ondisc
is a companion R package to sceptre that facilitates
analysis of large-scale single-cell data out-of-core on a laptop or
distributed across tens to hundreds processors on a cluster or cloud. In
both of these settings, ondisc requires only a few
gigabytes of memory, even if the input data are tens of gigabytes in
size. ondisc mainly is oriented toward single-cell CRISPR
screen analysis, but ondisc also can be used for
single-cell differential expression and single-cell co-expression
analyses. ondisc is powered by several new, efficient
algorithms for manipulating and querying large, sparse expression
matrices. Although ondisc and sceptre work
best in conjunction, ondisc can be used independently of
sceptre (and conversely, sceptre can be used
independently of ondisc).
Users can install ondisc using the code below.
ondisc depends on the Bioconductor package
Rhdf5lib, which should be installed from source before
installing ondisc. Users also should install
sceptredata, which contains the example data used in this
vignette.
# install.packages("BiocManager"); install.packages("devtools")
BiocManager::install("Rhdf5lib", type = "source") # Rhdf5lib
devtools::install_github("timothy-barry/ondisc") # ondisc
devtools::install_github("katsevich-lab/sceptredata") # sceptredataSee the frequently
asked questions page for tips on installing ondisc such
that it runs as fast as possible. We can load ondisc and
sceptredata by calling library().
The interface to ondisc is simple and minimal. The
package contains only one class: odm (short for
“ondisc matrix”). An odm object represents a
single-cell expression matrix stored on disk (as opposed to
in memory). odm objects can be used to store
expression matrices that are too large to fit in memory. Users can
create an odm object via one of two functions:
create_odm_from_cellranger() or
create_odm_from_r_matrix(). The former takes the output of
one or more calls to Cell Ranger count as input, while the latter takes
an R matrix (stored in standard format or sparse format) as input. Users
can interface with an odm object using several functions,
including the bracket ([,]) operator, which loads a
specified subset of the expression matrix into memory.
Initializing an odm object via
create_odm_from_cellranger()
ondisc provides two functions for initializing an
odm object: create_odm_from_cellranger() and
create_odm_from_r_matrix(). The former is considerably more
scalable and memory-efficient than the latter; thus, we recommend that
users employ create_odm_from_cellranger() when possible. We
illustrate use of create_odm_from_cellranger() on an
example single-cell CRISPR screen dataset stored in the
sceptredata package. The example data contain two
modalities, namely a gene modality and a CRISPR gRNA modality. There are
526 genes, 95 gRNAs, and 45,919 cells in the data. Users can read more
about the example data by evaluating
vignette("sceptredata") or
?highmoi_example_data in the console.
create_odm_from_cellranger() takes several arguments:
directories_to_load, directory_to_write,
write_cellwise_covariates, chunk_size,
compression_level, and grna_target_data_frame.
Only the first two of these arguments are required; the rest are set to
reasonable defaults. We describe the directories_to_load
and directory_to_write arguments below.
directories_to_load is a character vector specifying the
locations of one or more directories outputted by Cell Ranger count.
Below, we set directories_to_load to the (machine-specific)
location of the example data on disk.
directories_to_load <- paste0(
system.file("extdata", package = "sceptredata"),
"/highmoi_example/gem_group_", 1:2
)
directories_to_load # file paths to the example data on your computer## [1] "/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/sceptredata/extdata/highmoi_example/gem_group_1"
## [2] "/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/sceptredata/extdata/highmoi_example/gem_group_2"directories_to_load contains the file paths to two
directories, which correspond to cells sequenced across two batches. The
data are stored in feature
barcode format; each directory contains the files
barcodes.tsv.gz, features.tsv.gz, and
matrix.mtx.gz.
list.files(directories_to_load[1])## [1] "barcodes.tsv.gz" "features.tsv" "matrix.mtx"
list.files(directories_to_load[2])## [1] "barcodes.tsv.gz" "features.tsv.gz" "matrix.mtx.gz"Next, directory_to_write is a file path to the directory
in which to write the backing .odm file, which is the file
that will store the expression data on disk. .odm files
contain the same information as .mtx files but stored in a
more efficient format for CRISPR screen analysis, differential
expression analysis, and gene co-expression analysis. .odm
files simply are HDF5 files with special structure. We set
directory_to_write to temp_dir (i.e., the
temporary directory) in this example. The remaining arguments are
optional, and most users will not need to specify them; see
?create_odm_from_cellranger() for more information. Below,
we call create_odm_from_cellranger() on the example data,
saving the output of the function to the variable
out_list.
temp_dir <- tempdir()
out_list <- create_odm_from_cellranger(
directories_to_load = directories_to_load,
directory_to_write = temp_dir
)out_list contains three entries: gene,
grna, and cellwise_covariates.
gene and grna are the odm objects
corresponding to the gene and gRNA modalities, respectively. Meanwhile,
cellwise_covariates is a data frame that contains the
cell-wise covariates. (More on the cell-wise covariates later.) An
inspection of temp_dir reveals that the files
gene.odm and grna.odm have been written to
this directory.
