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CurIBENS

Benchmarking of different steps in the scRNAseq analysis of the Guo dataset (NSCLC, 2018)

#################### Dimension reduction ####################

On cherche le nombre de features optimal

#################### Determining the optimal nb of clusters ####################

  • ICA + MSTD (both)
  • ICA + MSTD w/ bulked clusters (Nathalie)
  • ICA + MSTD w/ bulked NN (Elise)
  • ICA + MSTD w/ bulked cells (Nathalie)
  • Evaluation on clustree (Elise)
  • Silhouette (Nathalie)
  • Packing nb (paper from Kegl) (both)

#################### Clustering ####################

  • DBScan + Minkowski (both)
  • Louvain based Seurat (both)
  • SIMLR (both)
  • NMF (Elise)

#################### Goodness of fit ####################

On essaie d'évaluer la justesse du clustering sur le dataset de Guo coupé en train et test sets

  • density for each cluster using dbscan (Nathalie)
  • density for each cluster w/ own code (both)
  • MSE on the clustering identity (Elise)

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