Abstract: Aspects of the disclosure relate to methods for improving compatibility of nucleic acid sequencing data obtained using different techniques. The disclosure is based, in part, on methods for mapping expression levels for genes expressed in a biological sample and obtained from a subject using a first protocol to expression levels as would have been determined through a second protocol if the second protocol were used to process the biological sample instead of the first protocol.

Abstract: Techniques for determining one or more cell composition percentages from expression data. The techniques include obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including first expression data associated with a first set of genes associated with a first cell type; determining a first cell composition percentage for the first cell type using the expression data and one or more non-linear regression models including a first non-linear regression model, wherein the first cell composition percentage indicates an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: processing the first expression data with the first non-linear regression model to determine the first cell composition percentage for the first cell type; and outputting the first cell composition percentage.

Abstract: Techniques for determining one or more cell composition percentages from expression data. The techniques include obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including first expression data associated with a first set of genes associated with a first cell type; determining a first cell composition percentage for the first cell type using the expression data and one or more non-linear regression models including a first non-linear regression model, wherein the first cell composition percentage indicates an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: processing the first expression data with the first non-linear regression model to determine the first cell composition percentage for the first cell type; and outputting the first cell composition percentage.

Abstract: Aspects of the disclosure relate to methods for improving compatibility of nucleic acid sequencing data obtained using different techniques. The disclosure is based, in part, on methods for mapping expression levels for genes expressed in a biological sample and obtained from a subject using a first protocol to expression levels as would have been determined through a second protocol if the second protocol were used to process the biological sample instead of the first protocol.

Abstract: Techniques for using machine learning to estimate tumor expression levels of genes in tumor cells. The techniques include obtaining expression data for a set of genes comprising a first plurality of genes associated with the tumor cells and a second plurality of genes associated with tumor microenvironment cells; determining the tumor expression levels of the first plurality of genes in the tumor cells using a plurality of machine learning models, the determining comprising: generating a first set of features for the first gene; providing the first set of features as input to the first machine learning model to obtain an output comprising a tumor microenvironment expression level estimate of the first gene in the tumor microenvironment cells; and determining a first tumor expression level for the first gene in the tumor cells using the output of the first machine learning model and a total expression level for the first gene.

Abstract: Techniques for determining one or more cell composition percentages from expression data. The techniques include obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including first expression data associated with a first set of genes associated with a first cell type; determining a first cell composition percentage for the first cell type using the expression data and one or more non-linear regression models including a first non-linear regression model, wherein the first cell composition percentage indicates an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: processing the first expression data with the first non-linear regression model to determine the first cell composition percentage for the first cell type; and outputting the first cell composition percentage.

Abstract: Techniques for determining one or more cell composition percentages from expression data. The techniques include obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including first expression data associated with a first set of genes associated with a first cell type; determining a first cell composition percentage for the first cell type using the expression data and one or more non-linear regression models including a first non-linear regression model, wherein the first cell composition percentage indicates an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: processing the first expression data with the first non-linear regression model to determine the first cell composition percentage for the first cell type; and outputting the first cell composition percentage.

Abstract: Described herein are various methods of collecting and processing of tumor and/or healthy tissue samples to extract nucleic acid and perform nucleic acid sequencing. Also described herein are various methods of processing nucleic acid sequencing data to remove bias from the nucleic acid sequencing data. Also described herein are various methods of evaluating the quality of nucleic acid sequence information. The identity and/or integrity of nucleic acid sequence data is evaluated prior to using the sequence information for subsequent analysis (for example for diagnostic, prognostic, or clinical purposes). The methods enable a subject, doctor, or user to characterize or classify various types of cancer precisely, and thereby determine a therapy or combination of therapies that may be effective to treat a cancer in a subject based on the precise characterization.

Abstract: Techniques for determining one or more cell composition percentages from expression data. The techniques include obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including first expression data associated with a first set of genes associated with a first cell type; determining a first cell composition percentage for the first cell type using the expression data and one or more non-linear regression models including a first non-linear regression model, wherein the first cell composition percentage indicates an estimated percentage of cells of the first cell type in the biological sample, wherein determining the first cell composition percentage for the first cell type comprises: processing the first expression data with the first non-linear regression model to determine the first cell composition percentage for the first cell type; and outputting the first cell composition percentage.

Abstract: Described herein are various methods of collecting and processing of tumor and/or healthy tissue samples to extract nucleic acid and perform nucleic acid sequencing. Also described herein are various methods of processing nucleic acid sequencing data to remove bias from the nucleic acid sequencing data. Also described herein are various methods of evaluating the quality of nucleic acid sequence information. The identity and/or integrity of nucleic acid sequence data is evaluated prior to using the sequence information for subsequent analysis (for example for diagnostic, prognostic, or clinical purposes). The methods enable a subject, doctor, or user to characterize or classify various types of cancer precisely, and thereby determine a therapy or combination of therapies that may be effective to treat a cancer in a subject based on the precise characterization.

Abstract: Described herein are various methods of collecting and processing of tumor and/or healthy tissue samples to extract nucleic acid and perform nucleic acid sequencing. Also described herein are various methods of processing nucleic acid sequencing data to remove bias from the nucleic acid sequencing data. Also described herein are various methods of evaluating the quality of nucleic acid sequence information. The identity and/or integrity of nucleic acid sequence data is evaluated prior to using the sequence information for subsequent analysis (for example for diagnostic, prognostic, or clinical purposes). The methods enable a subject, doctor, or user to characterize or classify various types of cancer precisely, and thereby determine a therapy or combination of therapies that may be effective to treat a cancer in a subject based on the precise characterization.