,
Ahmad Al Ajami [aut] ,
Federico Marini [aut] ,
Katharina Imkeller [aut] | Title: | S4 Class for Single Cell Data with Allele and Functional Levels for Immune Genes |
|---|---|
| Description: | Defines a S4 class that is based on SingleCellExperiment. In addition to the usual gene layer the object can also store data for immune genes such as HLAs, Igs and KIRs at allele and functional level. The package is part of a workflow named single-cell ImmunoGenomic Diversity (scIGD), that firstly incorporates allele-aware quantification data for immune genes. This new data can then be used with the here implemented data structure and functionalities for further data handling and data analysis. |
| Authors: | Jonas Schuck [aut, cre] ,
Ahmad Al Ajami [aut] ,
Federico Marini [aut] ,
Katharina Imkeller [aut] |
| Maintainer: | Jonas Schuck <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.3.0 |
| Built: | 2024-10-31 05:33:47 UTC |
| Source: | https://github.com/bioc/SingleCellAlleleExperiment |
Internal function for the first assay extension used in the
SingleCellAlleleExperiment() constructor, computing the first of the two
new subassays that get appended to the quantification assay.
This subassay contains the allele gene identifiers instead of the
allele identifiers present in the raw data and sums up the expression counts
of alleles that have the same allele gene identifiers.
alleles2genes(sce, lookup, exp_type, gene_symbols)alleles2genes(sce, lookup, exp_type, gene_symbols)
sce |
A |
lookup |
A data.frame object containing the lookup table. |
exp_type |
Internal character string parameter that determines in which
format the gene symbols in the input data are. Can be |
gene_symbols |
A logical parameter to decide whether to compute additional gene gene symbols in case the raw data only contains ENSEMBL gene identifiers. |
A SingleCellExperiment object
Check package installation for optional functionalities
check_valid_optional_package(log, gene_symbols)check_valid_optional_package(log, gene_symbols)
log |
A logical parameter to decide if a logcounts assay should be computed
based on library factors computed with |
gene_symbols |
A logical parameter to decide whether to compute additional gene gene symbols in case the raw data only contains ENSEMBL gene identifiers. |
Error messages if cases are met
Extend rowData with new annotation columns
ext_rd(sce, exp_type, gene_symbols, verbose = FALSE)ext_rd(sce, exp_type, gene_symbols, verbose = FALSE)
sce |
A |
exp_type |
Internal character string parameter that determines in which
format the gene symbols in the input data are. Can be |
gene_symbols |
A logical parameter to decide whether to compute additional gene gene symbols in case the raw data only contains ENSEMBL gene identifiers. |
verbose |
A logical parameter to decide if runtime-messages should be
shown during function execution. Use |
A SingleCellExperiment object
Internal function used in get_allelecounts() to subsample the
quantification assay and only return the rows specifying
allele-quantification information.
find_allele_ids(sce)find_allele_ids(sce)
sce |
A |
A SingleCellExperiment object
Internal function for the second assay extension used in the
SingleCellAlleleExperiment() constructor, computing the second of the two
new subassays that get appended to the quantification assay. This subassay
contains the functional allele classes and sums up the expression counts of
the allele genes that are in the same functional group.
genes2functional(sce, lookup, exp_type, gene_symbols)genes2functional(sce, lookup, exp_type, gene_symbols)
sce |
A |
lookup |
A data.frame object containing the lookup table. |
exp_type |
Internal character string parameter that determines in which
format the gene symbols in the input data are. Can be |
gene_symbols |
A logical parameter to decide whether to compute additional gene gene symbols in case the raw data only contains ENSEMBL gene identifiers. |
A SingleCellExperiment object
Getter function returning subsampled SCAE object with all rows containing immune gene information. These rows are identified by "I" in rowData(scae)$NI_I and "G" in rowData(scae)$Quant_type.
get_agenes(scae)get_agenes(scae)
scae |
A |
A SingleCellAlleleExperiment object.
Internal function used to build a subassay containing counts from raw alleles. The rownames of this subassay are already translated to the corresponding immune gene identifier, which are extracted from the lookup table.
get_allelecounts(sce, lookup)get_allelecounts(sce, lookup)
sce |
A |
lookup |
A data.frame object containing the lookup table. |
A SingleCellExperiment object
Creates a knee plot information, ranking the barcodes according to their
total UMI count. The information is later on passed to the
metadata(scae)[["knee_info"]] slot.
get_knee_info(matrix, genes, barcodes)get_knee_info(matrix, genes, barcodes)
matrix |
A sparse |
genes |
A data.frame object containing gene identifiers. |
barcodes |
A data.frame object containing barcode identifiers. |
A list including a data.frame with barcode rank information, the corresponding knee and inflection point.
