssertOK($s);\n", "\n", "my %puts = (\n", " ## Inputs\n", " inputs_args_0 =>\n", " AI::TensorFlow::Libtensorflow::Output->New({\n", " oper => $graph->OperationByName('serving_default_args_0'),\n", " index => 0,\n", " }),\n", "\n", " ## Outputs\n", " outputs_human =>\n", " AI::TensorFlow::Libtensorflow::Output->New({\n", " oper => $graph->OperationByName('StatefulPartitionedCall'),\n", " index => 0,\n", " }),\n", " outputs_mouse =>\n", " AI::TensorFlow::Libtensorflow::Output->New({\n", " oper => $graph->OperationByName('StatefulPartitionedCall'),\n", " index => 1,\n", " }),\n", ");\n", "\n", "p %puts;" ] }, { "cell_type": "markdown", "id": "cda15cc2", "metadata": {}, "source": [ "We need a helper to simplify running the session and getting just the predictions that we want." ] }, { "cell_type": "code", "execution_count": 12, "id": "0ba71ab8", "metadata": {}, "outputs": [], "source": [ "my $predict_on_batch = sub {\n", " my ($session, $t) = @_;\n", " my @outputs_t;\n", "\n", " $session->Run(\n", " undef,\n", " [$puts{inputs_args_0}], [$t],\n", " [$puts{outputs_human}], \\@outputs_t,\n", " undef,\n", " undef,\n", " $s\n", " );\n", " AssertOK($s);\n", "\n", " return $outputs_t[0];\n", "};\n", "\n", "undef;" ] }, { "cell_type": "markdown", "id": "c6c2695b", "metadata": {}, "source": [ "## Encoding the data\n", "\n", "The model specifies that the way to get a sequence of DNA bases into a `TFTensor` is to use [one-hot encoding](https://en.wikipedia.org/wiki/One-hot#Machine_learning_and_statistics) in the order `ACGT`.\n", "\n", "This means that the bases are represented as vectors of length 4:\n", "\n", "| base | vector encoding |\n", "|------|-----------------|\n", "| A | `[1 0 0 0]` |\n", "| C | `[0 1 0 0]` |\n", "| G | `[0 0 1 0]` |\n", "| T | `[0 0 0 1]` |\n", "| N | `[0 0 0 0]` |\n", "\n", "\n", "We can achieve this encoding by creating a lookup table with a PDL ndarray. This could be done by creating a byte PDL ndarray of dimensions `[ 256 4 ]` to directly look up the the numeric value of characters 0-255, but here we'll go with a smaller `[ 5 4 ]` ndarray and transliterate the numeric values of `NACGT` to 0-4." ] }, { "cell_type": "code", "execution_count": 13, "id": "e8d7273d", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing one-hot encoding:\n", "\n", "Encoder is\n", "PDL: Float D [5,4]\n", "[\n", " [0 1 0 0 0]\n", " [0 0 1 0 0]\n", " [0 0 0 1 0]\n", " [0 0 0 0 1]\n", "]\n", "\n", "One-hot encoding of sequence 'ACGTNtgcan' is:\n", "PDL: Float D [4,10]\n", "[\n", " [1 0 0 0]\n", " [0 1 0 0]\n", " [0 0 1 0]\n", " [0 0 0 1]\n", " [0 0 0 0]\n", " [0 0 0 1]\n", " [0 0 1 0]\n", " [0 1 0 0]\n", " [1 0 0 0]\n", " [0 0 0 0]\n", "]\n", "\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "use PDL;\n", "\n", "our $SHOW_ENCODER = 1;\n", "\n", "sub one_hot_dna {\n", " my ($seq) = @_;\n", "\n", " my $from_alphabet = \"NACGT\";\n", " my $to_alphabet = pack \"C*\", 0..length($from_alphabet)-1;\n", "\n", " # sequences from UCSC genome have both uppercase and lowercase bases\n", " my $from_alphabet_tr = $from_alphabet . lc $from_alphabet;\n", " my $to_alphabet_tr = $to_alphabet x 2;\n", "\n", " my $p = zeros(byte, bytes::length($seq));\n", " my $p_dataref = $p->get_dataref;\n", " ${ $p_dataref } = $seq;\n", " eval \"tr/$from_alphabet_tr/$to_alphabet_tr/\" for ${ $p_dataref };\n", " $p->upd_data;\n", "\n", " my $encoder = append(float(0), identity(float(length($from_alphabet)-1)) );\n", " say \"Encoder is\\n\", $encoder->info, $encoder if $SHOW_ENCODER;\n", "\n", " my $encoded = $encoder->index( $p->dummy(0) );\n", "\n", " return $encoded;\n", "}\n", "\n", "####\n", "\n", "{\n", "\n", "say \"Testing one-hot encoding:\\n\";\n", "\n", "my $onehot_test_seq = \"ACGTNtgcan\";\n", "my $test_encoded = one_hot_dna( $onehot_test_seq );\n", "$SHOW_ENCODER = 0;\n", "\n", "say \"One-hot encoding of sequence '$onehot_test_seq' is:\";\n", "say $test_encoded->info, $test_encoded;\n", "\n", "}" ] }, { "cell_type": "markdown", "id": "345cc422", "metadata": {}, "source": [ "Note that in the above, the PDL ndarray's\n", "\n", " * first dimension is 4 which matches the last dimension of the input `TFTensor`;\n", " * second dimension is the sequence length which matches the penultimate dimension of the input `TFTensor`.\n", "\n", "Now we need a way to deal with the sequence interval. We're going to use 1-based coordinates as BioPerl does. In fact, we'll extend a BioPerl class." ] }, { "cell_type": "code", "execution_count": 14, "id": "39d330f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing interval resizing:\n", "\n", "Testing interval chr11:4..8 with length 5\n", "-----\n", "Interval: chr11:4..8 -> chr11:4..8, length 5 : 5 -> 5 (+ 0)\n", "Interval: chr11:4..8 -> chr11:3..8, length 6 : 5 -> 6 (+ 1)\n", "Interval: chr11:4..8 -> chr11:3..9, length 7 : 5 -> 7 (+ 2)\n", "Interval: chr11:4..8 -> chr11:2..9, length 8 : 5 -> 8 (+ 3)\n", "Interval: chr11:4..8 -> chr11:2..10, length 9 : 5 -> 9 (+ 4)\n", "Interval: chr11:4..8 -> chr11:1..10, length 10 : 5 -> 10 (+ 5)\n", "\n", "Testing interval chr11:5..8 with length 4\n", "-----\n", "Interval: chr11:5..8 -> chr11:5..8, length 4 : 4 -> 4 (+ 0)\n", "Interval: chr11:5..8 -> chr11:5..9, length 5 : 4 -> 5 (+ 1)\n", "Interval: chr11:5..8 -> chr11:4..9, length 6 : 4 -> 6 (+ 2)\n", "Interval: chr11:5..8 -> chr11:4..10, length 7 : 4 -> 7 (+ 3)\n", "Interval: chr11:5..8 -> chr11:3..10, length 8 : 4 -> 8 (+ 4)\n", "Interval: chr11:5..8 -> chr11:3..11, length 9 : 4 -> 9 (+ 5)\n", "\n", "Testing interval chr11:5..9 with length 5\n", "-----\n", "Interval: chr11:5..9 -> chr11:5..9, length 5 : 5 -> 5 (+ 0)\n", "Interval: chr11:5..9 -> chr11:4..9, length 6 : 5 -> 6 (+ 1)\n", "Interval: chr11:5..9 -> chr11:4..10, length 7 : 5 -> 7 (+ 2)\n", "Interval: chr11:5..9 -> chr11:3..10, length 8 : 5 -> 8 (+ 3)\n", "Interval: chr11:5..9 -> chr11:3..11, length 9 : 5 -> 9 (+ 4)\n", "Interval: chr11:5..9 -> chr11:2..11, length 10 : 5 -> 10 (+ 5)\n", "\n", "Testing interval chr11:6..9 with length 4\n", "-----\n", "Interval: chr11:6..9 -> chr11:6..9, length 4 : 4 -> 4 (+ 0)\n", "Interval: chr11:6..9 -> chr11:6..10, length 5 : 4 -> 5 (+ 1)\n", "Interval: chr11:6..9 -> chr11:5..10, length 6 : 4 -> 6 (+ 2)\n", "Interval: chr11:6..9 -> chr11:5..11, length 7 : 4 -> 7 (+ 3)\n", "Interval: chr11:6..9 -> chr11:4..11, length 8 : 4 -> 8 (+ 4)\n", "Interval: chr11:6..9 -> chr11:4..12, length 