,
Anne-Marie Lyne [aut],
Leila Perie [aut]| Title: | Cellular DNA Barcode Analysis toolkit |
|---|---|
| Description: | The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data. |
| Authors: | Wenjie Sun [cre, aut] ,
Anne-Marie Lyne [aut],
Leila Perie [aut] |
| Maintainer: | Wenjie Sun <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.11.0 |
| Built: | 2024-07-17 12:02:39 UTC |
| Source: | https://github.com/bioc/CellBarcode |
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 that holds the raw barcodes sequence without filtering,
where each element is a data.table corresponding to the successive samples.
Each table has 3 columns: 1. umi_seq (optional): UMI sequence. 2.
barcode_seq: barcode sequence. 3. count: how many reads a full sequence
has. In this table, barcode_seq value can be duplicated, as two different
full read sequences can have 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 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.
A BarcodeObj object.
####### # 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 metadata bc_meta(bc_obj)$phenotype <- c("l", "b") bc_meta(bc_obj) bc_subset(bc_obj, sample = phenotype == "l") # Select samples by sample name bc_obj[, "test1"] bc_obj[, c("test1", "test2")] bc_subset(bc_obj, sample = "test1", barcode = c("AACCTT", "AACCTT")) # Apply barcodes blacklist bc_subset( bc_obj, sample = c("test1", "test2"), barcode = c("AACCTT")) # Join two samples with no barcodes overlap bc_obj["AGAG", "test1"] + bc_obj["AAAG", "test2"] # Join two samples with overlap barcodes bc_obj_join <- bc_obj["AGAG", "test1"] + bc_obj["AGAG", "test2"] bc_obj_join # The same barcode will be merged after applying bc_cure_depth() bc_cure_depth(bc_obj_join) # Remove barcodes bc_obj bc_obj - "AAAG" # Select barcodes in a white list bc_obj bc_obj * "AAAG" ########## # Create BarcodeObj with fastq file fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") library(ShortRead) bc_extract(fq_file, pattern = "AAAAA(.*)CCCCC") ####### # data manipulation on BarcodeObj object data(bc_obj) bc_obj # Select barcodes bc_subset(bc_obj, barcode = c("AACCTT", "AACCTT")) bc_obj[c("AGAG", "AAAG"), ] # Select samples by metadata bc_meta(bc_obj)$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 blacklist 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 overlap barcodes 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" ###
Transforms BarcodeObj object into data.frame, data.table or
matrix.
bc_2df(barcodeObj) bc_2dt(barcodeObj) bc_2matrix(barcodeObj) ## S4 method for signature 'BarcodeObj' bc_2df(barcodeObj) ## S4 method for signature 'BarcodeObj' bc_2dt(barcodeObj) ## S4 method for signature 'BarcodeObj' bc_2matrix(barcodeObj)bc_2df(barcodeObj) bc_2dt(barcodeObj) bc_2matrix(barcodeObj) ## S4 method for signature 'BarcodeObj' bc_2df(barcodeObj) ## S4 method for signature 'BarcodeObj' bc_2dt(barcodeObj) ## S4 method for signature 'BarcodeObj' bc_2matrix(barcodeObj)
barcodeObj |
A |
A data.frame, with two columns: barcode_seq and
count.
data(bc_obj) bc_obj <- bc_cure_depth(bc_obj) # BarcodeObj to data.frame bc_2df(bc_obj) # BarcodeObj to data.table bc_2dt(bc_obj) # BarcodeObj to matrix bc_2matrix(bc_obj) ###data(bc_obj) bc_obj <- bc_cure_depth(bc_obj) # BarcodeObj to data.frame bc_2df(bc_obj) # BarcodeObj to data.table bc_2dt(bc_obj) # BarcodeObj to matrix bc_2matrix(bc_obj) ###
Finds the cutoff point for the barcode count filtering based on the barcode count distribution.
bc_auto_cutoff(barcodeObj, useCleanBc = TRUE) ## S4 method for signature 'BarcodeObj' bc_auto_cutoff(barcodeObj, useCleanBc = TRUE)bc_auto_cutoff(barcodeObj, useCleanBc = TRUE) ## S4 method for signature 'BarcodeObj' bc_auto_cutoff(barcodeObj, useCleanBc = TRUE)
barcodeObj |
A |
useCleanBc |
A logical value, if |
The one dimension kmeans clustering is applied to identify the "true barcode" based on the read count. The algorithm detail is: 1. Remove the barcodes with counts below the median of counts. 2. Transform the count by log2(x+1). 3. Apply the 1-dimension clustering to the log count, with the cluster number of 2 and weights of the log count. 4. Choose the minimum count value in the cluster with more counts as cutoff point.
For more info about 1 dimension kmeans used here please refer to
Ckmeans.1d.dp.
a numeric vector of the cutoff point.
data(bc_obj) bc_auto_cutoff(bc_obj)data(bc_obj) bc_auto_cutoff(bc_obj)
bc_barcodes used to get the barcode sequences in BarcodeObj
object. The input
BarcodesObj object should be pre-processed by bc_cure_*
functions, such as bc_cure_depth, bc_cure_umi.
bc_barcodes(barcodeObj, unlist = TRUE) ## S4 method for signature 'BarcodeObj' bc_barcodes(barcodeObj, unlist = TRUE)bc_barcodes(barcodeObj, unlist = TRUE) ## S4 method for signature 'BarcodeObj' bc_barcodes(barcodeObj, unlist = TRUE)
barcodeObj |
A |
unlist |
A logical value. If TRUE, the function returns a vector of unique barcode list from all samples; otherwise a list will be returned. In the latter case, each element of the list contains the barcodes of a sample. |
A character vector or a list.
data(bc_obj) # Get unique barcodes vector of all samples bc_barcodes(bc_obj) # Get a list with each element containing barcodes from one sample bc_barcodes(bc_obj, unlist = FALSE) ###data(bc_obj) # Get unique barcodes vector of all samples bc_barcodes(bc_obj) # Get a list with each element containing barcodes from one sample bc_barcodes(bc_obj, unlist = FALSE) ###
cleanBc slot of BarcodeObj object contains the processed barcode reads
frequency data. For more detail about the cleanBc slot, see
BarcodeObj. bc_cleanBc is used to access
the 'cleanBc' slot in the BarcodeObj.
bc_cleanBc(barcodeObj, isList = TRUE) ## S4 method for signature 'BarcodeObj' bc_cleanBc(barcodeObj, isList = TRUE)bc_cleanBc(barcodeObj, isList = TRUE) ## S4 method for signature 'BarcodeObj' bc_cleanBc(barcodeObj, isList = TRUE)
barcodeObj |
A |
isList |
A logical value, if TRUE (default), the return is a list with each sample
as an element. Otherwise, the function will return a |
If a list is requested, each list element is a data.frame
for each sample. In a data.frame, there are 2 columns 1.
barcode_seq: barcode sequence 2. counts: reads count, or UMI
count if the cleanBc was created by bc_cure_umi.
