Method and system for subsampling of cells from single-cell genomics dataset

A computer-implemented method of down-sampling a single-cell genomics dataset having an original manifold by extraction of a subsample of cells from a plurality of cells in a single-cell genomics dataset, the method comprising the steps of:

• obtaining a single-cell genomics dataset represented in at least two dimensions, wherein information about each cell is represented in a first dimension and information about genomic features is represented in a second dimension;
• generating a cell-cell neighborhood graph from the single-cell genomics dataset, the cell-cell neighborhood graph providing information about similarities of the genomic features of the cells, wherein the cells are represented as vertices in the cell-cell neighborhood graph, wherein the step of generating the cell-cell neighborhood graph from the single-cell genomics dataset comprises performing a k nearest neighbors algorithm, wherein the k nearest neighbors algorithm comprises identifying k nearest neighbors of each cell, wherein k is a positive integer, and creating a cell-cell neighborhood graph wherein the cells are represented as vertices and the vertices are connected via edges if at least one of the vertices is a k nearest neighbor of the other one;
• dividing the cells in the cell-cell neighborhood graph into computational seed cells and non-seed cells, wherein the seed cells are selected to capture a heterogeneity of the cells;
• assigning at least one first prize to the seed cells and at least one second prize to the non-seed cells in the cell-cell neighborhood graph;
• and traversing the cell-cell neighborhood graph using a prize collecting steiner tree algorithm, wherein the algorithm is configured to include as many seed cells as possible and as few non-seed cells as possible, to obtain a subsample of cells preserving the original manifold.