PCA, population structure, and K-means clustering BNFO 601.

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Presentation transcript:

PCA, population structure, and K-means clustering BNFO 601

Publicly available real data Datasets (Noah Rosenberg’s lab): –East Asian admixture: 10 individuals from Cambodia, 15 from Siberia, 49 from China, and 16 from Japan; 459,188 SNPs –African admixture: 32 Biaka Pygmy individuals, 15 Mbuti Pygmy, 24 Mandenka, 25 Yoruba, 7 San from Namibia, 8 Bantu of South Africa, and 12 Bantu of Kenya; 454,732 SNPs –Middle Eastern admixture: contains 43 Druze from Israil- Carmel, 47 Bedouins from Israel-Negev, 26 Palestinians from Israel-Central, and 30 Mozabite from Algeria-Mzab; 438,596 SNPs

East Asian admixture

African admixture

Middle Eastern admixture

Clustering Suppose we want to cluster n vectors in R d into two groups. Define C 1 and C 2 as the two groups. Our objective is to find C 1 and C 2 that minimize where m i is the mean of class C i

K-means algorithm for two clusters Input: Algorithm: 1.Initialize: assign x i to C 1 or C 2 with equal probability and compute means: 2.Recompute clusters: assign x i to C 1 if ||x i -m 1 ||<||x i -m 2 ||, otherwise assign to C 2 3.Recompute means m 1 and m 2 4.Compute objective 5.Compute objective of new clustering. If difference is smaller than then stop, otherwise go to step 2.

K-means Is it guaranteed to find the clustering which optimizes the objective? It is guaranteed to find a local optimal We can prove that the objective decreases with subsequence iterations

Proof sketch of convergence of k-means Justification of first inequality: by assigning x j to the closest mean the objective decreases or stays the same Justification of second inequality: for a given cluster its mean minimizes squared error loss