A method comprising: using at least one computer hardware processor to perform: obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the expression data including first expression data associated with a first set of genes of the plurality of gene sets, the first set of genes associated with a first cell type of the plurality of cell types; determining a plurality of cell composition percentages for the plurality of cell types, including a first cell composition percentage for the first cell type, using the expression data associated with the plurality of gene sets and a plurality of 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 plurality of cell composition percentages for the plurality of cell types including the first cell composition percentage for the first cell type comprises: for each cell type of the plurality of cell types, processing expression data associated with the set of genes associated with the cell type using a respective non-linear regression model of the plurality of non-linear regression models to determine the cell composition percentage for the respective cell type, wherein processing expression data associated with the set of genes associated with the cell type using the respective non-linear regression model of the plurality of non-linear regression models comprises processing the first expression data with the first non-linear regression model to determine the first cell composition percentage; and outputting the first cell composition percentage; wherein different non-linear regression models are used to determine cell composition percentages for different cell types and each of the non-linear regression models has been trained to estimate cell composition percentages for a specific respective cell type of the plurality of cell types, obtaining expression data for a biological sample, the biological sample previously obtained from a subject, the expression data including expression data associated with a plurality of gene sets associated with a respective plurality of cell types, the expression data including first expression data associated with a first set of genes of the plurality of gene sets, the first set of genes associated with a first cell type of the plurality of cell types; determining a plurality of cell composition percentages for the plurality of cell types, including a first cell composition percentage for the first cell type, using the expression data associated with the plurality of gene sets and a plurality of 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 plurality of cell composition percentages for the plurality of cell types including the first cell composition percentage for the first cell type comprises: for each cell type of the plurality of cell types, processing expression data associated with the set of genes associated with the cell type using a respective non-linear regression model of the plurality of non-linear regression models to determine the cell composition percentage for the respective cell type, wherein processing expression data associated with the set of genes associated with the cell type using the respective non-linear regression model of the plurality of non-linear regression models comprises processing the first expression data with the first non-linear regression model to determine the first cell composition percentage; and outputting the first cell composition percentage; for each cell type of the plurality of cell types, processing expression data associated with the set of genes associated with the cell type using a respective non-linear regression model of the plurality of non-linear regression models to determine the cell composition percentage for the respective cell type, wherein processing expression data associated with the set of genes associated with the cell type using the respective non-linear regression model of the plurality of non-linear regression models comprises processing the first expression data with the first non-linear regression model to determine the first cell composition percentage; and outputting the first cell composition percentage; wherein different non-linear regression models are used to determine cell composition percentages for different cell types and each of the non-linear regression models has been trained to estimate cell composition percentages for a specific respective cell type of the plurality of cell types,