If a data.frame is requested, the data.frame in the list
described above are combined into one data.frame by row, with an extra
column named sample_name for identifying sample.
data(bc_obj) # Get the data in cleanBc slot # default the return value is a list bc_cleanBc(bc_obj) # The return value can be a data.frame bc_cleanBc(bc_obj, isList=FALSE) ###data(bc_obj) # Get the data in cleanBc slot # default the return value is a list bc_cleanBc(bc_obj) # The return value can be a data.frame bc_cleanBc(bc_obj, isList=FALSE) ###
Create a BarcodeObj object from extracted barcodes data
bc_create_BarcodeObj(x, sample_name = NULL, metadata = NULL, ordered = TRUE) ## S4 method for signature 'matrix' bc_create_BarcodeObj(x, sample_name = NULL, metadata = NULL) ## S4 method for signature 'data.frame' bc_create_BarcodeObj(x, sample_name = NULL, metadata = NULL)bc_create_BarcodeObj(x, sample_name = NULL, metadata = NULL, ordered = TRUE) ## S4 method for signature 'matrix' bc_create_BarcodeObj(x, sample_name = NULL, metadata = NULL) ## S4 method for signature 'data.frame' bc_create_BarcodeObj(x, sample_name = NULL, metadata = NULL)
x |
The barcodes data, it can be matrix, data.frame with each row as a barcode each column as a sample. The row names should be given as the barcode sequences, and the column names can be given as the sample names. |
sample_name |
A character vector, optional, specifying the sample name. |
metadata |
A data.frame, optional, specifying the metadata of each sample. The row names of the metadata should be the same as the sample names. |
ordered |
A logical value. If the value is true, the return barcodes (UMI-barcode tags) are sorted by the read counts. |
A BarcodeObj object.
data(bc_obj) m = bc_2matrix(bc_obj) bc_create_BarcodeObj(m)data(bc_obj) m = bc_2matrix(bc_obj) bc_create_BarcodeObj(m)
bc_cure_cluster performs clustering of barcodes by editing distance,
and remove the minority barcodes with a similar sequence. This function is only
applicable for the BarcodeObj object with a cleanBc slot. The barcodes
with a smaller reads count will be removed.
bc_cure_cluster( barcodeObj, dist_threshold = 1, depth_fold_threshold = 1, dist_method = "hamm", cluster_method = "greedy", count_threshold = 1e+09, dist_costs = list(replace = 1, insert = 1, delete = 1) ) ## S4 method for signature 'BarcodeObj' bc_cure_cluster( barcodeObj, dist_threshold = 1, depth_fold_threshold = 1, dist_method = "hamm", cluster_method = "greedy", count_threshold = 1e+07, dist_costs = list(replace = 1, insert = 1, delete = 1) )bc_cure_cluster( barcodeObj, dist_threshold = 1, depth_fold_threshold = 1, dist_method = "hamm", cluster_method = "greedy", count_threshold = 1e+09, dist_costs = list(replace = 1, insert = 1, delete = 1) ) ## S4 method for signature 'BarcodeObj' bc_cure_cluster( barcodeObj, dist_threshold = 1, depth_fold_threshold = 1, dist_method = "hamm", cluster_method = "greedy", count_threshold = 1e+07, dist_costs = list(replace = 1, insert = 1, delete = 1) )
barcodeObj |
A BarcodeObj object. |
dist_threshold |
A single integer, or vector of integers with the length of
sample number, specifying the editing distance threshold for defining two
similar barcode sequences. If the input is a vector, each value in the vector
relates to one sample according to its order in |
depth_fold_threshold |
A single numeric or vector of numeric with the
length of sample number, specifying the depth fold change threshold of
removing the similar minority barcode. The majority of barcodes should have at
least |
dist_method |
A character string, specifying the editing distance used for evaluating barcode similarity. It can be "hamm" for Hamming distance or "leven" for Levenshtein distance. |
cluster_method |
A character string specifying the algorithm used to perform the clustering of barcodes. Currently only "greedy" is available, in this case, The most and the least abundant barcode will be used for comparing, the least abundant barcode is preferentially removed. |
count_threshold |
An integer, read depth threshold to consider a barcode as a true barcode. If a barcode with a count higher than this threshold it will not be removed, even if the barcode is similar to a more abundant one. Default is 1e9. |
dist_costs |
A list, the cost of the events of distance algorithm,
applicable when Levenshtein distance is applied. The
names of vector have to be |
A BarcodeObj object with cleanBc slot updated.
data(bc_obj) d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_obj <- bc_extract(list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) # Remove barcodes with depth < 5 (bc_cured <- bc_cure_depth(bc_obj, depth=5)) # Do the clustering, remove the less abundant barcodes # one by hamming distance <= 1 bc_cure_cluster(bc_cured, dist_threshold = 1) # Levenshtein distance <= 1 bc_cure_cluster(bc_cured, dist_threshold = 2, dist_method = "leven", dist_costs = list("insert" = 2, "replace" = 1, "delete" = 2)) ###data(bc_obj) d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_obj <- bc_extract(list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) # Remove barcodes with depth < 5 (bc_cured <- bc_cure_depth(bc_obj, depth=5)) # Do the clustering, remove the less abundant barcodes # one by hamming distance <= 1 bc_cure_cluster(bc_cured, dist_threshold = 1) # Levenshtein distance <= 1 bc_cure_cluster(bc_cured, dist_threshold = 2, dist_method = "leven", dist_costs = list("insert" = 2, "replace" = 1, "delete" = 2)) ###
bc_cure_depth filters barcodes by the read counts or the UMI counts.
bc_cure_depth(barcodeObj, depth = 0, isUpdate = TRUE) ## S4 method for signature 'BarcodeObj' bc_cure_depth(barcodeObj, depth = 0, isUpdate = TRUE)bc_cure_depth(barcodeObj, depth = 0, isUpdate = TRUE) ## S4 method for signature 'BarcodeObj' bc_cure_depth(barcodeObj, depth = 0, isUpdate = TRUE)
barcodeObj |
A BarcodeObj object. |
depth |
A numeric or a vector of numeric, specifying the threshold of
minimum count for a barcode to keep. If the input is a vector and the vector
length is not the same as the sample number, the element will be repeatedly
used. And when the depth argument is a number with a negative value, automatic
cutoff point will be chosen by |
isUpdate |
A logical value. If TRUE, the |
A BarcodeObj object with cleanBc slot updated or
created.
data(bc_obj) d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_obj <- bc_extract(list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) # Remove barcodes with depth < 5 (bc_cured <- bc_cure_depth(bc_obj, depth=5)) bc_2matrix(bc_cured) # Use UMI information, filter the barcode < 5 UMI bc_umi_cured <- bc_cure_umi(bc_obj, depth =0, doFish=TRUE, isUniqueUMI=TRUE) bc_cure_depth(bc_umi_cured, depth = 5) ###data(bc_obj) d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_obj <- bc_extract(list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) # Remove barcodes with depth < 5 (bc_cured <- bc_cure_depth(bc_obj, depth=5)) bc_2matrix(bc_cured) # Use UMI information, filter the barcode < 5 UMI bc_umi_cured <- bc_cure_umi(bc_obj, depth =0, doFish=TRUE, isUniqueUMI=TRUE) bc_cure_depth(bc_umi_cured, depth = 5) ###
When the UMI is applied, bc_cure_umi can filter the UMI-barcode tags
by counts.
bc_cure_umi(barcodeObj, depth = 2, doFish = FALSE, isUniqueUMI = FALSE) ## S4 method for signature 'BarcodeObj' bc_cure_umi(barcodeObj, depth = 1, doFish = FALSE, isUniqueUMI = FALSE)bc_cure_umi(barcodeObj, depth = 2, doFish = FALSE, isUniqueUMI = FALSE) ## S4 method for signature 'BarcodeObj' bc_cure_umi(barcodeObj, depth = 1, doFish = FALSE, isUniqueUMI = FALSE)
barcodeObj |
A BarcodeObj object. |
depth |
A numeric or a vector of numeric, specifying the UMI-barcode tag count threshold. Only the barcodes with UMI-barcode tag count equal to or larger than the threshold are kept. |
doFish |
A logical value, if true, for barcodes with UMI read depth
above the threshold, “fish” for identical barcodes with UMI read depth below
the threshold. The consequence of |
isUniqueUMI |
A logical value. In the case that a UMI relates to several barcodes, if you believe that the UMI is absolutely unique, then only the UMI-barcodes tags with the highest count are kept for each UMI. |
When invoking this function, it processes the data with following steps:
(if isUniqueUMI is TRUE) Find the dominant UMI-barcode tag with the highest reads count in each UMI.
