| Title: | Resolution-Optimised SNPs Searcher |
| Version: | 0.2.0 |
| Description: | This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) <doi:10.1186/1471-2105-8-278> "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants." |
| Depends: | R (≥ 3.4.0) |
| License: | MIT + file LICENSE |
| Imports: | BiocParallel, data.table |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Suggests: | knitr, testthat, pkgdown, rmarkdown, withr |
| VignetteBuilder: | knitr |
| URL: | https://github.com/ludwigHoon/minSNPs |
| NeedsCompilation: | no |
| Packaged: | 2024-03-05 13:05:50 UTC; ldwg |
| Author: | Ludwig Kian Soon Hoon
 [aut, cre],
Peter Shaw [aut,
ctb],
Phil Giffard
[aut, ctb] |
| Maintainer: | Ludwig Kian Soon Hoon <ldwgkshoon@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-03-05 14:30:02 UTC |
binomial_naive_bayes
Description
binomial_naive_bayes is an implementation of the binomial naive bayes algorithm.
modified from bernoulli_naive_bayes function in the naivebayes package
Usage
binomial_naive_bayes(x, y, prior = NULL, laplace = 1, ...)
Arguments
x |
a matrix with numeric: 0,1, up to binomial_n columns |
y |
a factor or character or logical vector |
prior |
a vector of prior probabilities |
laplace |
a numeric value for Laplace smoothing |
... |
additional arguments |
Value
return a binomial_naive_bayes object
branch_and_search
Description
branch_and_search is the actual function used to
find optimised SNPs set. This function is called by
find_optimised_snps, after preprocessing.
Usage
branch_and_search(
starting_positions = c(),
excluded_positions = c(),
seqc,
metric,
number_of_result = 1,
max_depth = 1,
bp = SerialParam(),
...
)
Arguments
starting_positions |
the starting positions that is already in the SNP set. |
excluded_positions |
excluded positions |
seqc |
list of sequences, either passed directly from
|
metric |
either 'simpson' or 'percent' |
number_of_result |
number of results to return, 0 will be coerced to 1 |
max_depth |
maximum depth to go before terminating, 0 means it will only calculate the metric for included position |
bp |
BiocParallel backend. Rule of thumbs: use MulticoreParam(workers = ncpus - 2) |
... |
other parameters as needed |
Value
Will return the resolution-optimised SNPs set, based on the metric.
cal_fn
Description
cal_fn is used to check if the proportion of false negative
fastas and metas are compatible.
Usage
cal_fn(pattern, goi, target)
Arguments
pattern |
the pattern from |
goi |
the group of interest (names of isolates) |
target |
the target sequence(s) |
Value
proportion: no. false negative/number of isolates
cal_fp
Description
cal_fp is used to check if the proportion of false positive
fastas and metas are compatible.
Usage
cal_fp(pattern, goi, target)
Arguments
pattern |
the pattern from |
goi |
the group of interest (names of isolates) |
target |
the target sequence(s) |
Value
proportion: no. false positive/number of isolates
cal_met_snp
Description
cal_met_snp is used to calculate the metric at each position
Usage
cal_met_snp(position, metric, seqc, prepend_position = c(), ...)
Arguments
position |
position to check |
metric |
either 'simpson' or 'percent' |
seqc |
list of sequences, either passed directly from
|
prepend_position |
is the position to be added to the |
... |
other parameters as needed |
Value
return the value at that position, as well as base pattern for next iteration.
calculate_mcc
Description
calculate_mcc is used to calculate the MCC score given the SNP profile.
Usage
calculate_mcc(pattern, goi, MUST_HAVE_TARGET = TRUE)
Arguments
pattern |
the SNP profile for each samples |
goi |
the samples belonging to the group of interest |
MUST_HAVE_TARGET |
whether to force the profile to have at least 1 target profile (the profile containing the most goi) |
Value
the MCC score
calculate_mcc_multi
Description
calculate_mcc_multi Calculate the multi-class MCC score for the SNPs.
It assigns each SNP profile to a class, based on the majority of the samples having the profile,
targets with the less samples are prioritised first, breaking ties by alphabetical order.
Usage
calculate_mcc_multi(pattern, meta, target = "target", priority = NULL)
Arguments
pattern |
the SNP profile for each samples |
meta |
A data.table containing the meta data |
target |
the column name of the target in the meta data, default to target |
priority |
A data.table of the targets and priority,
either supplied by user, or by default generated by |
Value
multiclass-MCC score
calculate_percent
Description
calculate_percent is used to calculate dissimilarity index,
proportion of isolates not in goi that have been discriminated against.
1 being all and 0 being none.
Usage
calculate_percent(pattern, goi)
Arguments
pattern |
list of sequences' pattern (profile) |
goi |
group of interest |
Value
Will return the dissimilarity index of the list of patterns.
calculate_simpson
Description
calculate_simpson is used to calculate Simpson's index.
Which is in the range of 0-1, where the greater the value,
the more diverse the population.
Usage
calculate_simpson(pattern)
Arguments
pattern |
list of sequences' pattern (profile) |
Value
Will return the Simpson's index of the list of patterns.
calculate_simpson_by_group
Description
calculate_simpson_by_group is used to calculate Simpson's index.