list.files(temp_dir, pattern = "*.odm")## [1] "gene.odm" "grna.odm"Interacting with the odm object
We extract the odm object corresponding to the gene
modality as follows.
gene_odm <- out_list[["gene"]]Evaluating an odm object in the console prints
information about the matrix, including the number of features and cells
contained within the matrix, as well as the file path to the
(machine-specific) backing .odm file.
gene_odm## An object of class odm with the following attributes:
## • 526 features
## • 45919 cells
## • Backing file: /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpiobX7h/gene.odmodm objects support several key matrix operations,
including ncol(), nrow(),
rownames(), and [,]. ncol() and
nrow() return the number of rows (i.e., features) and
columns (i.e., cells) contained within the matrix, respectively.
n_features <- nrow(gene_odm)
n_features## [1] 526
n_cells <- ncol(gene_odm)
n_cells## [1] 45919Next, rownames() returns the feature IDs.
## [1] "ENSG00000069275" "ENSG00000117222" "ENSG00000117266" "ENSG00000117280"
## [5] "ENSG00000133059" "ENSG00000133065"Finally, the bracket operator ([,]) loads a specified
row of the expression matrix into memory. One can index into the rows by
integer index or feature ID, as follows.
expression_vector <- gene_odm[2,]
head(expression_vector)## [1] 2 1 0 1 1 0
expression_vector <- gene_odm["ENSG00000117222",]
head(expression_vector)## [1] 2 1 0 1 1 0Indexing into an odm object by column is not supported.
Finally, odm objects take up very little space, as the data
are stored on disk rather than in-memory. For example,
gene_odm takes up only 40 kilobytes of memory.
object.size(gene_odm) |> format(units = "Kb")## [1] "38.7 Kb"Supported modalities
ondisc supports the following Cell Ranger modalities:
Gene Expression, CRISPR Guide Capture (i.e.,
gRNA expression), and Antibody Capture (i.e., protein
expression). (The modality of a given feature is listed within the third
column of the unzipped features.tsv file; see the Cell
Ranger documentation for more information.) The table below maps the
modality name used by Cell Ranger to that used by
ondisc.
| Cell Ranger modality name |
ondisc modality name |
|---|---|
Gene Expression |
gene |
CRISPR Guide Capture |
grna |
Antibody Capture |
protein |
We provide an example of using
create_odm_from_cellranger() to import a dataset containing
three modalities: gene expression, gRNA expression, and protein
expression. We use a synthetic dataset for this purpose (so as to reduce
the amount of data stored within the sceptredata package).
To this end we call the function
write_example_cellranger_dataset(), which creates a
synthetic single-cell dataset, writing the dataset to disk in Cell
Ranger feature barcode format. (See
?write_example_cellranger_dataset() for more information
about this function.) We create a synthetic single-cell dataset
consisting of 500 genes, 50 gRNAs, 20 proteins, and 10,000 cells.
Furthermore, we specify that the cells are sequenced across three
batches. We write the synthetic dataset to the directory
temp_dir.
set.seed(4)
example_data <- write_example_cellranger_dataset(
n_features = c(500, 50, 20),
n_cells = 10000,
n_batch = 3,
modalities = c("gene", "grna", "protein"),
directory_to_write = temp_dir ,
p_set_col_zero = 0
)The synthetic data are contained in the directories
batch_1, batch_2, and batch_3
within temp_dir:
directories_to_load <- list.files(
temp_dir,
pattern = "batch_",
full.names = TRUE
)
directories_to_load## [1] "/var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpiobX7h/batch_1"
## [2] "/var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpiobX7h/batch_2"
## [3] "/var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpiobX7h/batch_3"Each of these directories contains the files
matrix.mtx.gz, features.tsv.gz, and
barcodes.tsv.gz. For example, the contents of the
batch_1 are as follows.
list.files(directories_to_load[1])## [1] "barcodes.tsv.gz" "features.tsv.gz" "matrix.mtx.gz"We call create_odm_from_cellranger() to import these
data, saving the output of the function in the variable
out_list.
out_list <- create_odm_from_cellranger(
directories_to_load = directories_to_load,
directory_to_write = temp_dir
)out_list contains the cell-wise covariate data frame
alongside odm objects corresponding to the gene, gRNA, and
protein modalities.
names(out_list)## [1] "gene" "grna" "protein"
## [4] "cellwise_covariates"Moreover, the files gene.odm, grna.odm, and
protein.odm have been written to disk. (The previous
gene.odm and grna.odm files are
overwritten.)
list.files(temp_dir, pattern = "*.odm")## [1] "gene.odm" "grna.odm" "protein.odm"The cell-wise covariate data frame
As part of importing the data,
create_odm_from_cellranger() computes the cell-wise
covariates. We print the first few rows of the cell-wise covariate data
frame corresponding to the synthetic data below.