UMI-barcode depth filtering.
(if doFish is TRUE) Fishing the UMI-barcode tags with low reads count.
A BarcodeObj object with cleanBc slot updated (or
created).
data(bc_obj) d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_obj <- bc_extract(list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) # Use UMI information to remove the barcode <= 5 UMI-barcode tags bc_umi_cured <- bc_cure_umi(bc_obj, depth =0, doFish=TRUE, isUniqueUMI=TRUE) bc_cure_depth(bc_umi_cured, depth = 5)data(bc_obj) d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_obj <- bc_extract(list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) # Use UMI information to remove the barcode <= 5 UMI-barcode tags bc_umi_cured <- bc_cure_umi(bc_obj, depth =0, doFish=TRUE, isUniqueUMI=TRUE) bc_cure_depth(bc_umi_cured, depth = 5)
bc_extract identifies the barcodes (and UMI) from the sequences using
regular expressions. pattern and pattern_type arguments are
necessary, which provides the barcode (and UMI) pattern and their location
within the sequences.
bc_extract( x, pattern = "", sample_name = NULL, metadata = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'data.frame' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'ShortReadQ' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'DNAStringSet' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'integer' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'character' bc_extract( x, pattern = "", sample_name = NULL, metadata = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'list' bc_extract( x, pattern = "", sample_name = NULL, metadata = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE )bc_extract( x, pattern = "", sample_name = NULL, metadata = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'data.frame' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'ShortReadQ' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'DNAStringSet' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'integer' bc_extract( x, pattern = "", sample_name = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'character' bc_extract( x, pattern = "", sample_name = NULL, metadata = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE ) ## S4 method for signature 'list' bc_extract( x, pattern = "", sample_name = NULL, metadata = NULL, maxLDist = 0, pattern_type = c(barcode = 1), costs = list(sub = 1, ins = 99, del = 99), ordered = TRUE )
x |
A single or a list of fastq files, ShortReadQ, DNAStringSet, data.frame, or named integer. |
pattern |
A string or a string vector with the same number of files, specifying the regular expression with capture. It matches the barcode (and UMI) with capture pattern. |
sample_name |
A string vector, applicable when |
metadata |
A |
maxLDist |
An integer. The minimum mismatch threshold for barcode
matching, when maxLDist is 0, the |
pattern_type |
A vector. It defines the barcode (and UMI) capture group. See Details. |
costs |
A named list, applicable when maxLDist > 0, specifying the
weight of each mismatch event while extracting the barcodes. The list
element name have to be |
ordered |
A logical value. If the value is true, the return barcodes (UMI-barcode tags) are sorted by the read counts. |
The pattern argument is a regular expression, the capture operation
() identifying the barcode or UMI. pattern_type argument
annotates capture, denoting the UMI or the barcode captured pattern. In the
example:
([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC
|---------| starts with 3 base pairs UMI.
|----------| constant sequence in the backbone.
|-------| flexible barcode sequences.
|---------| 3' constant sequence.
In UMI part [ACGT]{3}, [ACGT] means it can be one of
the "A", "C", "G" and "T", and {3} means it repeats 3 times.
In the barcode pattern [ACGT]+, the + denotes
that there is at least one of the A or C or G or
T.
This function returns a BarcodeObj object if the input is a list or a
vector of Fastq files, otherwise it returns a data.frame. In
the later case
the data.frame has columns:
umi_seq (optional): UMI sequence, applicable when there is UMI
in 'pattern' and 'pattern_type' argument.
barcode_seq: barcode sequence.
count: reads number.
fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") library(ShortRead) # barcode from fastq file bc_extract(fq_file, pattern = "AAAAA(.*)CCCCC") # barcode from ShortReadQ object sr <- readFastq(fq_file) # ShortReadQ bc_extract(sr, pattern = "AAAAA(.*)CCCCC") # barcode from DNAStringSet object ds <- sread(sr) # DNAStringSet bc_extract(ds, pattern = "AAAAA(.*)CCCCC") # barcode from integer vector iv <- tables(ds, n = Inf)$top # integer vector bc_extract(iv, pattern = "AAAAA(.*)CCCCC") # barcode from data.frame df <- data.frame(seq = names(iv), freq = as.integer(iv)) # data.frame bc_extract(df, pattern = "AAAAA(.*)CCCCC") # barcode from list of DNAStringSet l <- list(sample1 = ds, sample2 = ds) # list bc_extract(l, pattern = "AAAAA(.*)CCCCC") # Extract UMI and barcode d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) # barcode backbone with UMI and barcode pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_extract( list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) ###fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") library(ShortRead) # barcode from fastq file bc_extract(fq_file, pattern = "AAAAA(.*)CCCCC") # barcode from ShortReadQ object sr <- readFastq(fq_file) # ShortReadQ bc_extract(sr, pattern = "AAAAA(.*)CCCCC") # barcode from DNAStringSet object ds <- sread(sr) # DNAStringSet bc_extract(ds, pattern = "AAAAA(.*)CCCCC") # barcode from integer vector iv <- tables(ds, n = Inf)$top # integer vector bc_extract(iv, pattern = "AAAAA(.*)CCCCC") # barcode from data.frame df <- data.frame(seq = names(iv), freq = as.integer(iv)) # data.frame bc_extract(df, pattern = "AAAAA(.*)CCCCC") # barcode from list of DNAStringSet l <- list(sample1 = ds, sample2 = ds) # list bc_extract(l, pattern = "AAAAA(.*)CCCCC") # Extract UMI and barcode d1 <- data.frame( seq = c( "ACTTCGATCGATCGAAAAGATCGATCGATC", "AATTCGATCGATCGAAGAGATCGATCGATC", "CCTTCGATCGATCGAAGAAGATCGATCGATC", "TTTTCGATCGATCGAAAAGATCGATCGATC", "AAATCGATCGATCGAAGAGATCGATCGATC", "CCCTCGATCGATCGAAGAAGATCGATCGATC", "GGGTCGATCGATCGAAAAGATCGATCGATC", "GGATCGATCGATCGAAGAGATCGATCGATC", "ACTTCGATCGATCGAACAAGATCGATCGATC", "GGTTCGATCGATCGACGAGATCGATCGATC", "GCGTCCATCGATCGAAGAAGATCGATCGATC" ), freq = c( 30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6 ) ) # barcode backbone with UMI and barcode pattern <- "([ACTG]{3})TCGATCGATCGA([ACTG]+)ATCGATCGATC" bc_extract( list(test = d1), pattern, sample_name=c("test"), pattern_type=c(UMI=1, barcode=2)) ###
bc_extract_10X_fastq can extract cellular barcode, UMI, and lineage barcode
sequences from 10X Genomics scRNASeq fastq file. This function can process
the barcodes in the scRNASeq fastq file or target amplified fastq files directly.