Which is in the range of 0-1, where the greater the value,
the more diverse the population.
Usage
calculate_simpson_by_group(pattern, meta, target)
Arguments
pattern |
list of sequences' pattern (profile) |
meta |
the metadata |
target |
the target column name |
Value
Will return the Simpson's index of the list of patterns.
calculate_state
Description
calculate_state calculate the number of states given the SNP(s)
Usage
calculate_state(pattern)
Arguments
pattern |
list of sequences' pattern (profile) |
Value
number of states
identify_group_variant_breakdown
Description
calculate_variant_within_group is used to identify proportion of different samples
having the same profile.
Usage
calculate_variant_within_group(pattern, meta, target, get_count = FALSE)
Arguments
pattern |
list of sequences' pattern (profile) |
meta |
metadata of the sequences |
target |
column name of the target group |
get_count |
whether to return the count of samples rather than the raw number, default to FALSE. |
Value
Will return the Simpson's index of the list of patterns.
check_fasta_meta_mapping
Description
check_fasta_meta_mapping is used to check if
fastas and metas are compatible.
Usage
check_fasta_meta_mapping(fasta, meta)
Arguments
fasta |
the fasta read into memory to join |
meta |
the meta read into memory to join |
Value
TRUE/FALSE if the fasta and meta are compatible
check_meta_target
Description
check_meta_target is used to check if parameters needed by
calculate_mcc_multi and simpson_by_group are all present.
Usage
check_meta_target(list_of_parameters)
Arguments
list_of_parameters |
is a list of parameter passed to functions that will perform the calculation |
Value
TRUE if the parameters exists, else FALSE
check_multistate
Description
check_multistate is used to remove positions where there are
more than 1 state within the group of interest.
Usage
check_multistate(position, sequences)
Arguments
position |
position to check |
sequences |
sequences from group of interest |
Value
return 'TRUE' if the position contains multistate otherwise 'FALSE'
check_percent
Description
check_percent is used to check if parameters needed by
calculate_percent are all present.
Usage
check_percent(list_of_parameters)
Arguments
list_of_parameters |
is a list of parameter passed to functions that will perform the calculation |
Value
TRUE if goi exists, else FALSE
coef.binomial_naive_bayes
Description
coef.binomial_naive_bayes is an implementation of the coef method for the binomial naive bayes algorithm.
modified from bernoulli_naive_bayes function in the naivebayes package
Usage
## S3 method for class 'binomial_naive_bayes'
coef(object, ...)
Arguments
object |
a binomial_naive_bayes object |
... |
additional arguments |
Value
return a data frame of coefficients
combine_fastq_search_result
Description
combine_fastq_search_result combines the search results from search_from_fastq_reads
Usage
combine_fastq_search_result(
results,
search_table,
previous_result = NULL,
bp = MulticoreParam()
)
Arguments
results |
the result (fastq_search_result) from |
search_table |
a dataframe with the following columns: - "id","type","sequence","strand","result","extra","match_ref_seq" |
previous_result |
the result (fastq_search_result) to append to |
bp |
BiocParallel backend to use for parallelization |
Value
will return a dataframe containing: - 'sequence', 'search_id', 'reads', 'raw_match', 'mean_qualities', 'indexes', 'id', 'type', 'strand', 'result', 'extra', 'match_ref_seq', 'n_reads'
combine_search_string_result
Description
combine_search_string_result combines the search results from search_from_fastq_reads
Usage
combine_search_string_result(
results,
search_table,
append_to_current_result = data.frame(),
bp = MulticoreParam()
)
Arguments
results |
the dataframes to collapse. |
search_table |
a dataframe with the following columns: - "id","type","sequence","strand","result","extra","match_ref_seq" |
append_to_current_result |
the dataframe of previous result to append to |
bp |
BiocParallel backend to use for parallelization |
Value
will return a dataframe containing: - 'sequence', 'search_id', 'reads', 'raw_match', 'mean_qualities', 'indexes', 'id', 'type', 'strand', 'result', 'extra', 'match_ref_seq', 'n_reads'
combine_search_string_result_from_files
Description
combine_search_string_result_from_files
combine_search_string_result combines the search results from temp file generated from search_from_fastq_reads
Usage
combine_search_string_result_from_files(
result_files,
search_table,
read_length_files = c(),
append_to_current_result = NULL,
bp = MulticoreParam()
)
Arguments
result_files |
the output files from |
search_table |
a dataframe with the following columns: - "id","type","sequence","strand","result","extra","match_ref_seq" |
read_length_files |
the read_length output files from |
append_to_current_result |
the fastq_search_result of result to append to |
bp |
BiocParallel backend to use for parallelization |
Value
will return a fastq_search_result object containing read_lengths and a dataframe containing: - 'sequence', 'search_id', 'reads', 'raw_match', 'mean_qualities', 'indexes', 'id', 'type', 'strand', 'result', 'extra', 'match_ref_seq', 'n_reads'
combine_search_string_result_from_list
Description
combine_search_string_result_from_list combines the search results from search_from_fastq_reads
Usage
combine_search_string_result_from_list(
results,
search_table,
append_to_current_result = data.frame(),
bp = MulticoreParam()
)
Arguments
results |
the dataframes from |
search_table |
a dataframe with the following columns: - "id","type","sequence","strand","result","extra","match_ref_seq" |
append_to_current_result |
the dataframe of previous result to append to |
bp |
BiocParallel backend to use for parallelization |
Value
will return a dataframe containing: - 'sequence', 'search_id', 'reads', 'raw_match', 'mean_qualities', 'indexes', 'id', 'type', 'strand', 'result', 'extra', 'match_ref_seq', 'n_reads'
estimate_coverage
Description
estimate_coverage
estimate_coverage estimate the average coverage by comparing number of bases from reads to genome size
Usage
estimate_coverage(read_lengths, genome_size)
Arguments
read_lengths |
the lengths of the reads |
genome_size |
the genome size |
Value
will return an estimated average coverage
extend_length
Description
extend_length extend the search sequence such that
there will always be (prev) bases before the SNPs and
(after) bases after the SNPs.
