0.6.0

• config_utils.py: Add additional information saved upon running Selene. Specifically, we now save the version of Selene that the latest run used, make a copy of the input configuration file, and save this along with the model architecture file in the output directory. This adds a new dependency to Selene, the package ruamel.yaml
• H5Dataloader and _H5Dataset: Previously H5Dataloader had a number of arguments that were used to then initialize _H5Dataset internally. One major change in this version is that we now ask that users initialize _H5Dataset explicitly and then pass it to H5Dataloader as a class argument. This makes the two classes consistent with the PyTorch specifications for Dataset and DataLoader classes, enabling them to be compatible with different data parallelization configurations supported by PyTorch and the PyTorch Lightning framework.
• _H5Dataset class initialization optional arguments:
  • unpackbits can now be specified separately for sequences and targets by way of unpackbits_seq and unpackbits_tgt
  • use_seq_len enables subsetting to the center use_seq_len length of the sequences in the dataset.
  • shift (particularly paired with use_seq_len) allows for retrieving sequences shifted from the center position by shift bases. Note currently shift only allows shifting in one direction, depending on whether you pass in a positive or negative integer.
• GenomicFeaturesH5: This is a new targets class to handle continuous-valued targets, stored in an HDF5 file, that can be retrieved based on genomic coordinate. Once again, genomic regions are stored in a tabix-indexed .bed file, with the main change being that the BED file now specifies for each genomic regions the index of the row in the HDF5 matrix that contains all the target values to predict. If multiple target rows are returned for a query region, the average of those rows is returned.
• RandomPositionsSampler:
  • exclude_chrs: Added a new optional argument which by default excludes all nonstandard chromosomes exclude_chrs=['_'] by ignoring all chromosomes with an underscore in the name. Pass in a list of chromosomes or substrings to exclude. When loading possible sampling positions, the class now iterates through the exclude_chrs list and checks for each substring s in list if s in chrom, and if so, skips that chromosome entirely.
  • Internal function _retrieve now takes in an optional argument strand (default None) to enable explicit retrieval of a sequence at chrom, position for a specific side. The default behavior of the RandomPositionsSampler class remains the same, with the strand side randomly selected for each genomic position sampled.
• PerformanceMetrics:
  • Now supports spearmanr and pearsonr from scipy.stats. Room for improvement to generalize this class in the future.
  • The update function now has an optional argument scores which can pass in a subset of the metrics as list(str) to compute.
• TrainModel:
  • self.step starts from self._start_step, which is non-zero if loaded from a Selene-saved checkpoint
  • removed call to self._test_metrics.visualize in evaluate since the visualize method does not generalize well.
• NonStrandSpecific: Can now handle a model outputting two outputs in forward, will handle by taking either the mean or max of each of the two individual outputs for their forward and reverse predictions. A custom NonStrandSpecific class is recommended for more specific cases.

0.5.3

Adjust dependency requirements (NumPy, Cython lower bound requirement)

0.5.2

Fixes a NumPy/Cython type error causing build issues with Python 3.9+

0.5.0

Version 0.5.0

New functionality

• sampler.MultiSampler: MultiSampler accepts any Selene sampler for each of the train, validation, and test partitions where previously MultiFileSampler only accepted FileSamplers. We will deprecate MultiFileSampler in our next major release.
• DataLoader: Parallel data loading based on PyTorch's DataLoader class, which can be used with Selene's MultiSampler and MultiFileSampler class. (see: sampler.SamplerDataLoader, sampler.H5DataLoader)
• To support parallelism via multiprocessing, the sampler that SamplerDataLoader used needs to be picklable. To enable this, opening file operations are delayed to when any method that needs the file is called. There is no change to the API and setting init_unpicklable=True in __init__ for Genome and all OnlineSampler classes will fully reproduce the functionality in selene_sdk<=0.4.8.
• sampler.RandomPositionsSampler: added support for center_bin_to_predict taking in a list/tuple of two integers to specify the region from which to query the targets---that is, center_bin_to_predict by default (center_bin_to_predict=<int>) queries targets based on the center bin size, but can be specified as start and end integers that are not at the center if desired.
• EvaluateModel: accepts a list of metrics (by default computing ROC AUC and average precision) with which to evaluate the test dataset.

Usage

• Command-line interface (CLI): You can now run the CLI directly with python -m selene_sdk (if you have cloned the repository, make sure you have locally installed selene_sdk via python setup.py install, or selene_sdk is in the same directory as your script / added to PYTHONPATH). Developers can make a copy of the selene_sdk/cli.py script and use it the same way that selene_cli.py was used in earlier versions of Selene (python -u cli.py <config-yml> [--lr])

Bug fixes

• EvaluateModel: use_features_ord allows you to evaluate a trained model on only a subset of chromatin features (targets) predicted by the model. If you are using a FileSampler for your test dataset, you now have the option to pass in a subsetted matrix; however, this matrix must be ordered the same way as features (the original targets prediction ordering) and not in the same ordering as use_features_ord. However, the final model predictions and targets
  (test_predictions.npz and test_targets.npz) will be outputted according to the use_features_ord list and ordering.
• MatFileSampler: Previously the MatFileSampler reset the pointer to the start of the matrix too early (going back to the first sample before we had finished sampling the whole matrix).
• CLI learning rate: Edge cases (e.g. not specifying the learning rate via CLI or config) previously were not handled correctly and did not throw an informative error.

0.4.8

Enhancements

• PyTorch now has flexible state dict loading, which allows users more flexibility in loading models that were trained with older/newer versions of PyTorch. Selene has been updated to use this parameter.
• Added HeartENN model architecture ahead of publication.

0.4.7

Bugfixes:

• Use self.use_cuda in get_predict for raw sequence input in the AnalyzeSequences class.

0.4.6

Updates

• Allow users to pass in individual sequences to get_predictions in AnalyzeSequences class and get the model prediction directly (as opposed to having it be written to an output file).

0.4.5

Updates

• Specify upper & lower bounds for Selene's torch dependency
• Add '.' as a valid delimiter for VCF multiallelic parsing
• Allow users to evaluate on subsets of features in EvaluateModel

Bugfixes:

• BASES_ARR type consistency (specify as a list only) and resetting for lua-trained model vs. Selene-trained model.

0.4.4

Updates

• Refactored variant effect prediction to simplify the code
• Removed contains_unk column from output of get_predictions_from_fasta in AnalyzeSequences class

Bugfixes

• Fixed variant effect prediction handling for odd-length sequences

0.4.3

Updates:

• Add a column contains_unk to BED/VCF predictions. This boolean column indicates whether a sequence contains any unknown bases.

Bugfixes:

• MultiModelWrapper can be used with CUDA.