bc_extract_sc_fastq( fq1, fq2 = NULL, patternCellBarcode = NULL, patternUMI = NULL, patternBarcode = NULL )bc_extract_sc_fastq( fq1, fq2 = NULL, patternCellBarcode = NULL, patternUMI = NULL, patternBarcode = NULL )
fq1 |
A string, the fastq file contains the cellular barcode and lineage barcode |
fq2 |
A string, it is optional, it provides the second fastq file contains the cellular barcode and lineage barcode. Two fastq files will be concatenated for the barcode extraction |
patternCellBarcode |
A string, defines the regular expression to match
the single cell cellular barcode sequence. The expected sequence should be in
the first catch. Please see the documents of
|
patternUMI |
A string, defines the regular expression to match the UMI
sequence. The expected sequence should be in the first catch. Please see the
documents of |
patternBarcode |
the regular expression to match the lineage barcode. The
expected sequence should be in the first catch. Please see the documents of
|
It should take some effort to define the regular expression to match the barcode sequence. Here I also provide the example to extract the barcode from 10X Genomics scRNASeq results. It also can be used to extract the barcode from other system.
The function can process the barcodes in the scRNASeq fastq file or target amplified fastq files. For the 10X scRNASeq fastq file, the cellular barcode is in the first 16bp of the read1, the UMI is in the next 12bp, and the lineage barcode is in the read2.
The usage of the function will be like this:
bc_extract_sc_fastq(
fq1 = "read1.fastq.gz",
fq2 = "read2.fastq.gz",
patternCellBarcode = "(.{16})",
patternUMI = ".{16}(.{12})",
patternBarcode = "CGAAGTATCAAG(.+)CCGTAGCAAG"
)
A BarcodeObj object with each cell as a sample.
bc_extract, bc_extract_sc_sam,
bc_extract_sc_sam can extract cellular barcode, UMI, and lineage
barcode sequences from 10X Genomics scRNASeq sam file (or bam file have
similar data structure). This function can not process bam file directly,
users need to uncompress the bam file to get a sam file to run this
function See example.
bc_extract_sc_sam(sam, pattern, cell_barcode_tag = "CR", umi_tag = "UR") bc_extract_sc_bam(bam, pattern, cell_barcode_tag = "CR", umi_tag = "UR")bc_extract_sc_sam(sam, pattern, cell_barcode_tag = "CR", umi_tag = "UR") bc_extract_sc_bam(bam, pattern, cell_barcode_tag = "CR", umi_tag = "UR")
sam |
A string, define the un-mapped sequences |
pattern |
A string, define the regular expression to match the barcode
sequence. The barcode sequence should be in the first catch. Please see the
documents of |
cell_barcode_tag |
A string, define the tag of cellular barcode field in sam file. The default is "CR". |
umi_tag |
A string, define the tag of a UMI field in the sam file. |
bam |
A string, define the bam file, it will be converted to sam file |
Although the function 'bc_extract_sc_bam' can process bam file directly, some optimization is still working on, it will be much more efficient to use 'samtools' to get the sam file.
What's more, if the barcode sequence does not map to the reference genome. The user should use the samtools to get the un-mapped reads and save it as sam format for using as the input. It can save a lot of time. The way to get the un-mapped reads:
samtools view -f 4 input.bam > output.sam
A BarcodeObj object with each cell as a sample.
bc_extract,
bc_extract_sc_fastq
## NOT run # In the case that when the barcode sequence is not mapped to # reference genome, it will be much more efficient to get # the un-mapped sequences as the input. ## Get un-mapped reads # samtools view -f 4 input.bam > scRNASeq_10X.sam sam_file <- system.file("extdata", "scRNASeq_10X.sam", package = "CellBarcode") bc_extract_sc_sam( sam = sam_file, pattern = "AGATCAG(.*)TGTGGTA", cell_barcode_tag = "CR", umi_tag = "UR" ) ## Read bam file directly bam_file <- system.file("extdata", "scRNASeq_10X.bam", package = "CellBarcode") bc_extract_sc_bam( bam = bam_file, pattern = "AGATCAG(.*)TGTGGTA", cell_barcode_tag = "CR", umi_tag = "UR" )## NOT run # In the case that when the barcode sequence is not mapped to # reference genome, it will be much more efficient to get # the un-mapped sequences as the input. ## Get un-mapped reads # samtools view -f 4 input.bam > scRNASeq_10X.sam sam_file <- system.file("extdata", "scRNASeq_10X.sam", package = "CellBarcode") bc_extract_sc_sam( sam = sam_file, pattern = "AGATCAG(.*)TGTGGTA", cell_barcode_tag = "CR", umi_tag = "UR" ) ## Read bam file directly bam_file <- system.file("extdata", "scRNASeq_10X.bam", package = "CellBarcode") bc_extract_sc_bam( bam = bam_file, pattern = "AGATCAG(.*)TGTGGTA", cell_barcode_tag = "CR", umi_tag = "UR" )
messyBc slot of BarcodeObj object contains the raw barcode reads
frequency data. For more detail about the messyBc slot, see
BarcodeObj. bc_messyBc is used to access
the 'messyBc' slot in the BarcodeObj.
bc_messyBc(barcodeObj, isList = TRUE) ## S4 method for signature 'BarcodeObj' bc_messyBc(barcodeObj, isList = TRUE)bc_messyBc(barcodeObj, isList = TRUE) ## S4 method for signature 'BarcodeObj' bc_messyBc(barcodeObj, isList = TRUE)
barcodeObj |
A |
isList |
A logical value, if TRUE (default), the return is a list with each
sample as an element. Otherwise, the function will return a |
If a list is requested, in the list each element is a
data.frame corresponding to the successive samples. Each
data.frame has at most 3 columns: 1. umi_seq (optional): UMI
sequence. 2. barcode_seq: barcode sequence. 3. count: how many
reads a full sequence has.
If a data.frame is requested, the data.frame in the list
described above are combined into one data.frame by row, with an extra
column named sample_name for identifying sample.