Get NCBI genes using the org.HS.db package
get_ncbi_org(sce)get_ncbi_org(sce)
sce |
A |
A list of character strings for gene names.
Getter function returning subsampled SCAE object with all rows containing non-immune gene information. These rows are identified by "NI" in rowData(scae)$NI_I and "G" in rowData(scae)$Quant_type.
get_nigenes(scae)get_nigenes(scae)
scae |
A |
A SingleCellAlleleExperiment object.
Main read in function for reading in allele quantification data and
loading the data into an SingleCellAlleleExperiment object.
read_allele_counts( samples_dir, sample_names = names(samples_dir), filter_mode = c("no", "yes", "custom"), lookup_file = lookup, barcode_file = "cells_x_genes.barcodes.txt", gene_file = "cells_x_genes.genes.txt", matrix_file = "cells_x_genes.mtx", filter_threshold = NULL, log = FALSE, gene_symbols = FALSE, verbose = FALSE, BPPARAM = BiocParallel::SerialParam() )read_allele_counts( samples_dir, sample_names = names(samples_dir), filter_mode = c("no", "yes", "custom"), lookup_file = lookup, barcode_file = "cells_x_genes.barcodes.txt", gene_file = "cells_x_genes.genes.txt", matrix_file = "cells_x_genes.mtx", filter_threshold = NULL, log = FALSE, gene_symbols = FALSE, verbose = FALSE, BPPARAM = BiocParallel::SerialParam() )
samples_dir |
A character string determining the path to one directory containing input files. |
sample_names |
A character string for a sample identifier. Can be used to describe the used dataset or sample. |
filter_mode |
A vector containing three character strings that describe
different options for filtering. The value |
lookup_file |
A character string determining the path to the lookup table. |
barcode_file |
A character string determining the name of the file containing the barcode identifiers. |
gene_file |
A character string determining the name of the file containing the feature identifiers. |
matrix_file |
A character string determining the name of the file containing the count matrix. |
filter_threshold |
An integer value used as a threshold for filtering
low-quality barcodes/cells. Standard value is |
log |
A logical parameter to decide if logcounts assay should be computed
based on library factors computed with |
gene_symbols |
A logical parameter to decide whether to compute additional gene gene symbols in case the raw data only contains ENSEMBL gene identifiers. |
verbose |
A logical parameter to decide if additional runtime-messages
should be shown during function execution. Use |
BPPARAM |
A BiocParallelParam object specifying how loading should be parallelized for multiple samples. |
The SingleCellAlleleExperiment data structure serves as a data representation
for data generated with the scIGD workflow.
This workflow allows for the quantification of expression and interactive
exploration of donor-specific alleles of different immune genes and its
Input data are generated by the scIGD workflow is stored in a shared folder.
Expected naming scheme of the files from the data generating method:
quantification matrix: cells_x_genes.mtx
barcode information: cells_x_genes.barcodes.txt
feature information: cells_x_genes.genes.txt
allele lookup table: lookup_table.csv
File identifiers can be specifically stated if renamed.
Optional features:
Filtering: Used parameter is filter_mode. Default filtering is performed
with a threshold=0 UMIs. filter_mode="yes" performs advanced filtering based
on ranking the barcodes and infering a inflection point of a
knee plot. Information
regarding the knee plot is exported in the metadata(scae)[["knee_info"]] slot
for later plotting (see vignette).
Computing a logcount assay by normalizing the input data based on a
sizeFactor method recommended for single-cell data. Used parameter is
log=TRUE/FALSE.
Computing additional gene symbols in case the input data only contains gene
identifiers represented as Ensembl ids. Used parameter is gene_symbols=TRUE/FALSE.
