A method for predicting the cell spatial relation based on single-cell transcriptome sequencing data includes the steps of obtaining a probability matrix P of a cell-cell interaction strength matrix A based on single-cell transcriptome sequencing data; reconstructing, according to the obtained probability matrix P of the cell-cell interaction strength matrix A, a three-dimensional spatial structure in which cells interact with each other; and for each cell in the reconstructed three-dimensional spatial structure in which cells interact with each other, determining the intercellular distance threshold for each cell to interact with h cells on average to obtain an intercellular interaction network. The method requires only the single-cell transcriptome sequencing data to predict the interaction of the cells in three-dimensional space, which breaks the limitation of the existing technology that needs to obtain the spatial relationship of cells through imaging.

A method for predicting the cell spatial relation based on single-cell transcriptome sequencing data includes the steps of obtaining a probability matrix P of a cell-cell interaction strength matrix A based on single-cell transcriptome sequencing data; reconstructing, according to the obtained probability matrix P of the cell-cell interaction strength matrix A, a three-dimensional spatial structure in which cells interact with each other; and for each cell in the reconstructed three-dimensional spatial structure in which cells interact with each other, determining the intercellular distance threshold for each cell to interact with h cells on average to obtain an intercellular interaction network. The method requires only the single-cell transcriptome sequencing data to predict the interaction of the cells in three-dimensional space, which breaks the limitation of the existing technology that needs to obtain the spatial relationship of cells through imaging.

The present application generally relates to identifying gene clusters from long-read genomic sequencing data. The disclosure provides methods, non-transitory computer readable media, and apparatuses for processing long-read genomic sequencing data, performing error corrections, and identifying gene cluster, e.g. biosynthetic gene clusters. The methods, non-transitory computer readable media, and apparatuses described herein can be employed in broad areas of biological applications, such as drug discovery, industrial chemical discovery and production, and basic biological research.

The present application generally relates to identifying gene clusters from long-read genomic sequencing data. The disclosure provides methods, non-transitory computer readable media, and apparatuses for processing long-read genomic sequencing data, performing error corrections, and identifying gene cluster, e.g. biosynthetic gene clusters. The methods, non-transitory computer readable media, and apparatuses described herein can be employed in broad areas of biological applications, such as drug discovery, industrial chemical discovery and production, and basic biological research.

A memory device includes an array of memory cells configured on a die or chip and coupled to sense lines and access lines of the die or chip and a respective sense amplifier configured on the die or chip coupled to each of the sense lines. Each of a plurality of subsets of the sense lines is coupled to a respective local input/output (I/O) line on the die or chip for communication of data on the die or chip and a respective transceiver associated with the respective local I/O line, the respective transceiver configured to enable communication of the data to one or more device off the die or chip.

The technology disclosed describes determination of which elements of a sequence are nearest to uniformly spaced cells in a grid, where the elements have element coordinates, and the cells have dimension-wise cell indices and cell coordinates. The determination includes generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells. The subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, and the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates. The determination further includes generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence.

The technology disclosed describes determination of which elements of a sequence are nearest to uniformly spaced cells in a grid, where the elements have element coordinates, and the cells have dimension-wise cell indices and cell coordinates. The determination includes generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells. The subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, and the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates. The determination further includes generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence.

Provided herein are automated apparatus for the identification of microorganisms in various samples. The disclosure solves existing challenges encountered in identifying and distinguishing various types of microorganisms, including viruses and bacteria in a timely, efficient, and automated manner by sequencing.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.