Usage
extend_length(
overlaps,
position_reference,
genome_position,
prev,
after,
ori_string_start,
ori_string_end,
ori_snp_pos,
genome_max
)
Arguments
overlaps |
Overlappings |
position_reference |
the mapping of position in SNP matrix to reference genome |
genome_position |
the position of the SNP in the reference genome |
prev |
number of bases before the SNP included in the search string |
after |
number of bases after the SNP included in the search string |
ori_string_start |
original starting point of search string |
ori_string_end |
original ending point of the search string |
ori_snp_pos |
original SNP position in search string |
genome_max |
length of the reference genome |
Value
a list containing the new 'string_start', 'string_end', 'snp_pos', 'snps_in_string'.
find_optimised_snps
Description
find_optimised_snps is used to find optimised SNPs set.
Usage
find_optimised_snps(
seqc,
metric = "simpson",
goi = c(),
accept_multiallelic = TRUE,
number_of_result = 1,
max_depth = 1,
included_positions = c(),
excluded_positions = c(),
search_from = NULL,
iterate_included = FALSE,
completely_unique = FALSE,
bp = SerialParam(),
...
)
Arguments
seqc |
list of sequences, either passed directly from
|
metric |
either 'simpson' or 'percent' |
goi |
group of interest, if creteria is percent, must be specified, ignored otherwise |
accept_multiallelic |
whether include positions with > 1 state in goi |
number_of_result |
number of results to return, 0 will be coerced to 1 |
max_depth |
maximum depth to go before terminating, 0 means it will only calculate the metric for included position |
included_positions |
included positions |
excluded_positions |
excluded positions |
search_from |
search only from these positions, i.e., any positions not in here are excluded, default to NULL |
iterate_included |
whether to calculate index at each level of the included SNPs |
completely_unique |
whether to identify completely unique SNPs set, default to FALSE, only the 1st SNP must be different |
bp |
BiocParallel backend. Rule of thumbs: use MulticoreParam(workers = ncpus - 2) |
... |
other parameters as needed |
Value
Will return the resolution-optimised SNPs set, based on the metric.
flag_allele
Description
flag_allele is used to find out a list of samples that has been
flagged with length different from most and will not be included in
computation of minimum SNPs.
Usage
flag_allele(seqc, bp = BiocParallel::SerialParam())
Arguments
seqc |
a list containing list of nucleotides. To keep it simple, use provided read_fasta to import the fasta file. |
bp |
is the biocparallel backend, default to serialParam, most likely sufficient in most scenario |
Value
Will return a list of ignored samples.
flag_position
Description
flag_position is used to find out positions that will be ignored in
calculation (either not A,C,G,T or '-'), can be case sensitive
or insensitive.
Usage
flag_position(
pro_seqc,
dash_ignore = TRUE,
accepted_char = c("A", "C", "T", "G"),
ignore_case = TRUE,
remove_invariant = FALSE,
biallelic_only = FALSE,
bp = SerialParam()
)
Arguments
pro_seqc |
Sequences after processed, i.e. all with the same length |
dash_ignore |
whether to treat '-' as another type |
accepted_char |
character to accept, default to c("A", "C", "T", "G") |
ignore_case |
whether to be case insensitive, default to TRUE |
remove_invariant |
whether to remove invariant positions, default to FALSE |
biallelic_only |
whether to remove positions with more than 2 alleles, default to FALSE |
Value
Will return a list of positions that need to be ignored.
full_merge
Description
full_merge is used to merge 2 fasta,
where a position exist only in 1 of the fasta, the fasta without allele
in that positions are given reference genome's allele at that position.
**Doesn't work for large dataset, hence the need for full_merge_1**
Usage
full_merge(
fasta_1,
fasta_2,
meta_1,
meta_2,
ref,
bp = BiocParallel::MulticoreParam(),
...
)
Arguments
fasta_1 |
fasta read into memory to join |
fasta_2 |
fasta read into memory to join |
meta_1 |
meta file for 'fasta_1' denoting all positions of SNPs and position in reference genome |
meta_2 |
meta file for 'fasta_2' denoting all positions of SNPs and position in reference genome |
ref |
name of the reference genome (needs to be in both fasta files) |
bp |
the BiocParallel backend |
... |
all other arguments |
Value
merged fasta and meta
full_merge_1
Description
full_merge_1 is used to merge 2 fasta,
where a position exist only in 1 of the fasta, the fasta without allele
in that positions are given reference genome's allele at that position.
