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<p><strong>Last updated:</strong> 2018-07-17</p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>R Markdown file:</strong> up-to-date </summary></p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/tk382/SCNoisyClustering/tree/0e83fce3d3d4188c4adec16be40b67240577cb4c" target="_blank">0e83fce</a> </summary></p>
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated. <br><br> Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use <code>wflow_publish</code> or <code>wflow_git_commit</code>). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
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</code></pre>
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<p>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.</p>
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