Release History

1.1

Initial release including two methods for unsupervised learning (VAE & GAN). Also included ability to handle paired alpha/beta data.

1.2

Second release included major refactoring in code to streamline and share methods across classes. Included ability for algorithm to accept v/d/j gene usage. Added more analytical fetures and visualization methods. Removed GAN from unsupervised learning techniques.

1.2.7

On-graph clustering method introduced for repertoire classifier to improve classification performance.

1.2.13

Ability for HLA information to be incorporated in the analysis of TCR-Seq.

1.2.24

Added ability to do regression for sequence-based model.

1.3

Third release including improved repertoire classification architecture. Details in method will follow in manuscript.

1.4

Fourth release includes major refactoring of code and adding more features including: - Multi-Model Inference. When training the supervised sequence or repertoire classifier, in Monte-Carlo or K-Fold Cross Validation, a separate model will be stored for each cross-validation. When using the inference engine, users can choose to do an ensemble inference of some or many of the trained models. - HLA Supertype Integration. Previous versions allowed users to provide HLA alleles for additional dimension of featurization for the TCR. In this version, when providing HLA (either via the Get_Data or Load_Data methods), one now has the option of assigning the HLA-A and B genes to known supertypes for a more biologically functional representation of HLA. - VAE now has an optional method by which to find a minimal number of latent features to model the underlying distribution by incorporating a sparsity regularization on the latent layer. When using this feature, the VAE will provide a more compact latent space even if the initial latent_dim is unnecessarily high to model the distribution of data. - Supervised models now have an additional option to use Multi-Sample Dropout to improve training and generalization. - Incorporation of LogoMaker so now when Representative Sequences are generated along with enriched motifs, seq logos are made and saved directly in the results folder under Motifs. - Improved Motif Identification algorithm behind supervised method Representative_Sequences that uses a multinomial linear model to identify which motifs are associated to predicted probabilites from neural network. - Supervised Repertoire Model now able to do regression. By providing per-instance label with regression value with Load_Data method, this will automatically use the average of all instance level labels as the sample level value to regress the model against.

2.0

Fifth release: - Upgrading to use python 3.7 & Tensorflow 2.0 - For large repertoires, we have incorporated the ability to randomly subsample the repertoire over the course of training. Two methods of sub-sampling exist. 1) Completely randomly sampled from across the entire repertoire vs 2) randomly sampled as a probability function of the frequency of the TCR (at the amino acid level), meaning that a TCR with a 25% frequency will be sample at that probability.

2.1.0

• Upgrading to Tensorflow 2.7
• Improved handling of inference with previously unseen V/D/J gene usage.
• Improved computational efficiency for loading data from large files (~2x improvement in speed, 50% or more decrease in peak memory consumption).