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lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod  view on Meta::CPAN

# PODNAME: AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubEnformerGeneExprPredModel


## DO NOT EDIT. Generated from notebook/InferenceUsingTFHubEnformerGeneExprPredModel.ipynb using ./maint/process-notebook.pl.

use strict;
use warnings;
use utf8;
use constant IN_IPERL => !! $ENV{PERL_IPERL_RUNNING};
no if IN_IPERL, warnings => 'redefine'; # fewer messages when re-running cells

use feature qw(say);
use Syntax::Construct qw( // );

use lib::projectroot qw(lib);

BEGIN {
    if( IN_IPERL ) {
        $ENV{TF_CPP_MIN_LOG_LEVEL} = 3;
    }
    require AI::TensorFlow::Libtensorflow;
}

use URI ();
use HTTP::Tiny ();
use Path::Tiny qw(path);

use File::Which ();

use List::Util ();

use Data::Printer ( output => 'stderr', return_value => 'void', filters => ['PDL'] );
use Data::Printer::Filter::PDL ();
use Text::Table::Tiny qw(generate_table);

my $s = AI::TensorFlow::Libtensorflow::Status->New;
sub AssertOK {
    die "Status $_[0]: " . $_[0]->Message
        unless $_[0]->GetCode == AI::TensorFlow::Libtensorflow::Status::OK;
    return;
}
AssertOK($s);

use PDL;
use AI::TensorFlow::Libtensorflow::DataType qw(FLOAT);

use FFI::Platypus::Memory qw(memcpy);
use FFI::Platypus::Buffer qw(scalar_to_pointer);

sub FloatPDLTOTFTensor {
    my ($p) = @_;
    return AI::TensorFlow::Libtensorflow::Tensor->New(
        FLOAT, [ reverse $p->dims ], $p->get_dataref, sub { undef $p }
    );
}

sub FloatTFTensorToPDL {
    my ($t) = @_;

    my $pdl = zeros(float,reverse( map $t->Dim($_), 0..$t->NumDims-1 ) );

    memcpy scalar_to_pointer( ${$pdl->get_dataref} ),
        scalar_to_pointer( ${$t->Data} ),
        $t->ByteSize;
    $pdl->upd_data;

    $pdl;
}

# Model handle
my $model_uri = URI->new( 'https://tfhub.dev/deepmind/enformer/1' );
$model_uri->query_form( 'tf-hub-format' => 'compressed' );
my $model_base = substr( $model_uri->path, 1 ) =~ s,/,_,gr;
my $model_archive_path = "${model_base}.tar.gz";
my $model_sequence_length = 393_216; # bp

# Human targets from Basenji2 dataset
my $targets_uri  = URI->new('https://raw.githubusercontent.com/calico/basenji/master/manuscripts/cross2020/targets_human.txt');
my $targets_path = 'targets_human.txt';

# Human reference genome
my $hg_uri    = URI->new("http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz");
my $hg_gz_path   = "hg38.fa.gz";
# From http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/md5sum.txt
my $hg_md5_digest = "1c9dcaddfa41027f17cd8f7a82c7293b";

my $clinvar_uri  = URI->new('https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz');
my $clinvar_path = 'clinvar.vcf.gz';

my $http = HTTP::Tiny->new;

for my $download ( [ $model_uri   => $model_archive_path ],

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod  view on Meta::CPAN

=item *

the model and data takes 5 GB or more of disk space.

=back

meaning that you will need a total of B<8 GB> of disk space. You may need at least B<4 GB> of free memory to run the model.

=head1 COLOPHON

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 L<IPerl|https://p3rl.org/Devel::IPerl> (viewable online at L<nbviewer|https://nbviewer.org/github/EntropyOrg/perl-AI-...

You will also need the executables C<gunzip>, C<bgzip>, and C<samtools>. Furthermore,

=over

=item *

C<Bio::DB::HTS> requires C<libhts> and

=item *

C<PDL::Graphics::Gnuplot> requires C<gnuplot>.

=back

If you are running the code, you may optionally install the L<C<tensorflow> Python package|https://www.tensorflow.org/install/pip> in order to access the C<saved_model_cli> command, but this is only used for informational purposes.

=head1 TUTORIAL

=head2 Load the library

First, we need to load the C<AI::TensorFlow::Libtensorflow> library and more helpers. We then create an C<AI::TensorFlow::Libtensorflow::Status> object and helper function to make sure that the calls to the C<libtensorflow> C library are working prop...

  use strict;
  use warnings;
  use utf8;
  use constant IN_IPERL => !! $ENV{PERL_IPERL_RUNNING};
  no if IN_IPERL, warnings => 'redefine'; # fewer messages when re-running cells
  
  use feature qw(say);
  use Syntax::Construct qw( // );
  
  use lib::projectroot qw(lib);
  
  BEGIN {
      if( IN_IPERL ) {
          $ENV{TF_CPP_MIN_LOG_LEVEL} = 3;
      }
      require AI::TensorFlow::Libtensorflow;
  }
  
  use URI ();
  use HTTP::Tiny ();
  use Path::Tiny qw(path);
  
  use File::Which ();
  
  use List::Util ();
  
  use Data::Printer ( output => 'stderr', return_value => 'void', filters => ['PDL'] );
  use Data::Printer::Filter::PDL ();
  use Text::Table::Tiny qw(generate_table);
  
  my $s = AI::TensorFlow::Libtensorflow::Status->New;
  sub AssertOK {
      die "Status $_[0]: " . $_[0]->Message
          unless $_[0]->GetCode == AI::TensorFlow::Libtensorflow::Status::OK;
      return;
  }
  AssertOK($s);

And create helpers for converting between C<PDL> ndarrays and C<TFTensor> ndarrays.

  use PDL;
  use AI::TensorFlow::Libtensorflow::DataType qw(FLOAT);
  
  use FFI::Platypus::Memory qw(memcpy);
  use FFI::Platypus::Buffer qw(scalar_to_pointer);
  
  sub FloatPDLTOTFTensor {
      my ($p) = @_;
      return AI::TensorFlow::Libtensorflow::Tensor->New(
          FLOAT, [ reverse $p->dims ], $p->get_dataref, sub { undef $p }
      );
  }
  
  sub FloatTFTensorToPDL {
      my ($t) = @_;
  
      my $pdl = zeros(float,reverse( map $t->Dim($_), 0..$t->NumDims-1 ) );
  
      memcpy scalar_to_pointer( ${$pdl->get_dataref} ),
          scalar_to_pointer( ${$t->Data} ),
          $t->ByteSize;
      $pdl->upd_data;
  
      $pdl;
  }

=head2 Download model and data

=over

=item *

L<Enformer model|https://tfhub.dev/deepmind/enformer/1> from

  > 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. I<Nat Methods>. 2021 Oct;B<18(10)>:1196...

=item *

L<Human target dataset|https://github.com/calico/basenji/tree/master/manuscripts/cross2020> from

  > Kelley DR. Cross-species regulatory sequence activity prediction. I<PLoS Comput Biol>. 2020 Jul 20;B<16(7)>:e1008050. doi: L<10.1371/journal.pcbi.1008050|https://doi.org/10.1371/journal.pcbi.1008050>. PMID: L<32687525|https://pubmed.ncbi.nlm.nih....

=item *

L<UCSC hg38 genome|https://www.ncbi.nlm.nih.gov/assembly/GCA_000001405.15>. More info at L<http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/>; L<Genome Reference Consortium Human Build 38|https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/>...

  > 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 ...