9 : 4 -> 9 (+ 5)\n", "\n" ] } ], "source": [ "package Interval {\n", " use Bio::Location::Simple ();\n", "\n", " use parent qw(Bio::Location::Simple);\n", "\n", " sub center {\n", " my $self = shift;\n", " my $center = int( ($self->start + $self->end ) / 2 );\n", " my $delta = ($self->start + $self->end ) % 2;\n", " return $center + $delta;\n", " }\n", "\n", " sub resize {\n", " my ($self, $width) = @_;\n", " my $new_interval = $self->clone;\n", "\n", " my $center = $self->center;\n", " my $half = int( ($width-1) / 2 );\n", " my $offset = ($width-1) % 2;\n", "\n", " $new_interval->start( $center - $half - $offset );\n", " $new_interval->end( $center + $half );\n", "\n", " return $new_interval;\n", " }\n", "\n", " use overload '\"\"' => \\&_op_stringify;\n", "\n", " sub _op_stringify { sprintf \"%s:%s\", $_[0]->seq_id // \"(no sequence)\", $_[0]->to_FTstring }\n", "}\n", "\n", "#####\n", "\n", "{\n", "\n", "say \"Testing interval resizing:\\n\";\n", "sub _debug_resize {\n", " my ($interval, $to, $msg) = @_;\n", "\n", " my $resized_interval = $interval->resize($to);\n", "\n", " die \"Wrong interval size for $interval --($to)--> $resized_interval\"\n", " unless $resized_interval->length == $to;\n", "\n", " say sprintf \"Interval: %s -> %s, length %2d : %s\",\n", " $interval,\n", " $resized_interval, $resized_interval->length,\n", " $msg;\n", "}\n", "\n", "for my $interval_spec ( [4, 8], [5, 8], [5, 9], [6, 9]) {\n", " my ($start, $end) = @$interval_spec;\n", " my $test_interval = Interval->new( -seq_id => 'chr11', -start => $start, -end => $end );\n", " say sprintf \"Testing interval %s with length %d\", $test_interval, $test_interval->length;\n", " say \"-----\";\n", " for(0..5) {\n", " my $base = $test_interval->length;\n", " my $to = $base + $_;\n", " _debug_resize $test_interval, $to, \"$base -> $to (+ $_)\";\n", " }\n", " say \"\";\n", "}\n", "\n", "}\n", "\n", "undef;" ] }, { "cell_type": "code", "execution_count": 15, "id": "9dd8ddfd", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing sequence extraction:\n", "1 base: G (length 1)\n", "3 bases: NNN (length 3)\n", "5 bases: NNNNN (length 5)\n", "chr11 is of length 135086622\n", "chr11 bases: NNNNNNNNNN...NNNNNNNNNN (length 135086622)\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "use Bio::DB::HTS::Faidx;\n", "\n", "my $hg_db = Bio::DB::HTS::Faidx->new( $hg_bgz_path );\n", "\n", "sub extract_sequence {\n", " my ($db, $interval) = @_;\n", "\n", " my $chrom_length = $db->length($interval->seq_id);\n", "\n", " my $trimmed_interval = $interval->clone;\n", " $trimmed_interval->start( List::Util::max( $interval->start, 1 ) );\n", " $trimmed_interval->end( List::Util::min( $interval->end , $chrom_length ) );\n", "\n", " # Bio::DB::HTS::Faidx is 0-based for both start and end points\n", " my $seq = $db->get_sequence2_no_length(\n", " $trimmed_interval->seq_id,\n", " $trimmed_interval->start - 1,\n", " $trimmed_interval->end - 1,\n", " );\n", "\n", " my $pad_upstream = 'N' x List::Util::max( -($interval->start-1), 0 );\n", " my $pad_downstream = 'N' x List::Util::max( $interval->end - $chrom_length, 0 );\n", "\n", " return join '', $pad_upstream, $seq, $pad_downstream;\n", "}\n", "\n", "sub seq_info {\n", " my ($seq, $n) = @_;\n", " $n ||= 10;\n", " if( length $seq > $n ) {\n", " sprintf \"%s...