A SingleCellAlleleExperiment object.
example_data_5k <- scaeData::scaeDataGet(dataset="pbmc_5k") lookup_name <- "pbmc_5k_lookup_table.csv" lookup <- read.csv(system.file("extdata", lookup_name, package="scaeData")) # preflight mode, default filtering with a threshold of 0 UMI counts scae_preflight <- read_allele_counts(example_data_5k$dir, sample_names="example_data", filter_mode="no", lookup_file=lookup, barcode_file=example_data_5k$barcodes, gene_file=example_data_5k$features, matrix_file=example_data_5k$matrix, filter_threshold=NULL) scae_preflight # automatic filtering mode, filtering out low-quality cells # on the inflection point of the knee plot #scae_filtered <- read_allele_counts(example_data_5k$dir, # sample_names="example_data", # filter_mode="yes", # lookup_file=lookup, # barcode_file=example_data_5k$barcodes, # gene_file=example_data_5k$features, # matrix_file=example_data_5k$matrix, # filter_threshold=NULL, # verbose=TRUE) # scae_filtered # custom filtering mode, setting up a custom filter threshold for filtering # scae_custom_filter <- read_allele_counts(example_data_5k$dir, # sample_names="example_data", # filter_mode="custom", # lookup_file=lookup, # barcode_file=example_data_5k$barcodes, # gene_file=example_data_5k$features, # matrix_file=example_data_5k$matrix, # filter_threshold=200) # scae_custom_filterexample_data_5k <- scaeData::scaeDataGet(dataset="pbmc_5k") lookup_name <- "pbmc_5k_lookup_table.csv" lookup <- read.csv(system.file("extdata", lookup_name, package="scaeData")) # preflight mode, default filtering with a threshold of 0 UMI counts scae_preflight <- read_allele_counts(example_data_5k$dir, sample_names="example_data", filter_mode="no", lookup_file=lookup, barcode_file=example_data_5k$barcodes, gene_file=example_data_5k$features, matrix_file=example_data_5k$matrix, filter_threshold=NULL) scae_preflight # automatic filtering mode, filtering out low-quality cells # on the inflection point of the knee plot #scae_filtered <- read_allele_counts(example_data_5k$dir, # sample_names="example_data", # filter_mode="yes", # lookup_file=lookup, # barcode_file=example_data_5k$barcodes, # gene_file=example_data_5k$features, # matrix_file=example_data_5k$matrix, # filter_threshold=NULL, # verbose=TRUE) # scae_filtered # custom filtering mode, setting up a custom filter threshold for filtering # scae_custom_filter <- read_allele_counts(example_data_5k$dir, # sample_names="example_data", # filter_mode="custom", # lookup_file=lookup, # barcode_file=example_data_5k$barcodes, # gene_file=example_data_5k$features, # matrix_file=example_data_5k$matrix, # filter_threshold=200) # scae_custom_filter
Internal function used in read_allele_counts() that reads in the data
stated in the given directory path.
read_from_sparse_allele(path, barcode_file, gene_file, matrix_file)read_from_sparse_allele(path, barcode_file, gene_file, matrix_file)
path |
A character string determining the path to the directory containing the input files. |
barcode_file |
A character string determining the name of the file containing the sample-tag quantification data. |
gene_file |
A character string determining the name of the file containing the feature identifiers. |
matrix_file |
A character string determining the name of the file containing the count matrix. |
A list with three data.frames containing the input data information.
Setter function for the rowData slot for the SingleCellAlleleExperiment class.
## S4 replacement method for signature 'SingleCellAlleleExperiment,ANY' rowData(x) <- value## S4 replacement method for signature 'SingleCellAlleleExperiment,ANY' rowData(x) <- value
x |
A |
value |
Value of valid type and content (see validty.R) |
If you set rowData(scae)<- NULL the mandatory columns "NI_I" and "Quant_type"
will be kept silently, setting all other columns to NULL.
If you want to change the content of the mandatory "NI_I" and "Quant_type" columns check the valid values:
NI_I: c("NI" and "I") are valid values.
Quant_type: c("A", "G" "F") are valid values.
A SingleCellAlleleExperiment object
Setter function for the rowData slot for the SingleCellAlleleExperiment class.
## S4 replacement method for signature 'SingleCellAlleleExperiment,NULL' rowData(x) <- value## S4 replacement method for signature 'SingleCellAlleleExperiment,NULL' rowData(x) <- value
x |
A |
value |
NULL |
A SingleCellAlleleExperiment object
Function used for subsetting the different layers stored in a
SingleCellAlleleExperiment object. Valid subset values are:
subset=c("nonimmune", "alleles", "immune_genes", "functional_groups").
