SCMER - Manifold Preserving Feature Selection

SCMER is a feature selection methods designed for single-cell data analysis. It selects a compact sets of markers that preserve the manifold in the original data. It can also be used for data integration by using features in one modality to match the manifold of another modality.

Contents

• Installation
• Basic Usage
  • Advanced
    • Marker transfering
    • Batch stratification
    • Predetemined markers
    • Tuning
  • Full API
    • All model parameters
• Tutorials
  • scRNA
    • Melanoma TME (Tirosh et al.)
    • 198 Pan-Cancer Cell Lines Marker Selection
    • Ileum Lamina Propria Immunocyte Subtypes (Martin et al.)
    • Bone Marrow Immune Cell Differentiation
    • Dexamethasone Treated A549 Lung Cancer Cell Line (Cao et al.)
  • CyTOF
    • Bone Marrow (Levine et al.)
  • CITE-Seq (Genes retaining protein manifold)
    • Multiomics Analysis: CBMC Cite-Seq (Feature Selection on Donor 1)
    • Multiomics Analysis: CBMC Cite-Seq (Validation on Donor 2)
    • PBMC CITE-Seq Unbiased Feature Selection
  • Supplementary
    • Dexamethasone Treated A549 ATAC Peak Embedding
    • PBMC CITE-Seq RNA Processing
  • Simulation
    • Simulation
• FAQs
  • Installation
    • Q: I got a OSError [WinError 182] Error loading “…caffe2_detectron_ops.dll”. What can I do?
    • Q: I got an error for importing tables. What can I do?
• SCMER package contents
  • Comparison
    • Comparison.compare()
    • Comparison.make_recall_curve()
    • Comparison.read_gmt()
  • TsneL1
    • TsneL1.fit()
    • TsneL1.fit_transform()
    • TsneL1.get_mask()
    • TsneL1.transform()
    • TsneL1.tune()
  • UmapL1
    • UmapL1.fit()
    • UmapL1.fit_transform()
    • UmapL1.get_mask()
    • UmapL1.transform()
    • UmapL1.tune()
• Cite Our Work
• License
  • MIT License
• Contact

Indices and tables

• Index

• Module Index

• Search Page