data(bc_obj) # get the data in messyBc slot # default the return value is a list bc_messyBc(bc_obj) # The return value can be a data.frame bc_messyBc(bc_obj, isList=FALSE) ###data(bc_obj) # get the data in messyBc slot # default the return value is a list bc_messyBc(bc_obj) # The return value can be a data.frame bc_messyBc(bc_obj, isList=FALSE) ###
Sample information is kept in metadata. bc_meta is for accessing and
updating metadata in BarcodeObj object
bc_meta(barcodeObj) bc_meta(barcodeObj, key = NULL) <- value ## S4 method for signature 'BarcodeObj' bc_meta(barcodeObj) ## S4 replacement method for signature 'BarcodeObj' bc_meta(barcodeObj, key = NULL) <- valuebc_meta(barcodeObj) bc_meta(barcodeObj, key = NULL) <- value ## S4 method for signature 'BarcodeObj' bc_meta(barcodeObj) ## S4 replacement method for signature 'BarcodeObj' bc_meta(barcodeObj, key = NULL) <- value
barcodeObj |
A |
key |
A string, identifying the metadata record name to be modified. |
value |
A string vector or a data.frame. If the |
A data.frame
data(bc_obj) # get the metadata data.frame bc_meta(bc_obj) # assign value to metadata by $ operation bc_meta(bc_obj)$phenotype <- c("l", "b") # assign value to metadata by "key" argument bc_meta(bc_obj, key = "sample_type") <- c("l", "b") # show the updated metadata bc_meta(bc_obj) # assign new data.frame to metadata metadata <- data.frame( sample_name <- c("test1", "test2"), phenotype <- c("l", "b") ) rownames(metadata) = bc_names(bc_obj) bc_meta(bc_obj) <- metadata ###data(bc_obj) # get the metadata data.frame bc_meta(bc_obj) # assign value to metadata by $ operation bc_meta(bc_obj)$phenotype <- c("l", "b") # assign value to metadata by "key" argument bc_meta(bc_obj, key = "sample_type") <- c("l", "b") # show the updated metadata bc_meta(bc_obj) # assign new data.frame to metadata metadata <- data.frame( sample_name <- c("test1", "test2"), phenotype <- c("l", "b") ) rownames(metadata) = bc_names(bc_obj) bc_meta(bc_obj) <- metadata ###
Get or update sample names in BarcodeObj object and BarcodeQcSet.
bc_names(x) bc_names(x) <- value ## S4 method for signature 'BarcodeObj' bc_names(x) ## S4 replacement method for signature 'BarcodeObj,character' bc_names(x) <- value ## S4 method for signature 'BarcodeQcSet' bc_names(x) ## S4 replacement method for signature 'BarcodeQcSet,ANY' bc_names(x) <- valuebc_names(x) bc_names(x) <- value ## S4 method for signature 'BarcodeObj' bc_names(x) ## S4 replacement method for signature 'BarcodeObj,character' bc_names(x) <- value ## S4 method for signature 'BarcodeQcSet' bc_names(x) ## S4 replacement method for signature 'BarcodeQcSet,ANY' bc_names(x) <- value
x |
A |
value |
A character vector setting the new sample names, with the length
of the samples number in |
A character vector
data(bc_obj) bc_names(bc_obj) bc_names(bc_obj) <- c("new1", "new2")data(bc_obj) bc_names(bc_obj) bc_names(bc_obj) <- c("new1", "new2")
Dataset contains a BarcodeObj with makeup barcode data.
data(bc_obj)data(bc_obj)
This is a BarcodeObj object
A BarcodeObj object.
This is a BarcodeObj object derived from makeup data by:
d1 = data.frame(
seq = c(
"ACTTCGATCGATCGAAAAGATCGATCGATC",
"AATTCGATCGATCGAAGAGATCGATCGATC",
"CCTTCGATCGATCGAAGAAGATCGATCGATC",
"TTTTCGATCGATCGAAAAGATCGATCGATC",
"AAATCGATCGATCGAAGAGATCGATCGATC",
"CCCTCGATCGATCGAAGAAGATCGATCGATC",
"GGGTCGATCGATCGAAAAGATCGATCGATC",
"GGATCGATCGATCGAAGAGATCGATCGATC",
"ACTTCGATCGATCGAACAAGATCGATCGATC",
"GGTTCGATCGATCGACGAGATCGATCGATC",
"GCGTCCATCGATCGAAGAAGATCGATCGATC"
),
freq = c(
30, 60, 9, 10, 14, 5, 10, 30, 6, 4 , 6
)
)
d2 = data.frame(
seq = c(
"ACTTCGATCGATCGAAACGATCGATCGATC",
"AATTCGATCGATCGAAGAGATCGATCGATC",
"TTTTCGATCGATCGAAAAGATCGATCGATC",
"AAATCGATCGATCGAAGAGATCGATCGATC",
"CCCTCGATCGATCGAAGAAGATCGATCGATC",
"GGGTCGATCGATCGAAAAGATCGATCGATC",
"GGATCGATCGATCGAAGAGATCGATCGATC",
"ACTTCGATCGATCGAACAAGATCGATCGATC",
"GGTTCGATCGATCGACGAGATCGATCGATC",
"GCGTCCATCGATCGAAGAAGATCGATCGATC"
),
freq = c(
30, 9, 10, 14, 5, 10, 30, 6, 4 , 6
)
)
pattern = "TCGATCGATCGA([ACTG]+)ATCGATCGATC"
bc_obj = bc_extract(
list(test1 = d1, test2 = d2),
pattern, sample_name=c("test1", "test2"))
bc_obj = bc_cure_depth(bc_obj, depth=5)
# Save the dummy data
# save(bc_obj, file = "./data/bc_obj.RData")
###
This function is used to summarize the counts of each barcode.
bc_plot_count(barcodeObj, bins = 20, useCleaned = TRUE) ## S4 method for signature 'BarcodeObj' bc_plot_count(barcodeObj, bins = 20, useCleaned = TRUE)bc_plot_count(barcodeObj, bins = 20, useCleaned = TRUE) ## S4 method for signature 'BarcodeObj' bc_plot_count(barcodeObj, bins = 20, useCleaned = TRUE)
barcodeObj |
A BarcodeObj object |
bins |
The number of bins for the histogram |
useCleaned |
Whether to use the cleaned barcode data |
When useCleaned is TRUE, the cleaned barcode data will be used. Otherwise, the messy barcode data will be used. The output will be different when useCleaned is TRUE or FALSE. It also depends on whether the UMI is available. The counts include:
reads count (with barcode) versus the total reads
reads count per UMI
UMI count per barcode
barcode count per sample
reads or UMI count (dominant barcode) versus total count per sample
reads or UMI count (dominant barcode) distribution
A egg::ggarrange object
data(bc_obj) bc_plot_count(barcodeObj=bc_obj)data(bc_obj) bc_plot_count(barcodeObj=bc_obj)
Draw barcode count scatter plot for all pairwise combinations of samples
within a BarcodeObj object. It uses cleanBc slot in the
BarcodeObj object is used to draw the figure. If the BarcodeObj
object does not have a cleanBc slot, you have to run the bc_cure*
functions in ahead, such as bc_cure_depth,
bc_cure_umi.
bc_plot_mutual( barcodeObj, count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 ) ## S4 method for signature 'BarcodeObj' bc_plot_mutual( barcodeObj, count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 )bc_plot_mutual( barcodeObj, count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 ) ## S4 method for signature 'BarcodeObj' bc_plot_mutual( barcodeObj, count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 )
barcodeObj |
A |
count_marks |
A numeric or numeric vector, specifying the read count cutoff in the scatter plot for each sample. |
highlight |
A character vector, specifying the barcodes to be highlighted. |
log_coord |
A logical value, if TRUE (default), the |
alpha |
A numeric between 0 and 1, specifies the transparency of the dots in the scatter plot. |
A scatter plot matrix.
data(bc_obj) bc_plot_mutual(barcodeObj=bc_obj, count_marks=c(30, 20)) ###data(bc_obj) bc_plot_mutual(barcodeObj=bc_obj, count_marks=c(30, 20)) ###
Draws scatter plot for barcode read count between given pairs of samples with
a BarcodeObj object. This function will return a scatter plot matrix
contains the scatter plots for all given sample pairs.
