Last updated: 2018-07-11

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File	Version	Author	Date	Message
Rmd	df197b0	tk382	2018-07-11	switch description
html	c619fd5	tk382	2018-07-11	Build site.
Rmd	ad6e873	tk382	2018-07-11	wflow_publish(“analysis/about.Rmd”)
html	8642710	tk382	2018-07-11	Build site.
Rmd	b46e425	tk382	2018-07-11	update introduction
html	cff03bb	tk382	2018-06-18	Build site.
Rmd	05f125d	tk382	2018-06-18	change theme
html	eec8493	tk382	2018-06-18	Build site.
Rmd	ec5b689	tk382	2018-06-18	git2r is updated
html	f4c3391	tk382	2018-06-18	Build site.
Rmd	f8bf9d3	tk382	2018-06-18	commit message
html	30b6489	tk382	2018-06-18	second change?
Rmd	1f2fb0e	tk382	2018-06-18	first change
html	9702c91	tk382	2018-06-18	Build site.
Rmd	05fc61e	tk382	2018-06-18	Start workflowr project.

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SLSL (Scaled Lasso Similarity Learning) is an unsupervised learning tool for clustering single cells using their expression level data. SLSL constructs many similarity matrices using different distance measures (Euclidean, Pearson, and Spearman), and different kernel parameters to account for the possible nonlinear structure. Then, SLSL employs scaled lasso to find optimal weights on each of those similarity matrices and infer one final sparse similarity matrix. This procedure does not have rank constraint unlike many popular unsupervised learning tools, because imposing such inflexible structure does not guarantee higher accuracy. Instead, we employ network diffusion to make the final similarity matrix closer to the block diagonal matrix. This is in effect equivalent to shrinking the less important eigenvalues. Lastly, SLSL sequentially performs dimension reduction using tSNE and kmeans for the final clustering result.

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