Package: symphony 0.1.1

symphony: Efficient and Precise Single-Cell Reference Atlas Mapping

Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.

Authors:Joyce Kang [aut, cre], Ilya Korsunsky [aut], Soumya Raychaudhuri [aut]

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symphony.pdf |symphony.html✨
symphony/json (API)

# Install 'symphony' in R:

install.packages('symphony', repos = c('https://joycekang.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

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.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.83 score 134 scripts 415 downloads 15 exports 54 dependencies

Last updated 2 years agofrom:28b2a1af71. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 20 2024
R-4.5-win-x86_64	NOTE	Nov 20 2024
R-4.5-linux-x86_64	NOTE	Nov 20 2024
R-4.4-win-x86_64	NOTE	Nov 20 2024
R-4.4-mac-x86_64	NOTE	Nov 20 2024
R-4.4-mac-aarch64	NOTE	Nov 20 2024
R-4.3-win-x86_64	NOTE	Nov 20 2024
R-4.3-mac-x86_64	NOTE	Nov 20 2024
R-4.3-mac-aarch64	NOTE	Nov 20 2024

Exports:buildReference buildReferenceFromHarmonyObj calcknncorr calcknncorrWithinQuery calcPerCellMappingMetric calcPerClusterMappingMetric evaluate findVariableGenes knnPredict mapQuery plotReference rowSDs runPCAQueryAlone scaleDataWithStats vargenes_vst

Dependencies:BH class cli colorspace cowplot cpp11 data.table dplyr dqrng fansi farver FNN generics ggplot2 glue gtable harmony irlba isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig purrr R6 RANN RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppProgress RhpcBLASctl rlang RSpectra scales sitmo stringi stringr tibble tidyr tidyselect utf8 uwot vctrs viridisLite withr

Quickstart Tutorial

Rendered fromquickstart_tutorial.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2021-08-05
Started: 2021-08-05

Citation

Readme and manuals

Help Manual

Help page	Topics
Function for building a Symphony reference starting from expression matrix	buildReference
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.	buildReferenceFromHarmonyObj
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).	calcknncorr
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.	calcknncorrWithinQuery
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.	calcPerCellMappingMetric
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.	calcPerClusterMappingMetric
Function for evaluating F1 by cell type, adapted from automated cell type identifiaction benchmarking paper (Abdelaal et al. Genome Biology, 2019)	evaluate
Function to find variable genes using mean variance relationship method	findVariableGenes
Predict annotations of query cells from the reference using k-NN method	knnPredict
Function for mapping query cells to a Symphony reference	mapQuery
Log(CP10k+1) normalized counts matrix (genes by cells) for 10x PBMCs dataset for vignette.	pbmcs_exprs_small
Metadata for 10x PBMCs dataset for vignette.	pbmcs_meta_small
Function to plot reference, colored by cell type	plotReference
Calculate standard deviations by row	rowSDs
Runs a standard PCA pipeline on query (1 batch). Assumes query_exp is already normalized.	runPCAQueryAlone
Scale data with given mean and standard deviations	scaleDataWithStats
symphony	symphony
Function to find variable genes using variance stabilizing transform (vst) method	vargenes_vst