bc_plot_pair( barcodeObj, sample_x, sample_y, count_marks_x = NULL, count_marks_y = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 ) ## S4 method for signature 'BarcodeObj' bc_plot_pair( barcodeObj, sample_x, sample_y, count_marks_x = NULL, count_marks_y = count_marks_x, highlight = NULL, log_coord = TRUE, alpha = 0.7 )bc_plot_pair( barcodeObj, sample_x, sample_y, count_marks_x = NULL, count_marks_y = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 ) ## S4 method for signature 'BarcodeObj' bc_plot_pair( barcodeObj, sample_x, sample_y, count_marks_x = NULL, count_marks_y = count_marks_x, highlight = NULL, log_coord = TRUE, alpha = 0.7 )
barcodeObj |
A |
sample_x |
A character vector or a integer vector, specifying the sample
in |
sample_y |
A character vector or a integer vector, similar to
|
count_marks_x |
A numeric vector used to mark the cutoff point for samples in x axis |
count_marks_y |
A number vector used to mark the cutoff point for samples in the y-axis. |
highlight |
A character vector, specifying the barcodes that need to be highlighted. |
log_coord |
A logical value, if TRUE (default), the |
alpha |
A numeric between 0 and 1, specifies the transparency of the dots in the scatter plot. |
Scatter plot matrix.
data(bc_obj) bc_names(bc_obj) bc_plot_pair(barcodeObj=bc_obj, sample_x="test1", sample_y="test2", count_marks_x=30, count_marks_y=20) ###data(bc_obj) bc_names(bc_obj) bc_plot_pair(barcodeObj=bc_obj, sample_x="test1", sample_y="test2", count_marks_x=30, count_marks_y=20) ###
Draws barcode count distribution for each sample in a BarcodeObj object.
bc_plot_single( barcodeObj, sample_names = NULL, count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 ) ## S4 method for signature 'BarcodeObj' bc_plot_single( barcodeObj, sample_names = bc_names(barcodeObj), count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 )bc_plot_single( barcodeObj, sample_names = NULL, count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 ) ## S4 method for signature 'BarcodeObj' bc_plot_single( barcodeObj, sample_names = bc_names(barcodeObj), count_marks = NULL, highlight = NULL, log_coord = TRUE, alpha = 0.7 )
barcodeObj |
A |
sample_names |
A character vector or integer vector, specifying the samples used for the plot. |
count_marks |
A numeric or numeric vector, specifying the read count cutoff in the scatter plot for each sample. |
highlight |
A character vector, specifying the barcodes that need to be highlighted. |
log_coord |
A logical value, if TRUE (default), the |
alpha |
A numeric between 0 and 1, specifies the transparency of the dots in the scatter plot. |
1D distribution graph matrix.
data(bc_obj) bc_plot_single(bc_obj, count_marks=c(10, 11)) ###data(bc_obj) bc_plot_single(bc_obj, count_marks=c(10, 11)) ###
Remove low-quality sequences by base-pair quality, sequence length or unknown base "N".
bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0, sample_name = "" ) ## S4 method for signature 'ShortReadQ' bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0 ) ## S4 method for signature 'DNAStringSet' bc_seq_filter(x, min_read_length = 0, N_threshold = 0) ## S4 method for signature 'data.frame' bc_seq_filter(x, min_read_length = 0, N_threshold = 0) ## S4 method for signature 'character' bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0, sample_name = basename(x) ) ## S4 method for signature 'integer' bc_seq_filter(x, min_read_length = 0, N_threshold = 0) ## S4 method for signature 'list' bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0, sample_name = names(x) )bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0, sample_name = "" ) ## S4 method for signature 'ShortReadQ' bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0 ) ## S4 method for signature 'DNAStringSet' bc_seq_filter(x, min_read_length = 0, N_threshold = 0) ## S4 method for signature 'data.frame' bc_seq_filter(x, min_read_length = 0, N_threshold = 0) ## S4 method for signature 'character' bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0, sample_name = basename(x) ) ## S4 method for signature 'integer' bc_seq_filter(x, min_read_length = 0, N_threshold = 0) ## S4 method for signature 'list' bc_seq_filter( x, min_average_quality = 30, min_read_length = 0, N_threshold = 0, sample_name = names(x) )
x |
A single or a list of Fastq file, |
min_average_quality |
A numeric or a vector of numeric, specifying the threshold of the minimum average base quality of a sequence to be kept. |
min_read_length |
A single or a vector of integer, specifying the sequence length threshold. |
N_threshold |
A integer or a vector of integer, specifying the maximum
|
sample_name |
A string vector, specifying the sample name in the output. |
A ShortReadQ or DNAStringSet object with sequences passed the filters.
library(ShortRead) fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") # apply a filter to fastq files bc_seq_filter(fq_file) # Read in fastq files to get ShortReadQ object sr <- readFastq(fq_file[1]) # apply sequencing quality filter to ShortReadQ bc_seq_filter(sr) # get DNAStringSet object ds <- sread(sr) # Apply sequencing quality filter to DNAStringSet bc_seq_filter(ds) ###library(ShortRead) fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") # apply a filter to fastq files bc_seq_filter(fq_file) # Read in fastq files to get ShortReadQ object sr <- readFastq(fq_file[1]) # apply sequencing quality filter to ShortReadQ bc_seq_filter(sr) # get DNAStringSet object ds <- sread(sr) # Apply sequencing quality filter to DNAStringSet bc_seq_filter(ds) ###
bc_seq_qc evaluates sequences quality. See the return value for detail.
bc_seq_qc(x, sample_name = NULL, reads_sample_size = 1e+05) bc_plot_seqQc(x) ## S4 method for signature 'ShortReadQ' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'DNAStringSet' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'data.frame' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'integer' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'character' bc_seq_qc(x, sample_name = basename(x), reads_sample_size = 1e+05) ## S4 method for signature 'list' bc_seq_qc(x, sample_name = names(x)) ## S4 method for signature 'BarcodeQc' bc_plot_seqQc(x) ## S4 method for signature 'BarcodeQcSet' bc_plot_seqQc(x)bc_seq_qc(x, sample_name = NULL, reads_sample_size = 1e+05) bc_plot_seqQc(x) ## S4 method for signature 'ShortReadQ' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'DNAStringSet' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'data.frame' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'integer' bc_seq_qc(x, reads_sample_size = 1e+05) ## S4 method for signature 'character' bc_seq_qc(x, sample_name = basename(x), reads_sample_size = 1e+05) ## S4 method for signature 'list' bc_seq_qc(x, sample_name = names(x)) ## S4 method for signature 'BarcodeQc' bc_plot_seqQc(x) ## S4 method for signature 'BarcodeQcSet' bc_plot_seqQc(x)
x |
A single or list of Fastq files, ShortReadQ object, DNAStringSet object, data.frame or named integer vector. |
sample_name |
A character vector with the length of sample number, used to set the sample name. |
reads_sample_size |
A integer value defines the sample size of the sequences for quality control analysis. If there are fewer sequences comparing to this value, all the sequences will be used. The default is 1e5. |
A barcodeQc or a barcodeQcSet class. The barcodeQc is a list with four slots,
top: a data.frame with top 50 most frequency sequence,
distribution: a data.frame with the distribution of
read depth. It contains nOccurrences (depth), and nReads
(unique sequence) columns.
base_quality_per_cycle: data.frame with base-pair
location (NGS sequencing cycle) by row, and the base-pair quality summary
by column, including Mean, P5 (5
P75 (75
base_freq_per_cycle: data.frame with three columns: 1.
Cycle, the sequence base-pair location (NGS sequencing cycle); 2.