%s (length %d)\", uc substr($seq, 0, $n), uc substr($seq, -$n), length $seq;\n", " } else {\n", " sprintf \"%s (length %d)\", uc $seq, length $seq;\n", " }\n", "}\n", "\n", "####\n", "\n", "{\n", "\n", "say \"Testing sequence extraction:\";\n", "\n", "say \"1 base: \", seq_info\n", " extract_sequence( $hg_db,\n", " Interval->new( -seq_id => 'chr11',\n", " -start => 35_082_742 + 1,\n", " -end => 35_082_742 + 1 ) );\n", "\n", "say \"3 bases: \", seq_info\n", " extract_sequence( $hg_db,\n", " Interval->new( -seq_id => 'chr11',\n", " -start => 1,\n", " -end => 1 )->resize(3) );\n", "\n", "say \"5 bases: \", seq_info\n", " extract_sequence( $hg_db,\n", " Interval->new( -seq_id => 'chr11',\n", " -start => $hg_db->length('chr11'),\n", " -end => $hg_db->length('chr11') )->resize(5) );\n", "\n", "say \"chr11 is of length \", $hg_db->length('chr11');\n", "say \"chr11 bases: \", seq_info\n", " extract_sequence( $hg_db,\n", " Interval->new( -seq_id => 'chr11',\n", " -start => 1,\n", " -end => $hg_db->length('chr11') )->resize( $hg_db->length('chr11') ) );\n", "}" ] }, { "cell_type": "markdown", "id": "082e2c46", "metadata": {}, "source": [ "Now we can use the same target interval that is used in the example notebook which recreates part of [figure 1](https://www.nature.com/articles/s41592-021-01252-x/figures/1) from the Enformer paper." ] }, { "cell_type": "code", "execution_count": 16, "id": "2292a3ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target interval: chr11:35082743..35197430 with length 114688\n", "Target sequence is GGTGGCAGCC...ATCTCCTTTT (length 114688)\n", "\n", "Resized interval: chr11:34943479..35336694 with length 393216\n", "Resized sequence is ACTAGTTCTA...GGCCCAAATC (length 393216)\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my $target_interval = Interval->new( -seq_id => 'chr11',\n", " -start => 35_082_742 + 1, # BioPerl is 1-based\n", " -end => 35_197_430 );\n", "\n", "say \"Target interval: $target_interval with length @{[ $target_interval->length ]}\";\n", "\n", "die \"Target interval is not $model_central_base_pairs_length bp long\"\n", " unless $target_interval->length == $model_central_base_pairs_length;\n", "\n", "say \"Target sequence is \", seq_info extract_sequence( $hg_db, $target_interval );\n", "\n", "\n", "say \"\";\n", "\n", "\n", "my $resized_interval = $target_interval->resize( $model_sequence_length );\n", "say \"Resized interval: $resized_interval with length @{[ $resized_interval->length ]}\";\n", "\n", "die \"resize() is not working properly!\" unless $resized_interval->length == $model_sequence_length;\n", "\n", "my $seq = extract_sequence( $hg_db, $resized_interval );\n", "\n", "say \"Resized sequence is \", seq_info($seq);" ] }, { "cell_type": "markdown", "id": "dcaad7e2", "metadata": {}, "source": [ "To prepare the input we have to one-hot encode this resized sequence and give it a dummy dimension at the end to indicate that it is is a batch with a single sequence. Then we can turn the PDL ndarray into a `TFTensor` and pass it to our prediction