BarcodeObj-class {CellBarcode} | R Documentation |
A S4 object holds the barcode data and samples' metadata. A set of operations can be applied to the BarcodeObj object for quality control and selecting barcodes/samples subset.
The BarcodeObj object is a S4 object, it has three slots,
which can be access by "@" operator, they are messyBc
, cleanBc
and
metadata
. A BarcodeObj
object can be generated by bc_extract
function. The bc_extract
function can use various data types as input,
such as data.frame, fastq files or ShortReadQ.
Slot messyBc
is a list holds the raw barcodes sequence before filtering,
where each element is a data.table
corresponding to the successive samples.
Each table has 5 columns: 1. reads_seq
: full read sequence before
parsing. 2. match_seq
: the sequence matched by pattern given to
bc_extract
. 3. umi_seq
(optional): UMI sequence. 4.
barcode_seq
: barcode sequence. 5. count
: how many reads a full sequence
has. In this table, barcode_seq
value can be duplicated, as two different
full read sequences can contain the same barcode sequence, due to the
diversity of the UMI or mutations in the constant region.
Slot cleanBc
is a list
holds the barcodes sequence after applying filtering,
where each element is a data.table
corresponding to the successive samples.
The "cleanBc" slot contains 2 columns 1. barcode_seq
: barcode sequence
2. counts
: reads count, or UMI count if the cleanBc
was created by
bc_cure_umi
.
####### # Create BarcodeObj with fastq file fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") library(ShortRead) bc_extract(fq_file, pattern = "AAAAA(.*)CCCCC") ####### # data manipulation on BarcodeObj object data(bc_obj) bc_obj # Select barcodes bc_subset(bc_obj, barcode = c("AACCTT", "AACCTT")) bc_obj[c("AGAG", "AAAG"), ] # Select samples by meta data bc_meta(bc_obj)$phenotype <- c("l", "b") bc_meta(bc_obj) bc_subset(bc_obj, sample = phenotype == "l") # Select samples by sample name bc_obj[, "test1"] bc_obj[, c("test1", "test2")] bc_subset(bc_obj, sample = "test1", barcode = c("AACCTT", "AACCTT")) # Apply barcodes black list bc_subset( bc_obj, sample = c("test1", "test2"), barcode = c("AACCTT")) # Join two samples with no barcodes overlap bc_obj["AGAG", "test1"] + bc_obj["AAAG", "test2"] # Join two samples with barcodes overlap bc_obj_join <- bc_obj["AGAG", "test1"] + bc_obj["AGAG", "test2"] bc_obj_join # The same barcode will be merged after applying bc_cure_depth() bc_cure_depth(bc_obj_join) # Remove barcodes bc_obj bc_obj - "AAAG" # Select barcodes in a white list bc_obj bc_obj * "AAAG" ###