Base, DNA base;
Count: reads count.
summary: a numeric vector with following elements:
total_read, median_read_length,
p5_read_length, p95_read_length.
The barcodeQcSet is a list of barcodeQc.
library(ShortRead) # fastq file fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") bc_seq_qc(fq_file) # ShortReadQ sr <- readFastq(fq_file[1]) bc_seq_qc(sr) # DNAStringSet ds <- sread(sr) bc_seq_qc(ds) # List of DNAStringSet l <- list(sample1 = ds, sample2 = ds) bc_plot_seqQc(bc_seq_qc(l)) # List of ShortRead l_sr <- list(sample1 = sr, sample2 = sr) bc_plot_seqQc(bc_seq_qc(l_sr)) ###library(ShortRead) # fastq file fq_file <- system.file("extdata", "simple.fq", package="CellBarcode") bc_seq_qc(fq_file) # ShortReadQ sr <- readFastq(fq_file[1]) bc_seq_qc(sr) # DNAStringSet ds <- sread(sr) bc_seq_qc(ds) # List of DNAStringSet l <- list(sample1 = ds, sample2 = ds) bc_plot_seqQc(bc_seq_qc(l)) # List of ShortRead l_sr <- list(sample1 = sr, sample2 = sr) bc_plot_seqQc(bc_seq_qc(l_sr)) ###
Script to split barcodes from the genetic 'barcode mouse' construct as generated in the lab of Ton Schumacher (NKI, NL) in its remaining constant V, D and J elements and the modified elements (additions/deletions) in between those constant parts.
bc_splitVDJ( seqs, v_part = "TCCAGTAG", d_fwd = "TCTACTATCGTTACGAC", d_inv = "GTCGTAACGATAGTAGA", j_part = "GTAGCTACTACCG" )bc_splitVDJ( seqs, v_part = "TCCAGTAG", d_fwd = "TCTACTATCGTTACGAC", d_inv = "GTCGTAACGATAGTAGA", j_part = "GTAGCTACTACCG" )
seqs |
a character vector contains the barcode sequences. |
v_part |
a string given the V part sequence. |
d_fwd |
a string given the D region forwrad sequence. |
d_inv |
a string given the D region inverted sequence. |
j_part |
a string given the J region sequence. |
A list contains two data.frame named add.del.ok and add.del.err, which contain
columns with the remaining constant parts and inserted/deleted parts
## prepare input sequence seq_v <- c( "TCCAGTAGCTACTATCGTTACGAGTAGCTACTACCG", "TCCAGTAGCTACTATCGTTACGACGTAGCTACTACCG", "TCCATACTATCGTTACGACGTAGCTACTACG", "TCCAGTAGTCGTAACGATAGTAGAGTAGCTACTACCG" ) ## split the sequences bc_splitVDJ(seq_v)## prepare input sequence seq_v <- c( "TCCAGTAGCTACTATCGTTACGAGTAGCTACTACCG", "TCCAGTAGCTACTATCGTTACGACGTAGCTACTACCG", "TCCATACTATCGTTACGACGTAGCTACTACG", "TCCAGTAGTCGTAACGATAGTAGAGTAGCTACTACCG" ) ## split the sequences bc_splitVDJ(seq_v)
A set of functions and operators for subsetting or joining of
BarcodeObj object(s).
The bc_subset, * and - are used to select barcodes or
samples in a BarcodeObj object.
Two BarcodeObj objects can be joined by +.
bc_subset( barcodeObj, sample = NULL, barcode = NULL, black_list = NULL, is_sample_quoted_exp = FALSE ) bc_merge(barcodeObj_x, barcodeObj_y) ## S4 method for signature 'BarcodeObj' bc_subset( barcodeObj, sample = NULL, barcode = NULL, black_list = NULL, is_sample_quoted_exp = FALSE ) ## S4 method for signature 'BarcodeObj,BarcodeObj' bc_merge(barcodeObj_x, barcodeObj_y) ## S3 method for class 'BarcodeObj' barcodeObj_x + barcodeObj_y ## S3 method for class 'BarcodeObj' barcodeObj - black_list ## S3 method for class 'BarcodeObj' barcodeObj * white_listbc_subset( barcodeObj, sample = NULL, barcode = NULL, black_list = NULL, is_sample_quoted_exp = FALSE ) bc_merge(barcodeObj_x, barcodeObj_y) ## S4 method for signature 'BarcodeObj' bc_subset( barcodeObj, sample = NULL, barcode = NULL, black_list = NULL, is_sample_quoted_exp = FALSE ) ## S4 method for signature 'BarcodeObj,BarcodeObj' bc_merge(barcodeObj_x, barcodeObj_y) ## S3 method for class 'BarcodeObj' barcodeObj_x + barcodeObj_y ## S3 method for class 'BarcodeObj' barcodeObj - black_list ## S3 method for class 'BarcodeObj' barcodeObj * white_list
barcodeObj |
A BarcodeObj object. |
sample |
A character vector or integer vector or an expression
(expressio not applicable for |
barcode |
A vector of integer or string, indicating the selected barcode. |
black_list |
A character vector, specifying the black list with excluded barcodes. |
is_sample_quoted_exp |
A logical value. If TRUE, the expression in
|
barcodeObj_x |
A BarcodeObj object. |
barcodeObj_y |
A BarcodeObj object. |
white_list |
A character vector, giving the barcode white list. |
bc_subset and []: Gets samples and barcodes subset from a
BarcodeObj object.
+: Combines two BarcodeObj objects. The metadata,
cleanBc and
messyBc slot in the BarcodeObj objects will be joined.
For the metadata slot, the sample_name column, and the
Full outer join (the record in either BarcodeObj object) will be
performed with row names as the key.
The messyBc and cleanBc from two objects are combined by rows
for the same sample from two BarcodeObj objects.
-: removes barcodes in the black_list.
*: selects barcodes in the white_list.
A 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 metadata bc_meta(bc_obj)$phenotype <- c("l", "b") bc_meta(bc_obj) bc_subset(bc_obj, 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 barcode blacklist bc_subset( bc_obj, sample = c("test1", "test2"), barcode = c("AACCTT")) # Join two samples with different barcode sets bc_obj["AGAG", "test1"] + bc_obj["AAAG", "test2"] # Join two samples with overlap barcodes bc_obj_join <- bc_obj["AGAG", "test1"] + bc_obj["AGAG", "test2"] bc_obj_join # The same barcode will be removed after applying bc_cure_depth() bc_cure_depth(bc_obj_join) # Remove barcodes bc_obj bc_obj - "AAAG" # Select barcodes in a whitelist bc_obj bc_obj * "AAAG" ###data(bc_obj) bc_obj # Select barcodes bc_subset(bc_obj, barcode = c("AACCTT", "AACCTT")) bc_obj[c("AGAG", "AAAG"), ] # Select samples by metadata bc_meta(bc_obj)$phenotype <- c("l", "b") bc_meta(bc_obj) bc_subset(bc_obj, 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 barcode blacklist bc_subset( bc_obj, sample = c("test1", "test2"), barcode = c("AACCTT")) # Join two samples with different barcode sets bc_obj["AGAG", "test1"] + bc_obj["AAAG", "test2"] # Join two samples with overlap barcodes bc_obj_join <- bc_obj["AGAG", "test1"] + bc_obj["AGAG", "test2"] bc_obj_join # The same barcode will be removed after applying bc_cure_depth() bc_cure_depth(bc_obj_join) # Remove barcodes bc_obj bc_obj - "AAAG" # Select barcodes in a whitelist bc_obj bc_obj * "AAAG" ###
bc_summary_barcode evaluates sequence diversity metrics using the
barcodes data in the cleanBc slot of BarcodeObj object. It
also generates Lorenz curve and barcode frequency distribution graphs.
bc_summary_barcode(barcodeObj, plot = TRUE, log_x = TRUE) ## S4 method for signature 'BarcodeObj' bc_summary_barcode(barcodeObj, plot = TRUE, log_x = TRUE)bc_summary_barcode(barcodeObj, plot = TRUE, log_x = TRUE) ## S4 method for signature 'BarcodeObj' bc_summary_barcode(barcodeObj, plot = TRUE, log_x = TRUE)
barcodeObj |
A BarcodeObj object. |
plot |
A logical value, if TRUE, draw the Lorenz curve and barcode distribution graphs. |
log_x |
A logical value, if TRUE, the |
Followings are the metrics used for evaluating the barcode diversity:
Richness: The unique barcodes number , it evaluates the
richness of the barcodes.
