Abstract: Methods and systems for evaluating a query perturbation, in a cell based assay representing a test state, are provided. Control data points having dimensions representing measurements of different features across control cell aliquots are obtained. Test data points having dimensions representing measurements of different features across test cell aliquots are obtained. A composite test vector is computed between measures of central tendency across the control data points and measures of central tendency across the test data points. Query perturbation data points having dimensions representing measurements of different features across perturbation cell aliquots are obtained. A composite query perturbation vector is computed between measures of central tendency across the control data points and measures of central tendency across the plurality of query perturbation data points.

Abstract: Methods and systems for evaluating a query perturbation, in a cell based assay representing a test state, are provided. Control data points having dimensions representing measurements of different features across control cell aliquots are obtained. Test data points having dimensions representing measurements of different features across test cell aliquots are obtained. A composite test vector is computed between measures of central tendency across the control data points and measures of central tendency across the test data points. Query perturbation data points having dimensions representing measurements of different features across perturbation cell aliquots are obtained. A composite query perturbation vector is computed between measures of central tendency across the control data points and measures of central tendency across the plurality of query perturbation data points.

Abstract: A system for determining a goodness of a deep learning model comprises a memory coupled with a processor. The processor accesses a first set of vectors representative of images of a biological assay. The vectors of the first set of vectors are outputs of a first deep learning model. The processor creates a first distribution of a first plurality of pairwise comparisons of vectors, of the first set of vectors, which were generated from image pairs with similar cell perturbations. The processor creates a second distribution of a second plurality of pairwise comparisons of vectors, of the first set of vectors, which were generated from image pairs with dissimilar cell perturbations. The processor determines a difference between the first distribution and the second distribution and uses the difference to make a determination of goodness of the deep learning model as applied to the biological assay.

Abstract: A system for selecting CRISPR guides for knocking out one or more target genes in a target cell from a multiplicity of candidate guides comprises a memory and a processor. The processor determines whether the candidate guide meets a plurality of thresholds. The thresholds are associated with: a transcript support level; targeting a consensus sequence of a target gene; which exon of the target gene is targeted; targeting of a primary transcript, targeting of a common isoform; a precomputed prediction of editing outcomes; mapping to an expressed sequence; fraction of gene expression attributable to targeted transcripts; a common SNP overlap threshold; which exon of the target gene is targeted; overlap of a selected guide; predicted frameshift percentage; maximum and minimum GC content; off target score; where a coding sequence is targeted. In response to meeting the thresholds, the processor selects the candidate guide as a selected CRISPR guide.

Abstract: Methods and systems for evaluating a query perturbation, in a cell based assay representing a test state, are provided. Control data points having dimensions representing measurements of different features across control cell aliquots are obtained. Test data points having dimensions representing measurements of different features across test cell aliquots are obtained. A composite test vector is computed between measures of central tendency across the control data points and measures of central tendency across the test data points. Query perturbation data points having dimensions representing measurements of different features across perturbation cell aliquots are obtained. A composite query perturbation vector is computed between measures of central tendency across the control data points and measures of central tendency across the plurality of query perturbation data points.