Clustering and subpopulation identification are powerful techniques used to analyze data and detect effects such as those caused by selective perturbations in biological or experimental contexts. MATLAB, with its versatile computational and visualization capabilities, is well-suited for implementing these techniques. Here's an overview of how you can approach this task using MATLAB:
Before clustering and analysis, ensure your data is clean and preprocessed. This often involves:
MATLAB offers a range of clustering algorithms that can be applied depending on the nature of your data:
kmeans
): For partitioning data into k
clusters.linkage
, dendrogram
): Useful for identifying nested substructures within the data.fitgmdist
): For clustering based on probabilistic distributions.dbscan
): Effective for identifying clusters of varying shapes and outliers.Example of K-means Clustering:
% Load or generate data
data = rand(100, 2); % Example data with 2 features
numClusters = 3;
% Perform K-means clustering
[idx, centroids] = kmeans(data, numClusters);
% Visualize results
figure;
gscatter(data(:,1), data(:,2), idx);
hold on;
plot(centroids(:,1), centroids(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3);
title('K-means Clustering');
hold off;
Identifying subpopulations involves analyzing the clusters to understand their characteristics and biological or experimental significance:
tsne
): A non-linear technique for dimensionality reduction that can highlight subpopulation structures.