Single-cell RNAseq Analysis Walk-Through: NMF, Graph Clustering, XGBoost for Time-Series Samples

Join me in this concise walk-through video where I explore bioinformatic analysis techniques according to a paper published in Science in year 2020.
Using non-negative matrix factorization (NMF), graph-based clustering, and XGBoost machine learning, we analyze time-series samples and identify cell types for each cluster. Discover how NMF uncovers patterns, graph clustering reveals relationships, and XGBoost predicts and classifies.
Reference: Karthaus WR, Hofree M, Choi D, Linton EL, Turkekul M, Bejnood A, Carver B, Gopalan A, Abida W, Laudone V, Biton M. Regenerative potential of prostate luminal cells revealed by single-cell analysis. Science. 2020 May 1;368(6490):497-505. DOI: 10.1126/science.aay026
Github: github.com/Lin...
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#Bioinformatics #DataAnalysis #TimeSeries #NMF #GraphClustering #XGBoost
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[@RicardoRodriguez-yu8ss]

I have learned a lot with your videos!!! ❤❤ You are amazing

[@sunghyoukpark7423]

This is just awesome!!!

[@zestiny5810]

Really cool video. Thank you!! ❣

[@qiangchen9630]

Thank you so much!

[@ahmedal-mammari9639]

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[@philtoa334]

Very Nice.

[@CannabinatedFantasy]

awesome