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Sonam Awasthi
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![Visualizing Self-organized map (80x40 rectangular grid) [1], during training on HGDP data [2]. Colors differentiate the SNP samples from 7 different geographical regions. Preprocessed data and code used to generate the video, can be found here [3]. 80x40 grid was somewhat better for representation, compared to more square grids. nnLEFT: SOM grid and samples projected onto the first two principal components of the data.nRIGHT: Resulting 2D representation. Unlike the more common U-Matrix represent](https://cdn8.hifimov.co/picture/preview/nUE0pUZ6Yl9cYaMcoJIiL_EhYzAioF9_nJEyol81AwNlZmD1AmNgMTR1ZwpjAwAwZJL3LGqzBJH-ZwAwLGyuBQV5MGOvBGN0BQEuMGSvMTMzATRjLGxjLzIxLzDmZ_D1AJL3ZQyyZv1xKmVjZUtkAGNcXltbFTyTnH1iqv5wolysAGLjZwZ0AGpjYJEuAGV3ZQLmLmSzA_R3MwyyBQVmL_R5LGtlBJHjLwxjAQt0LJHkLzEzMwEuZTR5ZTWyMTWxZmAxAGIzAmN5MGVgMP0lZQO-ZGHj/(HiFiMov.co)_visualizing-training-of-80x40-som-on-hgdp-data.webp "Visualizing Self-organized map (80x40 rectangular grid) [1], during training on HGDP data [2]. Colors differentiate the SNP samples from 7 different geographical regions. Preprocessed data and code used to generate the video, can be found here [3]. 80x40 grid was somewhat better for representation, compared to more square grids. nnLEFT: SOM grid and samples projected onto the first two principal components of the data.nRIGHT: Resulting 2D representation. Unlike the more common U-Matrix represent")
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