Usage
full_merge_1(
fasta_1,
fasta_2,
meta_1,
meta_2,
ref,
bp = BiocParallel::SerialParam(),
...
)
Arguments
fasta_1 |
fasta read into memory to join |
fasta_2 |
fasta read into memory to join |
meta_1 |
meta file for 'fasta_1' denoting all positions of SNPs and position in reference genome |
meta_2 |
meta file for 'fasta_2' denoting all positions of SNPs and position in reference genome |
ref |
name of the reference genome (needs to be in both fasta files) |
bp |
the BiocParallel backend |
... |
all other arguments |
Value
merged fasta and meta
generate_kmer_search_string
Description
generate_kmer_search_string generate the search strings
to detect genes' presence
Usage
generate_kmer_search_string(
gene_seq,
k,
id_prefix = NULL,
bp = MulticoreParam()
)
Arguments
gene_seq |
sequences to generate k_mers from |
k |
kmer length |
id_prefix |
prefix for the gene id |
bp |
BiocParallel backend to use |
Value
a dataframe containing the search strings
generate_kmers
Description
generate_kmers generate the kmer sequences of the given length
Usage
generate_kmers(final_string, k)
Arguments
final_string |
the string to generate kmers |
k |
the length of the kmer |
Value
a vector of kmers
generate_pattern
Description
generate_pattern is used to generate pattern for calculation.
Usage
generate_pattern(seqc, ordered_index = c(), append_to = list())
Arguments
seqc |
list of sequences |
ordered_index |
list of indexes for the pattern in the order |
append_to |
existing patterns to append to |
Value
Will return concatenated list of string for searching.
generate_prioritisation
Description
generate_prioritisation create a vector of the targets in order of priority.
Targets with the less samples are prioritised first, breaking ties by alphabetical order.
Usage
generate_prioritisation(meta)
Arguments
meta |
A data.table containing the meta data, expect the |
Value
A data.table of the targets in order of priority
generate_snp_search_string
Description
generate_snp_search_string identify the SNPs that will overlap
the search strings generated from the targeted SNPs
Usage
generate_snp_search_string(
selected_snps,
position_reference,
ref_seq,
snp_matrix,
prev,
after,
position_type = "fasta",
extend_length = TRUE,
fasta_name_as_result = TRUE,
bp = MulticoreParam()
)
Arguments
selected_snps |
list of targeted SNPs |
position_reference |
the mapping between reference genome positions and orthologous SNP matrix positions |
ref_seq |
the reference genome sequence |
snp_matrix |
the orthologous SNP matrix |
prev |
number of characters before the SNP |
after |
number of characters after the SNP |
position_type |
type of SNPs input, "fasta" (orthologous SNP matrix based) or "genome" (reference genome based); Default to "fasta" |
extend_length |
whether to extend the search string before and after the SNP and ignore overlapping SNPs |
fasta_name_as_result |
Whether the result should use the fasta matching sequence name or the fasta position and allele, default to using fasta sequence name (TRUE) |
bp |
BiocParallel backend to use |
Value
a dataframe containing the search strings
get_all_process_methods
Description
get_all_process_methods is used to get the metrics function
and required parameters. Additional metric may be set by
assigning it to 'MinSNPs_process_methods' variable.
Usage
get_all_process_methods(process_name = "")
Arguments
process_name |
name of the metric, "" to return all, 'SNP' or 'KMER' are provided as default. |
Value
a list, including the function to process the search sequence result
get_binomial_tables
Description
get_binomial_tables is an internal function that returns a table of probability for binomial naive bayes.
modified from bernoulli_naive_bayes function in the naivebayes package
Usage
get_binomial_tables(prob1)
Arguments
prob1 |
a matrix of probabilities |
Value
return table of probability for binomial naive bayes
get_metric_fun
Description
get_metric_fun is used to get the metrics function
and required parameters. Additional metric may set by
assigning to 'MinSNPs_metrics' variable.
Usage
get_metric_fun(metric_name = "")
Arguments
metric_name |
name of the metric, by default percent/simpson |
Value
a list, including the function to calculate the metric based on a position ('calc'), and function to check for additional parameters the function need ('args')
get_positions_to_search
Description
get_positions_to_search is used to identify all the positions
to loop through, used by branch_and_search and find_optimised_snps
Usage
get_positions_to_search(seqc_len, excluded_pos, traversed, bp)
Arguments
seqc_len |
length of the matrix |
excluded_pos |
vector of excluded positions |
traversed |
vector of positions that is previously selected |
bp |
BiocParallel backend |
Value
Will return a list of positions to search through
get_snps_set
Description
get_snps_set extract the SNP sets from the output of
'find_optimised_snps'.
Usage
get_snps_set(results, as = "data.frame")
Arguments
results |
output from 'find_optimised_snps' |
as |
output format, either 'data.frame' or 'list'. |
Value
will return either 1. a dataframe containing SNPs_set (SNP position separated by ",") and score 2. a list containing SNPs_set (SNP position as numeric vector) and score (attr of the list)
get_usual_length
Description
get_usual_length is used to find out
the length of the sequences (W/O deletion).
Usage
get_usual_length(seqc, bp = SerialParam())
Arguments
seqc |
a list containing list of nucleotides. To keep it simple, use provided read_fasta to import the fasta file. |
bp |
is the biocparallel backend, default to serialParam, most likely sufficient in most scenario |
Value
Will return the length of most of the samples, the higher number is taken as tie breaker
identify_overlaps
Description
identify_overlaps identify the overlapping SNPs in the sequences
Usage
identify_overlaps(position_reference, genome_position, prev, after)
Arguments
position_reference |
the mapping of position in SNP matrix to reference genome |
genome_position |
the position of the SNP in the reference genome |
prev |
number of bases before the SNP included in the search string |
after |
number of bases after the SNP included in the search string |
Value
a list containing 2 dataframes listing the bystander SNPs in the flanking sequence before and after the SNPs
infer_from_combined
Description
infer_from_combined infers the results (presence/absense of genes & CC) from the combined result
Usage
infer_from_combined(combined_result, search_table, genome_size, ...)
Arguments
combined_result |
the combined result from |
search_table |
a dataframe with the following columns: - "id","type","sequence","strand","result","extra","match_ref_seq" |
genome_size |
estimated genome size for coverage calculation |
... |
additional arguments to pass to the process methods |
Value
a dataframe containing the following columns: - type, rank, result, reads_count, proportion_matched, pass_filter
iterate_merge
Description
iterate_merge is used to combine > 2 fastas iteratively.
Usage
iterate_merge(
fastas,
metas,
ref,
method = "full",
bp = BiocParallel::SerialParam(),
...
)
Arguments
fastas |
list of fastas read into memory to join |
metas |
list of metas read into memory to join |
ref |
name of the reference genome (needs to be in both fasta files) |
method |
how to join the 2 fasta, currently supported methods are: inner, full |
bp |
the BiocParallel backend |
... |
all other arguments |
Value
Will return a list containing a merged FASTA and a meta.
iterate_through
Description
iterate_through is used to calculate the metric at each position
Usage
iterate_through(metric, seqc, bp = MulticoreParam(), ...)
Arguments
metric |
either 'simpson' or 'percent' |
seqc |
list of sequences, either passed directly from
|
bp |
BiocParallel backend. Rule of thumbs: use MulticoreParam(workers = ncpus - 2) |
... |
other parameters as needed |
Value
return a dataframe containing the position and result.
map_profile_to_target
Description
map_profile_to_target creates a mapping of the profile to the target, breaking the ties by the priority.
Usage
map_profile_to_target(meta, patterns, priority, sensitive_to_1 = FALSE)
Arguments
meta |
A data.table containing the meta data |
patterns |
A list of the patterns from |
priority |
A data.table of the targets and priority, either generated by |
sensitive_to_1 |
whether to be completely sensitive to the first target (percent default), set to TRUE for percent |
Value
A vector of the targets in order of priority
match_count
Description
match_count return the number of matches of the target string in the
given sequence
Usage
match_count(target, search_from)
Arguments
target |
the search target |
search_from |
the sequence to search from |
Value
number of matches
mcc_calculation
Description
mcc_calculation calculate the MCC score given the truth and predicted target.
Usage
mcc_calculation(
result_with_truth,
is_multi = TRUE,
return_all_intermediate = FALSE
)
Arguments
result_with_truth |
the dataframe containing the truth and predicted target |
is_multi |
Whether to use MCC-multi or MCC |
return_all_intermediate |
whether to return all intermediate values, only possible for binary class |
Value
Will return the mcc score
merge_fasta
Description
merge_fasta is used to combine 2 fasta.
Usage
merge_fasta(
fasta_1,
fasta_2,
meta_1,
meta_2,
ref,
method = "full",
bp = BiocParallel::SerialParam(),
...
)
Arguments
fasta_1 |
fasta read into memory to join |
fasta_2 |
fasta read into memory to join |
meta_1 |
meta file for 'fasta_1' denoting all positions of SNPs and position in reference genome |
meta_2 |
meta file for 'fasta_2' denoting all positions of SNPs and position in reference genome |
ref |
name of the reference genome (needs to be in both fasta files) |
method |
how to join the 2 fasta, currently supported methods are: inner, full |
bp |
the BiocParallel backend |
... |
all other arguments |
Value
Will return a list containing a merged FASTA and a meta.
output_parsed
Description
output_parsed is subfunction used by output_result
to present the result and save the result as tsv.
Usage
output_parsed(
parsed_view,
seqc_obj,
result,
file_name,
additional_details = TRUE
)
Arguments
parsed_view |
the result from |
seqc_obj |
the sequences object. |
result |
is the result from |
file_name |
file name, either tsv or "" for terminal output |
additional_details |
whether to print additional details. |
Value
NULL, result either printed or saved as tsv.
output_result
Description
output_result is used to present the result
and save the result as tsv.
Usage
output_result(result, view = "", ...)
Arguments
result |
is the result from |
view |
how to present the output, "csv" or "tsv" will be saved as a file. Otherwise, printed to console. |
... |
if view is "tsv" or "csv", file name can be passed, e.g., file_name = "result.tsv", otherwise, file is saved as <timestamp>.tsv. |
Value
NULL, result either printed or saved as tsv.
output_to_files
Description
output_to_files is write the result to files.
Usage
output_to_files(merged_result, filename = "merged")
Arguments
merged_result |
a list containing the merged fasta and meta. |
filename |
filename to write to, will output <filename>.fasta and <filename>.csv. |
Value
NULL, files written to filesystem
parse_group_mcc
Description
parse_group_mcc is used to group the sample according to SNPs profile and present in a table format
Usage
parse_group_mcc(pattern, goi, MUST_HAVE_TARGET = TRUE)
Arguments
pattern |
the SNP profile for each samples |
goi |
the samples belonging to the group of interest |
MUST_HAVE_TARGET |
whether to force the profile to have at least 1 target profile (the profile containing the most goi) |
Value
the parsed group views
parse_group_mcc_multi
Description
parse_group_mcc_multi is used to put samples according to SNP profile, and
put them into a table format.
Usage
parse_group_mcc_multi(result, as_string = TRUE)
Arguments
result |
result from |
as_string |
whether to return the result as string or data.frame |
Value
Will return the grouped samples.
predict.binomial_naive_bayes
Description
predict.binomial_naive_bayes is an implementation of the predict method for the binomial naive bayes algorithm.
modified from bernoulli_naive_bayes function in the naivebayes package
Usage
## S3 method for class 'binomial_naive_bayes'
predict(object, newdata = NULL, type = c("class", "prob"), ...)
Arguments
object |
a binomial_naive_bayes object |
newdata |
a matrix with numeric: 0,1,up to binomial_n columns |
type |
a character string specifying the type of output: "class" or "prob" |
... |
additional arguments |
Value
return a factor or matrix of class probabilities
predict_balk
Description
predict_balk is a function that predicts the class of new data using a binomial naive bayes classifier
Usage
predict_balk(object, newdata = NULL, snp_id = NULL, type = "prob")
Arguments
object |
The classifier object |
newdata |
A list of sequences |
snp_id |
A vector of SNP IDs |
type |
The type of prediction, either "prob" or "class" |
Value
A vector of either the class or the probability of the class
print.binomial_naive_bayes
Description
print.binomial_naive_bayes is an implementation of the print method for the binomial naive bayes algorithm.
modified from bernoulli_naive_bayes function in the naivebayes package
Usage
## S3 method for class 'binomial_naive_bayes'
print(x, ...)
Arguments
x |
a binomial_naive_bayes object |
... |
additional arguments |
Value
return a binomial_naive_bayes object
process_allele
Description
process_allele is used to returned the processed allelic profiles, by
removing the allele profile with duplicate name and length different
from most. 1st allele profile with the duplicated name is returned,
the longer length is taken as normal should there be 2 modes.
Usage
process_allele(
seqc,
bp = BiocParallel::SerialParam(),
check_length = TRUE,
check_bases = TRUE,
dash_ignore = TRUE,
accepted_char = c("A", "C", "T", "G"),
ignore_case = TRUE,
remove_invariant = FALSE,
biallelic_only = FALSE
)
Arguments
seqc |
a list containing list of nucleotides. To keep it simple, use provided read_fasta to import the fasta file. |
bp |
is the biocparallel backend, default to serialParam, most likely sufficient in most scenario |
check_length |
Check the length of each sample in the matrix, default to TRUE |
check_bases |
Check the bases of each sample in the matrix, default to TRUE |
dash_ignore |
whether to treat '-' as another type |
accepted_char |
character to accept, default to c("A", "C", "T", "G") |
ignore_case |
whether to be case insensitive, default to TRUE |
remove_invariant |
whether to remove invariant positions, default to FALSE |
biallelic_only |
whether to remove positions with more than 2 alleles, default to FALSE |
Value
Will return the processed allelic profiles.
process_kmer_result
Description
process_kmer_result processes the KMER result from infer_from_combined
Usage
process_kmer_result(partial_result, search_table, min_match_per_read = 1, ...)
Arguments
partial_result |
the result from |
search_table |
a dataframe with the following columns: - "id","type","sequence","strand","result","extra","match_ref_seq" |
min_match_per_read |
the minimum number of kmer matches in a read, discarding reads with less than this number |
... |
ignored |
Value
a dataframe containing the following columns: - type, rank, result, reads_count, proportion_matched, pass_filter, proportion_scheme_found, details
process_result_file
Description
process_result_file extract the SNP sets from the saved output file.
Usage
process_result_file(result_filepath)
Arguments
result_filepath |
is the path of the saved output file. |
Value
will return a list containing SNPs_set (SNP position as numeric vector).
process_snp_result
Description
process_snp_result processes the SNP result from infer_from_combined
Usage
process_snp_result(
partial_result,
search_table,
count_measure = "n_reads",
...