Shannon index: Shannon diversity index is weighted geometric
average of the proportion of barcodes.
Equitability index: Shannon equitability characterize the
evenness of the barcodes, it is a value between 0 and 1, with 1 being
complete evenness.
Bit:
Shannon entropy , with a units of bit,
A data.frame with the following columns:
total_reads: total read number.
uniq_barcode: how many barcodes in the dataset.
shannon_index: Shannon's diversity index or Shannon–Wiener
index.
equitability_index: Shannon's equitability.
bit_index: Shannon bit information.
data(bc_obj) # filter barcode by the depth bc_obj <- bc_cure_depth(bc_obj) # Output the summary of the barcodes bc_summary_barcode(bc_obj)data(bc_obj) # filter barcode by the depth bc_obj <- bc_cure_depth(bc_obj) # Output the summary of the barcodes bc_summary_barcode(bc_obj)
Summary the "total read count" and "read length" of each samples within
a BarcodeQcSet object, and output a data.frame with sample by
row and different metrics by column.
bc_summary_seqQc(x) ## S4 method for signature 'BarcodeQcSet' bc_summary_seqQc(x)bc_summary_seqQc(x) ## S4 method for signature 'BarcodeQcSet' bc_summary_seqQc(x)
x |
a barcodeQcSet object. |
A data.frame with 5 columns: sample_name,
total_read, median_read_length, p5_read_length and
p95_read_length.
fq_file <- dir( system.file("extdata", "mef_test_data", package = "CellBarcode"), full=TRUE) bc_summary_seqQc(bc_seq_qc(fq_file)) ###fq_file <- dir( system.file("extdata", "mef_test_data", package = "CellBarcode"), full=TRUE) bc_summary_seqQc(bc_seq_qc(fq_file)) ###
The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. CellBarcode can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data.
Format the summary of BarcodeObj object for pretty print.
## S4 method for signature 'BarcodeObj' format(x)## S4 method for signature 'BarcodeObj' format(x)
x |
A BarcodeObj object |
Formated summary text.
data(bc_obj) # format BarcodeObj for pretty print format(bc_obj) ###data(bc_obj) # format BarcodeObj for pretty print format(bc_obj) ###
Parse 10X bam file
parse_10x_sam(in_file_path, regex_str, cell_barcode_tag = "CR", umi_tag = "UR")parse_10x_sam(in_file_path, regex_str, cell_barcode_tag = "CR", umi_tag = "UR")
in_file_path |
A string, define the un-mapped sequences |
regex_str |
A string, define the regular expression to match the barcode
sequence. The barcode sequence should be in the first catch. Please see the
|
cell_barcode_tag |
A string, define the tag of 10X cell barcode field in sam file. The default is "CR". |
umi_tag |
A string, define the tag of UMI field in the sam file. |
A data.frame with 4 columns:
cell_barcode: 10X cellular barcode.
umi: UMI sequence.
barcode_seq: lineage barcode.
count: reads count.
This function will merge the UMIs by using the hamming distance. If two UMIs have hamming distance no more than 1, only the UMI with more reads will be kept.
seq_correct( seq, count, count_threshold, dist_threshold, depth_fold_threshold = 1, dist_method = 1L, insert_cost = 1L, delete_cost = 1L, replace_cost = 1L )seq_correct( seq, count, count_threshold, dist_threshold, depth_fold_threshold = 1, dist_method = 1L, insert_cost = 1L, delete_cost = 1L, replace_cost = 1L )
seq |
A string vector. |
count |
An integer vector with the same order and length of UMI |
count_threshold |
An integer, barcode count threshold to consider a barcode as a true barcode, when when a barcode with count higher than this threshold it will not be removed. |
dist_threshold |
A integer, distance threshold to consider two barcodes are related. |
depth_fold_threshold |
An numeric, control the fold cange threshold between the ' major barcodes and the potential contamination that need to be removed. |
dist_method |
A integer, if 2 the levenshtein distance will be used, otherwise the hamming distance will be applied. |
insert_cost |
A integer, the insert cost when levenshtein distance is applied. |
delete_cost |
A integer, the delete cost when levenshtein distance is applied. |
replace_cost |
A integer, the replace cost when levenshtein distance is applied. |
This function will return the corrected UMI list.
a list with two data.frame. seq_freq_tab: table with barcode and corrected ' sequence reads; link_tab: data table record for the clustering process with ' first column of barcode be removed and second column of the majority barcode barcode.
Show the summary of BarcodeObj object for pretty print.
Show the summary of BarcodeQc object for pretty print.
Show the summary of BarcodeQcSet object for pretty print.
## S4 method for signature 'BarcodeObj' show(object) ## S4 method for signature 'BarcodeQc' show(object) ## S4 method for signature 'BarcodeQcSet' show(object)## S4 method for signature 'BarcodeObj' show(object) ## S4 method for signature 'BarcodeQc' show(object) ## S4 method for signature 'BarcodeQcSet' show(object)
object |
A BarcodeQcSet object |
Formated summary text.
Formated summary text.
Formated summary text.
data(bc_obj) # show BarcodeObj for pretty print bc_obj ###data(bc_obj) # show BarcodeObj for pretty print bc_obj ###
Subset the BarcodeQcSet
## S4 method for signature 'BarcodeQcSet' subset(x, i, drop = TRUE) ## S4 method for signature 'BarcodeQcSet,ANY,ANY,ANY' x[i, drop = TRUE]## S4 method for signature 'BarcodeQcSet' subset(x, i, drop = TRUE) ## S4 method for signature 'BarcodeQcSet,ANY,ANY,ANY' x[i, drop = TRUE]
x |
A BarcodeQcSet object |
i |
A integer vector or a character vector, specifying the selected samples. |
drop |
a logical value, if TRUE, when only one sample is selected, the output will be a BarcodeQc object. |
A BarcodeQcSet or BarcodeQc
example_data <- system.file("extdata", "mef_test_data", package = "CellBarcode") fq_files <- dir(example_data, "fastq.gz", full=TRUE) qc_noFilter <- bc_seq_qc(fq_files) qc_noFilter[1:3]example_data <- system.file("extdata", "mef_test_data", package = "CellBarcode") fq_files <- dir(example_data, "fastq.gz", full=TRUE) qc_noFilter <- bc_seq_qc(fq_files) qc_noFilter[1:3]