R: Efficient and Precise Single-Cell Reference Atlas Mapping

# Efficient and Precise Single-Cell Reference Atlas Mapping

## Documentation for package ‘symphony’ version 0.1.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

buildReference | Function for building a Symphony reference starting from expression matrix
buildReferenceFromHarmonyObj | Function for building a Symphony reference from a Harmony object. Useful if you would like your code to be more modular. Note that you must have saved vargenes_means_sds and PCA loadings.
calcknncorr | Calculates the k-NN correlation, which measures how well the sorted ordering of k nearest reference neighbors in a gold standard embedding correlate with the ordering for the same reference cells in an alternative embedding (i.e. from reference mapping). NOTE: it is very important for the order of reference cells (cols) in gold_ref matches that of alt_ref (same for matching columns of gold_query and alt_query).
calcknncorrWithinQuery | Calculates the k-NN correlation within the query cells only, which measures how well the sorted ordering of k nearest query neighbors in a query de novo PCA embedding correlate with the ordering for the cells in the reference mapping embedding.
calcPerCellMappingMetric | Per-cell Confidence Score: Calculates the weighted Mahalanobis distance for the query cells to reference clusters. Returns a vector of distance scores, one per query cell. Higher distance metric indicates less confidence.
calcPerClusterMappingMetric | Per-cluster Confidence Score: Calculates the Mahalanobis distance from user-defined query clusters to their nearest reference centroid after initial projection into reference PCA space. All query cells in a cluster get the same score. Higher distance indicates less confidence. Due to the instability of estimating covariance with small numbers of cells, we do not assign a score to clusters smaller than u * d, where d is the dimensionality of the embedding and u is specified.
evaluate | Function for evaluating F1 by cell type, adapted from automated cell type identifiaction benchmarking paper (Abdelaal et al. Genome Biology, 2019)
findVariableGenes | Function to find variable genes using mean variance relationship method
knnPredict | Predict annotations of query cells from the reference using k-NN method
mapQuery | Function for mapping query cells to a Symphony reference
pbmcs_exprs_small | Log(CP10k+1) normalized counts matrix (genes by cells) for 10x PBMCs dataset for vignette.
pbmcs_meta_small | Metadata for 10x PBMCs dataset for vignette.
plotReference | Function to plot reference, colored by cell type
rowSDs | Calculate standard deviations by row
runPCAQueryAlone | Runs a standard PCA pipeline on query (1 batch). Assumes query_exp is already normalized.
scaleDataWithStats | Scale data with given mean and standard deviations
symphony | symphony
vargenes_vst | Function to find variable genes using variance stabilizing transform (vst) method