scae_subset( scae, subset = c("nonimmune", "alleles", "immune_genes", "functional_groups") )scae_subset( scae, subset = c("nonimmune", "alleles", "immune_genes", "functional_groups") )
scae |
SCAE object |
subset |
Character string specifying a data layer. Valid values are
|
SCAE object
example_data_5k <- scaeData::scaeDataGet(dataset="pbmc_5k") lookup_name <- "pbmc_5k_lookup_table.csv" lookup <- read.csv(system.file("extdata", lookup_name, package="scaeData")) scae <- read_allele_counts(example_data_5k$dir, sample_names="example_data_wta", filter_mode="no", lookup_file=lookup, barcode_file=example_data_5k$barcodes, gene_file=example_data_5k$features, matrix_file=example_data_5k$matrix, filter_threshold=0, verbose=TRUE) scae scae_nonimmune_subset <- scae_subset(scae, subset="nonimmune") scae_nonimmune_subset scae_alleles_subset <- scae_subset(scae, subset="alleles") scae_alleles_subset scae_immune_genes_subset <- scae_subset(scae, subset="immune_genes") scae_immune_genes_subset scae_functional_groups_subset <- scae_subset(scae, subset="functional_groups") scae_functional_groups_subsetexample_data_5k <- scaeData::scaeDataGet(dataset="pbmc_5k") lookup_name <- "pbmc_5k_lookup_table.csv" lookup <- read.csv(system.file("extdata", lookup_name, package="scaeData")) scae <- read_allele_counts(example_data_5k$dir, sample_names="example_data_wta", filter_mode="no", lookup_file=lookup, barcode_file=example_data_5k$barcodes, gene_file=example_data_5k$features, matrix_file=example_data_5k$matrix, filter_threshold=0, verbose=TRUE) scae scae_nonimmune_subset <- scae_subset(scae, subset="nonimmune") scae_nonimmune_subset scae_alleles_subset <- scae_subset(scae, subset="alleles") scae_alleles_subset scae_immune_genes_subset <- scae_subset(scae, subset="immune_genes") scae_immune_genes_subset scae_functional_groups_subset <- scae_subset(scae, subset="functional_groups") scae_functional_groups_subset
Getter function returning subsampled SCAE object with all rows containing raw allele information. These rows are identified by "I" in rowData(scae)$NI_I and "A" in rowData(scae)$Quant_type.
scae_subset_alleles(scae)scae_subset_alleles(scae)
scae |
A |
A SingleCellAlleleExperiment object.
Getter function returning subsampled SCAE object with all rows containing functional class information. These rows are identified by "I" in rowData(scae)$NI_I and "F" in rowData(scae)$Quant_type.
scae_subset_functional(scae)scae_subset_functional(scae)
scae |
A |
A SingleCellAlleleExperiment object.
The SingleCellAlleleExperiment class is a comprehensive multi-layer data structure, enabling the representatino of immune genes at specific levels, including alleles, genes and groups of functionally similar genes. This data representation allows data handling and data analysis across these immunological relevant, different layers of annotation.
SingleCellAlleleExperiment( ..., lookup, metadata = NULL, threshold = 0, exp_type = "ENS", log = TRUE, gene_symbols = FALSE, verbose = FALSE )SingleCellAlleleExperiment( ..., lookup, metadata = NULL, threshold = 0, exp_type = "ENS", log = TRUE, gene_symbols = FALSE, verbose = FALSE )
... |
Arguments passed to the |
lookup |
A data.frame object containing the lookup table. |
metadata |
A list containing a dataframe and two integer values of
information regarding plotting a knee plot for quality control. This parameter
is linked to |
threshold |
An integer value used as a threshold for filtering low-quality barcodes/cells. |
exp_type |
Internal character string parameter that determines in which
format the gene symbols in the input data are. Can be |
log |
A logical parameter which determines if the user wants to
compute the |
gene_symbols |
A logical parameter to decide whether to compute additional gene gene symbols in case the raw data only contains ENSEMBL gene identifiers. |
verbose |
A logical parameter to decide if runtime-messages should be
shown during function execution. Use |
The SingleCellAlleleExperiment class builds upon and extends the data
representation that can be facilitated using a SingleCellExperiment
object.
The Constructor SingleCellAlleleExperiment() can be used on its own,
if raw data is processed accordingly (see examples) OR in a more
convenient way using this packages read in function read_allele_counts()
A getter function scae_subset() allows to subset the object according to
the newly implemented layers.
In this class, similar to the SingleCellExperiment class,
rows should represent genomic features (including immune genes, represented
as allele information), while columns represent single cells/barcodes.
The SingleCellAlleleExperiment data structure serves as a data representation
for data generated with the scIGD workflow.
