Methods for identifying clusters in a dataset, methods of analyzing cytometry data with the aid of a computer and methods of detecting cell sub-populations in a plurality of cells

A method of analyzing cytometry data with the aid of a computer, the method comprising:

• providing the computer with a dataset comprising the cytometry data;
• using the computer to identify clusters in the cytometry data, the clusters indicative of cell sub-populations, wherein identifying the clusters comprises:
• determining for each data point in the dataset, a plurality of parameters comprising a first parameter and a second parameter,
• the first parameter being a distance between the data point and a nearest other data point having a local density that is higher than a local density of the data point, and
• the second parameter being a function of the local density of the data point and the first parameter;
• running statistical tests on each of the first parameter and the second parameter across the dataset, to identify outliers of the first parameter and outliers of the second parameter;
• and
• designating each data point where both the first parameter and the second parameter are identified outliers, as a centre of a respective cluster.

A method of detecting cell sub-populations in a plurality of cells, the method comprising:

• performing cytometry on the plurality of cells to detect signals for each cell of the plurality of cells;
• recording in a dataset, the detected signals for the plurality of cells such that each data point in the dataset is associated with one cell of the plurality of cells;
• determining for each data point in the dataset, a plurality of parameters comprising a first parameter and a second parameter,
• the first parameter being a distance between the data point and a nearest other data point having a local density that is higher than a local density of the data point, and
• the second parameter being a function of the local density of the data point and the first parameter;
• running statistical tests on each of the first parameter and the second parameter across the dataset, to identify outliers of the first parameter and outliers of the second parameter;
• and
• designating each data point where both the first parameter and the second parameter are identified outliers, as a centre of a respective cluster, wherein each cluster is indicative of a cell sub-population.