In PMBio/MuDataMAE: Serialization for MultiAssayExperiment Objects

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Introduction

CITE-seq data provide RNA and surface protein counts for the same cells. This tutorial shows how MuData can be integrated into with Bioconductor workflows to analyse CITE-seq data.

Installation

The most recent dev build can be installed from GitHub:

library(remotes)
remotes::install_github("ilia-kats/MuData")

Stable version of MuData will be available in future bioconductor versions.

Loading libraries

library(MuData)
library(SingleCellExperiment)
library(MultiAssayExperiment)
library(SingleCellMultiModal)
library(scater)

library(rhdf5)

Loading data

We will use CITE-seq data accessible with the SingleCellMultiModal Bioconductor package, which was originally described in Stoeckius et al., 2017.

mae <- CITEseq(
    DataType="cord_blood", modes="*", dry.run=FALSE, version="1.0.0"
)

mae

We see two modalities in the object — scRNAseq and scADT, the latter providing counts for antibody-derived tags. Notably, each experiment is a matrix.

Processing ADT data

While CITE-seq analysis workflows such as CiteFuse should be consulted for more details, below we exemplify simple data transformation in order to demonstrate how their output can be saved to an H5MU file later on.

For ADT counts, we will apply CLR transformation following Hao et al., 2020:

# Define CLR transformation as in the Seurat workflow
clr <- function(data) t(
  apply(data, 1, function(x) log1p(
    x / (exp(sum(log1p(x[x > 0]), na.rm = TRUE) / length(x)))
  ))
)

We will make the ADT modality a SingleCellExperiment object and add an assay with CLR-transformed counts:

adt_counts <- mae[["scADT"]]

mae[["scADT"]] <- SingleCellExperiment(adt_counts)
assay(mae[["scADT"]], "clr") <- clr(adt_counts)

We will also generate reduced dimensions taking advantage of the functionality in the scater package:

mae[["scADT"]] <- runPCA(
  mae[["scADT"]], exprs_values = "clr", ncomponents = 20
)

plotReducedDim(mae[["scADT"]], dimred = "PCA",
               by_exprs_values = "clr", colour_by = "CD3")
plotReducedDim(mae[["scADT"]], dimred = "PCA",
               by_exprs_values = "clr", colour_by = "CD14")

Writing H5MU files

We can write the contents of the MultiAssayExperiment object into an H5MU file:

writeH5MU(mae, "cord_blood_citeseq.h5mu")

We can check that both modalities were written to the file, whether it was a matrix for RNA or SingleCellExperiment for ADT:

h5 <- rhdf5::H5Fopen("cord_blood_citeseq.h5mu")
h5ls(H5Gopen(h5, "mod"), recursive = FALSE)

... both assays for ADT — raw counts are stored in X and CLR-transformed counts are in the corresponding layer:

h5ls(H5Gopen(h5, "mod/scADT"), recursive = FALSE)
h5ls(H5Gopen(h5, "mod/scADT/layers"), recursive = FALSE)

... as well as reduced dimensions (PCA):

h5ls(H5Gopen(h5, "mod/scADT/obsm"), recursive = FALSE)
# There is an alternative way to access groups:
# h5&'mod'&'scADT'&'obsm'
rhdf5::H5close()

References

Muon: multimodal omics analysis framework preprint
mudata (Python) documentation
muon documentation and web page
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P.K., Swerdlow, H., Satija, R. and Smibert, P., 2017. Simultaneous epitope and transcriptome measurement in single cells. Nature methods, 14(9), pp.865-868.
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck III, W.M., Zheng, S., Butler, A., Lee, M.J., Wilk, A.J., Darby, C., Zager, M. and Hoffman, P., 2021. Integrated analysis of multimodal single-cell data. Cell.