cellwise_covariates <- out_list[["cellwise_covariates"]]
head(cellwise_covariates)## gene_n_umis gene_n_nonzero gene_p_mito grna_n_umis grna_n_nonzero
## <int> <int> <num> <int> <int>
## 1: 1030 196 0.4330097 131 22
## 2: 1034 187 0.4197292 126 24
## 3: 1142 203 0.4168126 126 20
## 4: 1177 217 0.4188615 119 21
## 5: 1083 207 0.4524469 118 24
## 6: 1095 193 0.4000000 89 17
## grna_feature_w_max_expression grna_frac_umis_max_feature protein_n_umis
## <char> <num> <int>
## 1: grna_33 0.07633588 64
## 2: grna_6 0.07936508 46
## 3: grna_31 0.07936508 51
## 4: grna_22 0.08403361 44
## 5: grna_20 0.08474576 31
## 6: grna_11 0.11235955 56
## protein_n_nonzero batch
## <int> <fctr>
## 1: 9 batch_1
## 2: 9 batch_1
## 3: 8 batch_1
## 4: 8 batch_1
## 5: 7 batch_1
## 6: 9 batch_1The modality to which a given covariate corresponds (“gene”, “grna”, or “protein”) is prepended to the name of the covariate. We describe each covariate below.
gene_n_umis: the number of gene UMIs sequenced in a given cell.gene_n_nonzero: the number of genes that exhibit nonzero expression in a given cell.gene_p_mito: the fraction of gene transcripts that map to mitochondrial genes in a given cell. (Mitochondrial genes are identified as genes whose name starts with"MT-"or"mt-".)grna_n_umis: similar togene_n_umisbut for the gRNA modality.grna_n_nonzero: similar togene_n_nonzerobut for the gRNA modality.grna_feature_w_max_expression: the ID of the gRNA that exhibits the maximum UMI count in a given cell.grna_frac_umis_max_feature: the fraction of UMIs that the maximally expressed gRNA in a given cell constitutes.protein_n_umis: similar togene_n_umisbut for the protein modality.protein_n_nonzero: similar togene_n_nonzerobut for the protein modality.batch: the batch in which a given cell was sequenced. Cells loaded from different directories are assumed to belong to different batches.
sceptre uses the covariates
grna_feature_w_max_expression and
grna_frac_umis_max_feature to assign gRNAs to cells.
Reading an .odm file into R
Users can read an .odm file into R by calling the
function initialize_odm_from_backing_file(). Below, we
delete all variables from the global namespace. Then, we call
initialize_odm_from_backing_file() on the file
gene.odm stored within temp_dir, which loads
the gene expression matrix that we created in the previous step.
rm(list = ls()) # delete all variables
temp_dir <- tempdir()
gene_odm <- initialize_odm_from_backing_file(
paste0(temp_dir, "/gene.odm")
)
gene_odm## An object of class odm with the following attributes:
## • 500 features
## • 10000 cells
## • Backing file: /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpiobX7h/gene.odm.odm files are portable. Thus, a user can create an
.odm file on one computer, move the .odm file
to another computer, and then open the .odm file on the
second computer. Note that odm objects themselves are not
portable; thus, to move an odm object from one computer to
another, the user should transfer the underlying .odm file
to the second computer and then open the .odm file on the
second computer via initialize_odm_from_backing_file().
Initializing an odm object via
create_odm_from_r_matrix()
We recommend that users create an odm object via
create_odm_from_cellranger(), as this function is highly
scalable and typically requires only a couple gigabytes of memory.
However, users also can convert an R matrix into an odm
object via the function create_odm_from_r_matrix().
create_odm_from_r_matrix() takes two main arguments:
mat and file_to_write. mat is a
standard R matrix (of type "matrix") or a sparse R matrix
(of type "dgCMatrix", "dgRMatrix", or
"dgTMatrix"). mat should contain row names
giving the ID of each feature. Next, file_to_write is a
fully-qualified file path specifying the location in which to write the
backing .odm file. We provide an example of calling
create_odm_from_r_matrix() on a gene-by-cell expression
matrix contained in the sceptredata package.
data(lowmoi_example_data)
gene_mat <- lowmoi_example_data$response_matrixgene_mat is a gene expression matrix containing 299
genes and 20,729 cells. (Users can evaluate
?lowmoi_example_data to see more information about this
matrix.) We pass this matrix to create_odm_from_r_matrix(),
setting file_to_write to
paste0(temp_dir, "/gene.odm").
file_to_write <- paste0(temp_dir, "/gene.odm")
gene_odm <- create_odm_from_r_matrix(
mat = gene_mat,
file_to_write = file_to_write
)gene_odm is a standard odm object.
gene_odm## An object of class odm with the following attributes:
## • 299 features
## • 20729 cells
## • Backing file: /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpiobX7h/gene.odmMoreover, the file gene.odm has been written to
temp_dir. (The previous gene.odm file is
overwritten.)
Notes on compression
create_odm_from_cellranger() and
create_odm_from_r_matrix() take optional arguments
chunk_size and compression_level (which are
set to reasonable defaults). chunk_size and
compression_level control the extent to which the backing
.odm file is compressed. chunk_size should be
a positive integer, and compression_level should be an
integer in the range of 0 to 9. Increasing the value of these arguments
increases the level of compression, thereby leading to a
smaller file size for the backing .odm file (but possibly
longer read and write times).
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