New technologies such as single-cell RNA sequencing and mass cytometry are measuring cellular populations in high dimensions, offering unparalleled insights into cellular behavior and enabling new scientific discoveries. However, when these measurements are applied to multiple samples or experimental conditions, the resulting systematic variations, or batch effects, confound biological variation and create a vexing problem in comparing cellular populations. Moreover, these batch effects, which arise as a result of changed environmental condition, instrument variation, machine calibration, or human handling differences, can be complex and highly non-linear transformations. Despite their ubiquity, there are few computational tools designed to correct generally for such effects while maintaining biological differences. The ones that exist hold strong assumptions (such as linear shifts between batches). Here, we propose an entirely novel approach to disentangling biological from batch variation where we take a specific subpopulation of cells as a control between the batches. This subpopulation can be an unchanged population (known via prior biology) or a repeatedly measured spike-in. We use an autoencoder to model the variation in the control, and then interfere with neuron activations on inference to correct for these differences on the entire sample. This technique , which we term neuron interference, is unique in its ability to generalize a batch effect learned on a subpopulation to the entire population .
While neural networks can be trained to map from one specific dataset to another, they usually do not learn a generalized transformation that can extrapolate accurately outside the space of training. For instance, a generative adversarial network (GAN) exclusively trained to transform images of cars from light to dark might not have the same effect on images of horses. This is because neural networks are good at generation within the manifold of the data that they are trained on. However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied. To address this, we introduce a technique called neuron editing that learns how neurons encode an edit for a particular transformation in a latent space. We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons. By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations. We showcase our technique on image domain/style transfer and two biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs .
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New technologies such as single-cell RNA sequencing and mass cytometry are measuring cellular populations in high dimensions, offering unparalleled insights into cellular behavior and enabling new scientific discoveries. However, when these measurements are applied to multiple samples or experimental conditions, the resulting systematic variations, or batch effects, confound biological variation and create a vexing problem in comparing cellular populations. Moreover, these batch effects, which arise as a result of changed environmental condition, instrument variation, machine calibration, or human handling differences, can be complex and highly non-linear transformations. Despite their ubiquity, there are few computational tools designed to correct generally for such effects while maintaining biological differences. The ones that exist hold strong assumptions (such as linear shifts between batches). Here, we propose an entirely novel approach to disentangling biological from batch variation where we take a specific subpopulation of cells as a control between the batches. This subpopulation can be an unchanged population (known via prior biology) or a repeatedly measured spike-in.
While neural networks can be trained to map from one specific dataset to another, they usually do not learn a generalized transformation that can extrapolate accurately outside the space of training. For instance, a generative adversarial network (GAN) exclusively trained to transform images of cars from light to dark might not have the same effect on images of horses. This is because neural networks are good at generation within the manifold of the data that they are trained on. However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied. To address this, we introduce a technique called neuron editing that learns how neurons encode an edit for a particular transformation in a latent space.
R
model the variation in the control, and then interfere with neuron activations on inference to correct for these differences on the entire sample. This technique , which we term neuron interference, is unique in its ability to generalize a batch effect learned on a subpopulation to the entire population
decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons. By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations. We showcase our technique on image domain/style transfer and two biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs