Find a set of barcode combinations with least heterogeneity in
barcode usage for single and dual indexing
Description
This function uses the Shannon Entropy to identify a set of compatible
barcode combinations with least heterogeneity in barcode usage,
in the context of single and dual indexing.
It performs either an exhaustive or a random-greedy search of compatible
DNA-barcode combinations depending on the size of the
DNA-barcode population and of the number of samples to be multiplexed.
Usage
experiment_design(file1, sample_number, mplex_level, platform = 4,
file2 = NULL, export = NULL, metric = NULL, d = 3, thrs_size_comb,
max_iteration, method)
Arguments
file1 |
The input data file that contains 2 columns separated by a
space or a tabulation, namely the sequence identifiers and corresponding
DNA sequence
|
sample_number |
Number of libraries to be sequenced.
|
mplex_level |
The number at which the barcodes will be multiplexed.
|
platform |
An integer representing the number of channels (1, 2, 4)
of the desired Illumina platform: 1 for iSeq; 2 for NextSeq, NovaSeq,
MiniSeq; 4 for HiSeq and MiSeq. 0 represents any other platform than
Illumina.
|
file2 |
The input data file that contains 2 columns separated by
a space or a tabulation, namely the sequence identifiers and
corresponding DNA sequence; used for dual-indexing, see details below.
|
export |
If not NULL, results are saved in a csv file at the
specified path.
|
metric |
The type of distance (hamming or seqlev).
|
d |
The minimum value of the distance.
|
thrs_size_comb |
The maximum size of the set of compatible
combinations to be used for the greedy optimization.
|
max_iteration |
The maximum number of iterations during the
optimizing step.
|
method |
The choice of the greedy search: 'greedy_exchange' or
'greedy_descent'.
|
Details
By specifying the total number of libraries and the number of libraries
to be multiplexed, this function returns an optimal set of DNA-barcode
combinations to be used for sequencing.
In the case of single indexing, one must only provide one input file
containing a list of DNA barcodes (file1 argument). The file2 argument being
optional, the program runs the optimisation for single indexing if this
argument is left empty. The output shows the sample ID with its respective
single barcode.
In the case of dual indexing, one must provide two input files
containing DNA barcodes as two separate sets of barcodes. The program will
detect these two files and automatically switch to the 'dual indexing' mode.
The program then runs the optimisation for each barcode set separately.
The output shows the sample ID with its respective pair of barcodes.
The inputs of the algorithm are a list of n distinct barcodes,
the number N of required libraries, and the multiplex level k; N = ak,
where a is the number of lanes of the flow cells to be used for the
experiment.
This step consists of identifying a set of compatible barcode
combinations. Given the number of barcodes and the multiplex level,
the total number of barcode combinations (compatible or not) reads:
(kn)
If this number is not too large, the algorithm will perform an
exhaustive search and output all compatible combinations of k barcodes.
Otherwise, it will proceed by picking up combinations at random, in
order to identify a large enough set of compatible barcode combinations.
Finds an optimized set of barcode combinations in which barcode
redundancy is minimized (see details in
optimize_combinations)
Value
A dataframe containing compatible DNA-barcode combinations organized
by lanes of the flow cell.
Examples
write.table(DNABarcodeCompatibility::IlluminaIndexesRaw,
txtfile <- tempfile(), row.names = FALSE, col.names = FALSE, quote=FALSE)
experiment_design(file1=txtfile, sample_number=18, mplex_level=3,
platform=4)