function." ] }, { "cell_type": "code", "execution_count": 17, "id": "1eeca53a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PDL: Float D [4,393216,1]\n" ] } ], "source": [ "my $sequence_one_hot = one_hot_dna( $seq )->dummy(-1);\n", "\n", "say $sequence_one_hot->info; undef;" ] }, { "cell_type": "code", "execution_count": 18, "id": "5effa39f", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[0;38;5;176mAI::TensorFlow::Libtensorflow::Tensor\u001b[m \u001b[0;38;5;81m{\u001b[m\n", " \u001b[0;38;5;104mType \u001b[m \u001b[0;38;5;176mFLOAT\u001b[m\n", " \u001b[0;38;5;104mDims \u001b[m \u001b[0;38;5;81m[\u001b[m \u001b[0;38;5;209m1\u001b[m \u001b[0;38;5;209m896\u001b[m \u001b[0;38;5;209m5313\u001b[m \u001b[0;38;5;81m]\u001b[m\n", " \u001b[0;38;5;104mNumDims \u001b[m \u001b[0;38;5;209m3\u001b[m\n", " \u001b[0;38;5;104mElementCount \u001b[m \u001b[0;38;5;209m4760448\u001b[m\n", "\u001b[0;38;5;81m}\u001b[m\n", "\n", "\n", "Devel::Timer Report -- Total time: 14.5641 secs\n", "Interval Time Percent\n", "----------------------------------------------\n", "01 -> 02 14.5634 100.00% prediction of sequence -> End of prediction of sequence\n", "02 -> 03 0.0007 0.00% End of prediction of sequence -> END\n", "00 -> 01 0.0000 0.00% INIT -> prediction of sequence\n" ] } ], "source": [ "use Devel::Timer;\n", "my $t = Devel::Timer->new;\n", "\n", "$t->mark('prediction of sequence');\n", "\n", "my $predictions = $predict_on_batch->( $session, FloatPDLTOTFTensor( $sequence_one_hot ) );\n", "\n", "$t->mark('End of prediction of sequence');\n", "\n", "p $predictions;\n", "\n", "$t->mark('END');\n", "$t->report();" ] }, { "cell_type": "markdown", "id": "d57c49ed", "metadata": {}, "source": [ "Now we turn the `TFTensor` output into a PDL ndarray." ] }, { "cell_type": "code", "execution_count": 19, "id": "900163c8", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PDL: Float D [5313,896]\n" ] } ], "source": [ "my $predictions_p = FloatTFTensorToPDL($predictions)->slice(',,(0)');\n", "say $predictions_p->info; undef;" ] }, { "cell_type": "markdown", "id": "8f751027", "metadata": {}, "source": [ "## Plot predicted tracks\n", "\n", "These predictions can be plotted " ] }, { "cell_type": "code", "execution_count": 20, "id": "ee878a9b", "metadata": {}, "outputs": [ { "data": { "image/png": "‰PNG\r\n\u001a\n\u0000\u0000\u0000\rIHDR\u0000\u0000\u0003è\u0000\u0000\u0003 \b\u0002\u0000\u0000\u0000?zßj\u0000\u0000 \u0000IDATxœìÝw`\u0014Uâ\u0007ð7[Ò;!\u0004\u0002\u0004¥*5\u001a:\b§ \bêÞᝠ`AQ\u0004\u0005EEAE¹\u0013\u007fØ\u000e\u0005\u000bœ¨X°R\u0014’€”\u0018\b$@zÝd{oS\u007f\u007fLX–ô,‰aÈ÷ó×îì”7»³³ßyóÞ[J\u0010\u0004\u0002\u0000\u0000\u0000\u0000\u0000W7Yg\u0017\u0000\u0000\u0000\u0000\u0000\u0000Z†à\u000e\u0000\u0000\u0000\u0000 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'CAGE:Keratinocyte - epidermal' => 4799 => log10(1 + $predictions_p->slice('(4799)')) ],\n", ");\n", "\n", "use PDL::Graphics::Gnuplot;\n", "\n", "my $plot_output_path = 'enformer-target-interval-tracks.png';\n", "my $gp = gpwin('pngcairo', font => \",10\", output => $plot_output_path, size => [10,2. * @tracks], aa => 2 );\n", "\n", "$gp->multiplot( layout => [1, scalar @tracks], title => $target_interval );\n", "\n", "$gp->options(\n", " offsets => [ graph => \"0.01, 0, 0, 0\" ],\n", " lmargin => \"at screen 0.05\",\n", ");\n", "\n", "my $x = zeroes($predictions_p->dim(1))->xlinvals($target_interval->start, $target_interval->end);\n", "\n", "my @tics_opts = (mirror => 0, out => 1);\n", "\n", "for my $i (0..