BCMPPy
Python package
Use an AnnData object, or supply an existing unintegrated cell-by-dimension embedding matrix.
Method
Batch-Constraint Manifold Partition (BCMP) derives proxy population labels directly from unintegrated single-cell data geometry. These labels can be used to compare batch-effect removal across candidate integration methods before cell annotations are available.
How BCMP works
1Represent the unintegrated manifold
Use the PCA representation constructed from raw counts.
Use the PCA representation constructed from raw counts.
2Partition at candidate representation scales
For each candidate representation scale
For each candidate representation scale
k, build a cell-cell graph and use community detection to define candidate domains.3Evaluate multi-batch support
At each candidate scale, determine whether every evaluated domain has sufficient support from multiple batches.
At each candidate scale, determine whether every evaluated domain has sufficient support from multiple batches.
4Select the smallest passing
Return the candidate domains from the smallest scale that passes the multi-batch support criterion.
kReturn the candidate domains from the smallest scale that passes the multi-batch support criterion.
Choose an implementation
BCMP is available in Python and R. Choose the package that fits the language and data structure already used in your analysis.
BCMPR
R package
Use a Seurat object, a SingleCellExperiment object, or supply an existing unintegrated cell-by-dimension embedding matrix.