Abstract: Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

Abstract: Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

Abstract: Disclosed herein are methods and systems for determining the numbers of targets. In some embodiments, the method comprise: stochastically barcoding targets using stochastic barcodes; obtaining sequencing data; for one or more of the targets: counting the number of molecular labels with distinct sequences associated with the target in the sequencing data; identifying clusters of molecular labels of the target using directional adjacency; collapsing the sequencing data using the clusters of molecular labels of the target identified; and estimating the number of the target.

Abstract: Disclosed herein are methods and systems for determining the numbers of targets. In some embodiments, the method comprise: stochastically barcoding targets using stochastic barcodes; obtaining sequencing data; for one or more of the targets: counting the number of molecular labels with distinct sequences associated with the target in the sequencing data; identifying clusters of molecular labels of the target using directional adjacency; collapsing the sequencing data using the clusters of molecular labels of the target identified; and estimating the number of the target.

Abstract: Disclosed herein are methods and systems for determining the numbers of targets. In some embodiments, the method comprise: stochastically barcoding targets using stochastic barcodes; obtaining sequencing data; for one or more of the targets: counting the number of molecular labels with distinct sequences associated with the target in the sequencing data; identifying clusters of molecular labels of the target using directional adjacency; collapsing the sequencing data using the clusters of molecular labels of the target identified; and estimating the number of the target.

Abstract: Disclosed herein are methods and systems for identifying targets for distinguishing cell types. In some embodiments, the method comprises: hierarchically clustering expression profiles of cells to generate a dendrogram with each leaf representing the expression profile of a different individual cell. The dendrogram can be pruned by eliminating invalid splits (and their children). The remaining leaves in the dendrogram can be merged, independent of their locations in the dendrogram, based on their distances to one another to generate clusters of expression profiles. The method can identify targets for distinguishing cell types based on targets that are expressed differentially in the clusters.

Abstract: Disclosed herein include systems, methods, compositions, and kits for determining numbers of occurrences of variants (e.g., transcript variants) of targets (e.g., gene targets) in cells and/or samples. In some embodiments, modification target sites (e.g., polyadenylation sites) and usage thereof are determined. Whole transcriptome amplification analysis can be performed and the sequencing reads obtained can be analyzed to identify polyadenylation sites (and usage thereof) for the design of customized primer panels for targeted scRNAseq experiments.

Abstract: Disclosed herein are methods and systems for determining occurrences of targets. In some embodiments, the method comprises: collapsing putative sequences of the target; collapsing molecular label sequences associated with the putative sequences of the target; and estimating the occurrence of the target, wherein the occurrence of the target estimated correlates with the occurrence of molecular label sequences associated with the putative sequences of the target in the sequencing data after collapsing the occurrence of the putative sequences of the target and the occurrence of noise molecular label sequences.

Abstract: Disclosed herein include methods and systems for identifying multiplet expression profiles. A plurality of synthetic multiplet expression profiles can be generated from a plurality of expression profiles. An expression profile can be identified as an expression for a singlet or a multiplet using a machine learning model trained using the plurality of synthetic multiplet (e.g., doublet) expression profiles.

Abstract: Disclosed herein are methods and systems for identifying targets for distinguishing cell types. In some embodiments, the method comprises: hierarchically clustering expression profiles of cells to generate a dendrogram with each leaf representing the expression profile of a different individual cell. The dendrogram can be pruned by eliminating invalid splits (and their children). The remaining leaves in the dendrogram can be merged, independent of their locations in the dendrogram, based on their distances to one another to generate clusters of expression profiles. The method can identify targets for distinguishing cell types based on targets that are expressed differentially in the clusters.

Abstract: Disclosed herein are methods and systems for classifying cell labels, for example identifying a signal cell label. In some embodiments, the method comprises: obtaining sequencing data of barcoded targets created using targets in cells barcoded using barcodes, wherein a barcode comprises a cell label and a molecular label. After ranking the cell labels, a minimum of a second derivative plot of a cumulative sum plot can be determined. Using the methods, a cell label can be classified as a signal cell label or a noise cell label based on the number of molecular labels with distinct sequences associated with the cell label and a cell label threshold.

Abstract: Disclosed herein are methods and systems for determining the numbers of targets. In some embodiments, the method comprise: stochastically barcoding targets using stochastic barcodes; obtaining sequencing data; for one or more of the targets: counting the number of molecular labels with distinct sequences associated with the target in the sequencing data; identifying clusters of molecular labels of the target using directional adjacency; collapsing the sequencing data using the clusters of molecular labels of the target identified; and estimating the number of the target.