High-throughput experiments like microarray, RNA-Seq, imaging, generate higher dimensional data It is challenging to visualize and analyze higher dimensional data. Dimension reduction techniques project/embed the data in lower dimension with minimum loss of relevant information. Two common dimension reduction methods in Biology: PCA and t-SNE.
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High-throughput experiments like microarray, RNA-Seq, imaging, generate higher dimensional data It is challenging to visualize and analyze higher dimensional data. Dimension reduction techniques project/embed the data in lower dimension with minimum loss of relevant information. Two common dimension reduction methods in Biology: PCA and t-SNE.
sejyoti/PCA-of-various-TCGA-dataset
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High-throughput experiments like microarray, RNA-Seq, imaging, generate higher dimensional data It is challenging to visualize and analyze higher dimensional data. Dimension reduction techniques project/embed the data in lower dimension with minimum loss of relevant information. Two common dimension reduction methods in Biology: PCA and t-SNE.
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