$#tracks) {\n", " my ($title, $id, $y) = @{$tracks[$i]};\n", " $gp->plot( {\n", " title => $title,\n", " border => [2],\n", " ytics => { @tics_opts, locations => [ ceil(($y->max-$y->min)/2)->sclr ] },\n", " ( $i == $#tracks\n", " ? ( xtics => { format => '%.3f', @tics_opts } )\n", " : ( xtics => 0 ) ),\n", " ( $i == $#tracks ? ( xlabel => 'location ({/Symbol \\264}10^7 bases)' ) : () ),\n", "\n", " },\n", " with => 'filledcurves',\n", " #'lc' => '#086eb5',\n", "\n", " # $x scaled by 1e7; filled curve between $y and the x-axis\n", " $x / 1e7, $y, pdl(0)\n", " );\n", "}\n", "\n", "$gp->end_multi;\n", "\n", "$gp->close;\n", "\n", "if( IN_IPERL ) {\n", " IPerl->png( bytestream => path($plot_output_path)->slurp_raw );\n", "}" ] }, { "cell_type": "markdown", "id": "b598bb55", "metadata": {}, "source": [ "## Parts of the original notebook that fall outside the scope\n", "\n", "In the orignal notebook, there are several more steps that have not been ported here:\n", "\n", " 1. \"Compute contribution scores\":\n", "\n", " This task requires implementing `@tf.function` to compile gradients.\n", "\n", "\n", " 2. \"Predict the effect of a genetic variant\" and \"Score multiple variants\":\n", "\n", " The first task is possible, but the second task requires loading a pre-processing pipeline for scikit-learn and unfortunately this pipeline is stored as a pickle file that is valid for an older version of scikit-learn (version 0.23.2) and as such its behaviour is tied to that version." ] }, { "cell_type": "code", "execution_count": 21, "id": "af1e93b2", "metadata": {}, "outputs": [], "source": [ "# Some code that could be used for working with variants.\n", "1 if <<'COMMENT';\n", "\n", "use Bio::DB::HTS::VCF;\n", "\n", "my $clinvar_tbi_path = \"${clinvar_path}.tbi\";\n", "unless( -f $clinvar_tbi_path ) {\n", " system( qw(tabix), $clinvar_path );\n", "}\n", "my $v = Bio::DB::HTS::VCF->new( filename => $clinvar_path );\n", "$v->num_variants\n", "\n", "COMMENT\n", "\n", "undef;" ] }, { "cell_type": "markdown", "id": "909d2b52", "metadata": {}, "source": [ "# RESOURCE USAGE" ] }, { "cell_type": "code", "execution_count": 22, "id": "0afb74d4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Disk space usage: 4.66G\n" ] } ], "source": [ "use Filesys::DiskUsage qw/du/;\n", "\n", "my $total = du( { 'human-readable' => 1, dereference => 1 },\n", " $model_archive_path, $model_base, $new_model_base,\n", "\n", " $targets_path,\n", "\n", " $hg_gz_path,\n", " $hg_bgz_path, $hg_bgz_fai_path,\n", "\n", " $clinvar_path,\n", "\n", " $plot_output_path,\n", ");\n", "\n", "say \"Disk space usage: $total\"; undef;" ] } ], "metadata": { "kernelspec": { "display_name": "IPerl 0.012", "language": "perl", "name": "iperl" }, "language_info": { "file_extension": ".pl", "mimetype": "text/x-perl", "name": "perl", "version": "5.36.0" } }, "nbformat": 4, "nbformat_minor": 5 }