This workflow allows for the quantification of expression and interactive
exploration of donor-specific alleles of different immune genes and its
A SingleCellAlleleExperiment object.
##-If you want to use the Constructor on its own, some preprocessing is ##-necessary to bring the data in proper format ##-Here, we use an example dataset found in in the `scaeData` package. ##-Find an alternative and recommended read in below as a second example example_data_5k <- scaeData::scaeDataGet(dataset="pbmc_5k") lookup_name <- "pbmc_5k_lookup_table.csv" lookup <- read.csv(system.file("extdata", lookup_name, package="scaeData")) barcode_loc <- file.path(example_data_5k$dir, example_data_5k$barcodes) feature_loc <- file.path(example_data_5k$dir, example_data_5k$features) matrix_loc <- file.path(example_data_5k$dir, example_data_5k$matrix) feature_info <- utils::read.delim(feature_loc, header=FALSE) cell_names <- utils::read.csv(barcode_loc, sep="", header=FALSE) mat <- t(Matrix::readMM(matrix_loc)) ##-Prepare input data colnames(feature_info) <- "Ensembl_ID" sample_names <- "pbmc_5k" sparse_mat <- as(mat, "CsparseMatrix") ##--colData cell_info_list <- S4Vectors::DataFrame(Sample=rep(sample_names, length(cell_names)), Barcode=cell_names$V1, row.names=NULL) ##--rowData and count matrix rownames(feature_info) <- feature_info[,1] cnames <- cell_info_list$Barcode colnames(sparse_mat) <- cnames scae <- SingleCellAlleleExperiment(assays=list(counts=sparse_mat), rowData=feature_info, colData=cell_info_list, lookup=lookup, verbose=TRUE) scae ##-OR, use the read in function `read_allele_counts()` !![RECOMMENDED]!! ##-Find more examples in its documentation using `?read_allele_counts` # scae_2 <- read_allele_counts(example_data_5k$dir, # sample_names="example_data", # filter_mode="no", # lookup_file=lookup, # barcode_file=example_data_5k$barcodes, # gene_file=example_data_5k$features, # matrix_file=example_data_5k$matrix, # verbose=TRUE) # scae_2##-If you want to use the Constructor on its own, some preprocessing is ##-necessary to bring the data in proper format ##-Here, we use an example dataset found in in the `scaeData` package. ##-Find an alternative and recommended read in below as a second example example_data_5k <- scaeData::scaeDataGet(dataset="pbmc_5k") lookup_name <- "pbmc_5k_lookup_table.csv" lookup <- read.csv(system.file("extdata", lookup_name, package="scaeData")) barcode_loc <- file.path(example_data_5k$dir, example_data_5k$barcodes) feature_loc <- file.path(example_data_5k$dir, example_data_5k$features) matrix_loc <- file.path(example_data_5k$dir, example_data_5k$matrix) feature_info <- utils::read.delim(feature_loc, header=FALSE) cell_names <- utils::read.csv(barcode_loc, sep="", header=FALSE) mat <- t(Matrix::readMM(matrix_loc)) ##-Prepare input data colnames(feature_info) <- "Ensembl_ID" sample_names <- "pbmc_5k" sparse_mat <- as(mat, "CsparseMatrix") ##--colData cell_info_list <- S4Vectors::DataFrame(Sample=rep(sample_names, length(cell_names)), Barcode=cell_names$V1, row.names=NULL) ##--rowData and count matrix rownames(feature_info) <- feature_info[,1] cnames <- cell_info_list$Barcode colnames(sparse_mat) <- cnames scae <- SingleCellAlleleExperiment(assays=list(counts=sparse_mat), rowData=feature_info, colData=cell_info_list, lookup=lookup, verbose=TRUE) scae ##-OR, use the read in function `read_allele_counts()` !![RECOMMENDED]!! ##-Find more examples in its documentation using `?read_allele_counts` # scae_2 <- read_allele_counts(example_data_5k$dir, # sample_names="example_data", # filter_mode="no", # lookup_file=lookup, # barcode_file=example_data_5k$barcodes, # gene_file=example_data_5k$features, # matrix_file=example_data_5k$matrix, # verbose=TRUE) # scae_2
Miscellaneous methods for the SingleCellAlleleExperiment class
and its descendants that do not fit into any other documentation category
such as, for example, show methods.
## S4 method for signature 'SingleCellAlleleExperiment' show(object)## S4 method for signature 'SingleCellAlleleExperiment' show(object)
object |
a |
Returns NULL