README.md

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Efficient and precise single-cell reference atlas mapping with Symphony

Preprint on bioRxiv

Installation

Install the current version of Symphony from GitHub with:

# install.packages("devtools")
devtools::install_github("immunogenomics/symphony")

Install should take <10 mins (pending no major issues). See installation notes below.

Usage/Demos

Tutorials

Downloading pre-built references:

Reference building

Option 1: Starting from existing Harmony object

This function compresses an existing Harmony object into a Symphony reference that enables query mapping. We recommend this option for most users since it allows your code to be more modular and flexible.

# Run Harmony to integrate the reference cells
ref_harmObj = harmony::HarmonyMatrix(
        data_mat = t(Z_pca_ref),   # starting embedding (e.g. PCA, CCA) of cells
        meta_data = ref_metadata,  # dataframe with cell metadata
        theta = c(2),              # cluster diversity enforcement
        vars_use = c('donor'),     # variable to integrate out
        nclust = 100,              # number of clusters in Harmony model
        max.iter.harmony = 10,     # max iterations of Harmony
        return_object = TRUE,      # set to TRUE to return the full Harmony object
        do_pca = FALSE             # do not recompute PCs
)

# Build Symphony reference
reference = buildReferenceFromHarmonyObj(
        ref_harmObj,            # output object from HarmonyMatrix()
        ref_metadata,           # dataframe with cell metadata
        vargenes_means_sds,     # gene names, means, and std devs for scaling
        loadings,               # genes x PCs
        verbose = TRUE,         # display output?
        do_umap = TRUE,         # run UMAP and save UMAP model to file?
        save_uwot_path = '/absolute/path/uwot_model_1' # filepath to save UMAP model)

Note that vargenes_means_sds requires column names c('symbol', 'mean', 'stddev') (see tutorial example).

Option 2: Starting from reference genes by cells matrix

This function performs all steps of the reference building pipeline including variable gene selection, scaling, PCA, Harmony, and Symphony compression.

# Build reference
reference = symphony::buildReference(
    ref_exp,                   # reference expression (genes by cells)
    ref_metadata,              # reference metadata (cells x attributes)
    vars = c('donor'),         # variable(s) to integrate over
    K = 100,                   # number of Harmony soft clusters
    verbose = TRUE,            # display verbose output
    do_umap = TRUE,            # run UMAP and save UMAP model to file
    do_normalize = FALSE,      # perform log(CP10k) normalization on reference expression
    vargenes_method = 'vst',   # variable gene selection method: 'vst' or 'mvp'
    vargenes_groups = 'donor', # metadata column specifying groups for variable gene selection within each group
    topn = 2000,               # number of variable genes (per group)
    theta = 2,                 # Harmony parameter(s) for diversity term
    d = 20,                    # number of dimensions for PCA
    save_uwot_path = 'path/to/uwot_model_1', # file path to save uwot UMAP model
    additional_genes = NULL    # vector of any additional genes to force include
)

Query mapping

Once you have a prebuilt reference (e.g. loaded from a saved .rds R object), you can directly map cells from a new query dataset onto it starting from query gene expression.

# Map query
query = mapQuery(query_exp,             # query gene expression (genes x cells)
                 query_metadata,        # query metadata (cells x attributes)
                 reference,             # Symphony reference object
                 vars = NULL,           # Query batch variables to harmonize over (NULL treats query as one batch)
                 do_normalize = FALSE,  # perform log(CP10k) normalization on query (set to FALSE if already normalized)
                 do_umap = TRUE)        # project query cells into reference UMAP

query$Z contains the harmonized query feature embedding.

If your query itself has multiple sources of batch variation you would like to integrate over (e.g. technology, donors, species), you can specify them in the vars parameter: e.g. vars = c('donor', 'technology')

Installation notes

System requirements:

Symphony has been successfully installed on Linux and Mac OS X using the devtools package to install from GitHub.

Dependencies:

Troubleshooting:

devtools::install_github("immunogenomics/harmony")

We have been notified of the following installation errors regarding systemfonts, textshaping, and ragg (which are all required by ggrastr):

# error when installing systemfonts
ft_cache.h:9:10: fatal error: ft2build.h: No such file or directory

# error when installing textshaping
Configuration failed to find the harfbuzz freetype2 fribidi library

# error when installing ragg
<stdin>:1:10: fatal error: ft2build.h: No such file or directory

These errors are not inherent to the Symphony package and we cannot fix them directly. However, as a workaround, you can install systemfonts, textshaping, and ragg separately using install.packages() and specify the path to the required files (replacing /path/to below with the path to the appropriate include directory containing the files).

# fix to install systemfonts
withr::with_makevars(c(CPPFLAGS="-I/path/to/include/freetype2/"), install.packages("systemfonts"))

# fix to install textshaping
withr::with_makevars(c(CPPFLAGS="-I/path/to/include/harfbuzz/ -I/path/to/include/fribidi/ -I/path/to/include/freetype2/"), install.packages("textshaping"))

# fix to install ragg
withr::with_makevars(c(CPPFLAGS="-I/path/to/include/freetype2/"), install.packages("ragg"))

Reproducing results from manuscript

Code to reproduce Symphony results from the Kang et al. manuscript is available on github.com/immunogenomics/symphony_reproducibility.



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symphony documentation built on Aug. 5, 2021, 9:07 a.m.