Method for identifying a subpopulation of specific cells among a large population of cells, said method comprising the following steps: a. exposing the cells of said large population to reagents, said n reagents allowing the detection of the presence, of the absence or of the amount of n different components of each cell of said large population, n being greater than or equal to 2, b. detecting said reagents for each cells belonging to said large population, in order to assign each cell to a specific position within an n-dimensional space, and the following steps carried out by a computer program run on a computer c. grouping the cells by clusterisation into k different clusters, each of the clusters being characterized by a center and a radius, the clusterisation being such that from 80% to 90% of the cells belonging to said large population are assigned to one of said k clusters, wherein the clustering step is achieved by carrying out a modified k-means algorithm which is such that in the re-assignment phase of k-means only points that lie within a maximum radius DMAX around cluster centres are used to re-compute said cluster centres, so that only points that lie in dense regions are clustered by said modified k-means algorithm, and wherein the initial number of clusters KI and the maximum radius DMAX are parameters of the modified k-means algorithm selected so that the percentage of the cells belonging to said large population which are assigned to one of said k clusters lies in said range from 80% to 90%, d. grouping adjacent clusters to obtain larger clusters, two clusters being adjacent if the Euclidian distance between the centers of said two clusters is less than twice the maximum radius DMAX, and estimating the centers C lk of said larger clusters as well as the covariance matrix of the cells belonging to said larger cluster, e. defining sliding regions for each larger cluster by increasing the radius of said larger cluster by a factor ε, ε varying from 0.01 to 0.1, and calculating the Mahalanobis distance for each cell that belongs to said sliding region, f. estimating the number of cells having a Mahalanobis distance lower than D lk (1+ε), but not belonging to the larger cluster, to measure and the density of said set, such that, if the density is greater than a value N, N being greater than 10, preferably N varies from 10 to 1000, in particular N varies from 10 to 500, then said set is considered to not contain said subpopulation of specific cells but to belong to the larger cluster, and steps e and f are repeated p times until the density of said set is lower than said value N, said set being defined by cells having a Mahalanobis distance lower than D lk (1+ε) p, g. identifying said subpopulation of specific cells as the set of cells which neither belong to a larger cluster nor to any considered sliding region. a. exposing the cells of said large population to reagents, said n reagents allowing the detection of the presence, of the absence or of the amount of n different components of each cell of said large population, n being greater than or equal to 2, b. detecting said reagents for each cells belonging to said large population, in order to assign each cell to a specific position within an n-dimensional space, and the following steps carried out by a computer program run on a computer c. grouping the cells by clusterisation into k different clusters, each of the clusters being characterized by a center and a radius, the clusterisation being such that from 80% to 90% of the cells belonging to said large population are assigned to one of said k clusters, wherein the clustering step is achieved by carrying out a modified k-means algorithm which is such that in the re-assignment phase of k-means only points that lie within a maximum radius DMAX around cluster centres are used to re-compute said cluster centres, so that only points that lie in dense regions are clustered by said modified k-means algorithm, and wherein the initial number of clusters KI and the maximum radius DMAX are parameters of the modified k-means algorithm selected so that the percentage of the cells belonging to said large population which are assigned to one of said k clusters lies in said range from 80% to 90%, d. grouping adjacent clusters to obtain larger clusters, two clusters being adjacent if the Euclidian distance between the centers of said two clusters is less than twice the maximum radius DMAX, and estimating the centers C lk of said larger clusters as well as the covariance matrix of the cells belonging to said larger cluster, e. defining sliding regions for each larger cluster by increasing the radius of said larger cluster by a factor ε, ε varying from 0.01 to 0.1, and calculating the Mahalanobis distance for each cell that belongs to said sliding region, f. estimating the number of cells having a Mahalanobis distance lower than D lk (1+ε), but not belonging to the larger cluster, to measure and the density of said set, such that, if the density is greater than a value N, N being greater than 10, preferably N varies from 10 to 1000, in particular N varies from 10 to 500, then said set is considered to not contain said subpopulation of specific cells but to belong to the larger cluster, and steps e and f are repeated p times until the density of said set is lower than said value N, said set being defined by cells having a Mahalanobis distance lower than D lk (1+ε) p, g. identifying said subpopulation of specific cells as the set of cells which neither belong to a larger cluster nor to any considered sliding region.