Home Community Researchers from Genentech and Stanford University Develop an Iterative Perturb-seq Procedure Leveraging Machine Learning for Efficient Design of Perturbation Experiments

Researchers from Genentech and Stanford University Develop an Iterative Perturb-seq Procedure Leveraging Machine Learning for Efficient Design of Perturbation Experiments

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Researchers from Genentech and Stanford University Develop an Iterative Perturb-seq Procedure Leveraging Machine Learning for Efficient Design of Perturbation Experiments

Basic details about gene and cell function is revealed by the expression response of a cell to a genetic disturbance. Using a readout of the expression response to a perturbation using single-cell RNA seq (scRNA-seq), perturb-seq is a brand new method for pooled genetic screens. Perturb-seq allows for the engineering of cells to a certain state, sheds light on the gene regulation system, and aids in identifying goal genes for therapeutic intervention. 

The efficiency, scalability, and breadth of Perturb-Seq have all been augmented by recent technological developments. The variety of tests needed to guage various perturbations multiplies exponentially as a consequence of the big variety of biological contexts, cell types, states, and stimuli. It is because non-additive genetic interactions are a possibility. Executing the entire experiments directly becomes impractical when there are billions of possible configurations.

Based on recent research, the outcomes of perturbations might be predicted using machine learning models. They use pre-existing Perturb-seq datasets to coach their algorithms, forecasting the expression results of unseen perturbations, individual genes, or mixtures of genes. Although these models show promise, they’re flawed as a consequence of a variety bias introduced by the unique experiment’s design, which affected the biological circumstances and perturbations chosen for training. 

Genentech and Stanford University researchers introduce a brand new way of interested by running a series of perturb-seq experiments to research a perturbation space. On this paradigm, the Perturb-seq assay is carried out in a wet-lab environment, and the machine learning model is implemented using an interleaving sequential optimal design approach. Data acquisition and re-training of the machine learning model occurs at each process stage. To make sure that the model can accurately forecast unprofiled perturbations, the researchers next use an optimal design technique to decide on a set of perturbation experiments. To intelligently sample the perturbation space, one must consider probably the most informative and representative perturbations to the model while allowing for diversity. This approach allows the creation of a model that has adequately explored the perturbation space with minimal perturbation experiments done.

Energetic learning relies on this principle, which has been extensively researched in machine learning. Document classification, medical imaging, and speech recognition are examples of the various areas which have put energetic learning into practice. The findings exhibit that energetic learning methods that work require a big initial set of labeled examples—profiled perturbations on this case—together with several batches that add as much as tens of 1000’s of labeled data points. The team also performed an economic evaluation that shows such conditions usually are not feasible as a consequence of the money and time constraints of iterative Perturb-seq within the lab.

To handle the problem of energetic learning in a budget context for Perturb-seq data, the team provides a novel approach termed ITERPERT (ITERative PERTurb-seq). Inspired by data-driven research, this work’s fundamental takeaway is that it is perhaps useful to complement data evidence with publically available prior knowledge sources, particularly within the early stages and when funds are tight. Data on physical molecular interactions, reminiscent of protein complexes, Perturb-seq information from comparable systems, and large-scale genetic screens using other modalities, reminiscent of genome-scale optical pooling screens, are examples of such prior knowledge. The prior knowledge encompasses several types of representation, including networks, text, images, and three-dimensional structures, which could possibly be difficult to utilize when engaging in energetic learning. To get around this, the team defines replicating kernel Hilbert spaces on all modalities and uses a kernel fusion approach to merge data from different sources.

They performed an intensive empirical investigation using a large-scale single-gene CRISPRi Perturb-seq dataset obtained in a cancer cell line (K562 cells). They benchmarked eight recent energetic learning methodologies to match ITERPERT to other often used approaches. ITERPERT obtained accuracy levels comparable to the highest energetic learning technique while using training data containing 3 times fewer perturbations. When considering batch effects throughout iterations, ITERPERT demonstrated strong performance in critical gene and genome-scale screens.


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Dhanshree

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Dhanshree Shenwai is a Computer Science Engineer and has a great experience in FinTech corporations covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is obsessed with exploring recent technologies and advancements in today’s evolving world making everyone’s life easy.


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