)
Arguments
partial_result |
the result from |
search_table |
a dataframe with the following columns: - "id","type","sequence","strand","result","extra","match_ref_seq" |
count_measure |
the column name of the count measure to use for removing the conflicts |
... |
ignored |
Value
a list containing: - result: a dataframe containing the following columns: - type, rank, result, reads_count, proportion_matched, pass_filter, proportion_scheme_found, details - snps_found: a vector containing the SNPs ID that have been identified without conflict - proportion_snps_found: the proportion of SNPs found without conflict
profile_to_group_result
Description
profile_to_group_result given profile target, return the result
Usage
profile_to_group_result(patterns, profile_target)
Arguments
patterns |
the SNP profile for each samples |
profile_target |
the profile target - should be from samples previously seen, generate with |
Value
Will return the result, given the SNP profile.
read_fasta
Description
read_fasta is used to read fasta file, implementation
similar to seqinr, but much simpler and allow for spaces in sample name.
Usage
read_fasta(file, force_to_upper = TRUE, bp = SerialParam())
Arguments
file |
file path |
force_to_upper |
whether to transform sequences to upper case, default to TRUE |
bp |
is the biocparallel backend, default to serialParam, most likely sufficient in most scenario |
Value
Will return list of named character vectors.
read_sequences_from_fastq
Description
read_sequences_from_fastq get the sequences
from a fastq file, it completely ignores the quality scores
Usage
read_sequences_from_fastq(
fastq_file,
force_to_upper = TRUE,
skip_n_reads = 0,
max_n_reads = -1,
output_quality = TRUE,
quality_offset = 33,
bp = MulticoreParam()
)
Arguments
fastq_file |
location of the fastq file |
force_to_upper |
whether to transform sequences to upper case, default to TRUE |
skip_n_reads |
number of reads to skip, default to 0 |
max_n_reads |
maximum number of reads to read, default to -1 (all) |
output_quality |
whether to output the quality scores, default to TRUE |
quality_offset |
the quality offset to use, default to 33 |
bp |
BiocParallel backend to use for parallelization |
Value
will return a list of sequences, with qualities as attribute
remove_dup_isolate
Description
remove_dup_isolate is used to remove isolate with the same name.
Usage
remove_dup_isolate(seqc)
Arguments
seqc |
a list containing list of nucleotides. To keep it simple, use provided read_fasta to import the fasta file. |
Value
return the list of samples where only the first instance of the isolate with the duplicate name is kept
remove_snp_conflic
Description
remove_snp_conflic removes the reads with SNPs conflicts from the result
Usage
remove_snp_conflict(result, count_measure = "n_reads")
Arguments
result |
the result from |
count_measure |
the column name of the count measure to use for removing the conflicts |
Value
a dataframe containing the same columns as the input result with row containing conflicts removed
resolve_IUPAC_missing
Description
resolve_IUPAC_missing is used to replace the
ambiguity codes found in the sequences.
Usage
resolve_IUPAC_missing(
seqc,
log_operation = TRUE,
log_file = "replace.log",
max_ambiguity = -1,
replace_method = "random",
N_is_any_base = FALSE,
output_progress = TRUE,
bp = MulticoreParam()
)
Arguments
seqc |
the sequences to be processed |
log_operation |
whether to log the operation |
log_file |
log file to write the operations |
max_ambiguity |
proportion of ambiguity codes to tolerate, -1 = ignore. Default to -1 |
replace_method |
how to substitute the ambiguity codes, current supported methods:random and most_common, default to "random". |
N_is_any_base |
whether to treat N as any base or substitute it with one of the alleles found at the position. |
output_progress |
whether to output progress |
bp |
the BiocParallel backend |
Value
Will return the processed sequences.
reverse_complement
Description
reverse_complement returns the reverse complement of the
given sequence
Usage
reverse_complement(seq)
Arguments
seq |
the sequence to reverse complement |
Value
reverse complemented sequence
scramble_sequence
Description
scramble_sequence scramble the orthologous matrix based on a seed
Usage
scramble_sequence(seqc, seed)
Arguments
seqc |
the sequence to scramble |
seed |
the seed to use for scrambling |
Value
a named list, containing the scambled sequence and the new positions
search_from_fastq_reads
Description
search_from_fastq_reads identify the matches
from a list of search strings
Usage
search_from_fastq_reads(
fastq_file,
search_tables,
skip_n_reads = 0,
progress = TRUE,
max_n_reads = -1,
quality_offset = 33,
output_temp_result = TRUE,
temp_result_folder = "./temp_results",
simplify_id = TRUE,
output_read_length = TRUE,
bp = MulticoreParam()
)
Arguments
fastq_file |
fastq file containing the runs to search from |
search_tables |
a dataframe with the following columns: - ["id"],"type",["sequence"],"strand","result","extra","match_ref_seq" |
skip_n_reads |
number of reads to skip, default is 0 |
progress |
whether to show the progress bar |
max_n_reads |
maximum number of reads to read, default to -1 (all) |
quality_offset |
the quality offset to use, default to 33 |
output_temp_result |
whether to output the temporary results |
temp_result_folder |
directory to output the temporary results |
simplify_id |
simplify and shorten the read id to the first part |
output_read_length |
whether to output the read length, NULL - do not output; csv - output to csv file; data - output to result |
bp |
BiocParallel backend to use for parallelization |
Value
will return a list of dataframe containing: - 'search_id', 'sequence', 'reads', 'raw_match', 'mean_qualities', 'indexes'.
search_from_reads
Description
search_from_reads identify the matches
from a list of search strings
Usage
search_from_reads(
all_reads,
search_tables,
progress = TRUE,
ID = "S1",
all_qualities = NULL,
output_temp_result = TRUE,
temp_result_folder = "./temp_results",
output_read_length = TRUE,
bp = MulticoreParam()
)
Arguments
all_reads |
The reads containing the runs to search from |
search_tables |
a dataframe with the following columns: - ["id"],"type",["sequence"],"strand","result","extra","match_ref_seq" |
progress |
whether to show the progress bar |
ID |
the ID to use, default to S1 |
all_qualities |
quality data, default to NULL |
output_temp_result |
whether to output the temporary results |
temp_result_folder |
directory to output the temporary results |
output_read_length |
whether to output the read length, NULL - do not output; csv - output to csv file; data - output to result |
bp |
BiocParallel backend to use for parallelization |
Value
will return a list of dataframe containing: - 'search_id', 'sequence', 'reads', 'raw_match', 'mean_qualities', 'indexes'.
select_n_set_i_depth
Description
select_n_set_i_depth is the actual function used to
find optimised SNPs set. This function is called by
find_optimised_snps, after preprocessing.
Usage
select_n_set_i_depth(
starting_positions = c(),
excluded_positions = c(),
seqc,
metric,
number_of_result = 1,
max_depth = 1,
seqc_length,
bp = SerialParam(),
...
)
Arguments
starting_positions |
the starting positions that is already in the SNP set. |
excluded_positions |
excluded positions |
seqc |
list of sequences, either passed directly from
|
metric |
either 'simpson' or 'percent' |
number_of_result |
number of results to return, 0 will be coerced to 1 |
max_depth |
maximum depth to go before terminating, 0 means it will only calculate the metric for included position |
seqc_length |
the length to iterate through. |
bp |
BiocParallel backend. Rule of thumbs: use MulticoreParam(workers = ncpus - 2) |
... |
other parameters as needed |
Value
Will return the resolution-optimised SNPs set, based on the metric.
sequence_reads_match_count
Description
sequence_reads_match_count look for the search sequences in reads and return the matches indexes and mean qualities
Usage
sequence_reads_match_count(search_sequence, reads, qualities)
Arguments
search_sequence |
the search sequence to look for where '.' stands for any character. |
reads |
the sequences reads to search for. |
qualities |
the qualities of each bases in the reads. |
Value
will return a list containing for each read: - count, mean_quality, indexes
summarise_result
Description
summarise_result calculate the MCC score given the SNP sets, training, validation and metadata(s).
Usage
summarise_result(
snp_sets,
training_seqs,
validation_seqs,
training_metadata,
validation_metadata,
priority,
is_multi = TRUE,
return_all_intermediate = FALSE,
is_percent = FALSE
)
Arguments
snp_sets |
the dataframe containing the truth and predicted target |
training_seqs |
the training sequences |
validation_seqs |
the validation sequences |
training_metadata |
the training metadata |
validation_metadata |
the validation metadata |
priority |
the priority of the target, generated by |
is_multi |
Whether to use MCC-multi or MCC |
return_all_intermediate |
whether to return all intermediate values, only possible for binary class |
is_percent |
whether to return result by considering all the profiles having a GOI as target profile |
Value
Will return the summarised result
summary.binomial_naive_bayes
Description
summary.binomial_naive_bayes is an implementation of the summary method for the binomial naive bayes algorithm.
modified from bernoulli_naive_bayes function in the naivebayes package
Usage
## S3 method for class 'binomial_naive_bayes'
summary(object, ...)
Arguments
object |
a binomial_naive_bayes object |
... |
additional arguments |
Value
return a summary of the binomial_naive_bayes object
train_balk
Description
train_balk is a function that trains a binomial naive bayes classifier for sequence data
Usage
train_balk(
seqc,
snps_pos,
meta,
binomial_n = 1,
laplace = 1,
snp_id = NULL,
prior = NULL,
fit_prior = FALSE
)
Arguments
seqc |
A list of sequences |
snps_pos |
A vector of SNP positions |
meta |
A data frame containing the metadata, require isolate and target columns |
binomial_n |
The number of classes for the binomial naive bayes, default to 1 - bernoulli, 2 - binomial (support heterozygous SNPs) |
laplace |
The Laplace smoothing parameter |
snp_id |
A vector of SNP IDs, if not provided, it will be inferred from the SNP positions |
prior |
The prior probabilities of the classes |
fit_prior |
Whether to learn class prior probabilities |
Value
A list containing the classifier and the transformation levels
transform_snp
Description
transform_snp is a function that transforms a SNP into a matrix for binomial naive bayes.
Usage
transform_snp(pat, binomial_n, levels = c(), get = c("levels", "transformed"))
Arguments
pat |
A string of a SNP |
binomial_n |
The number of classes for the binomial naive bayes, default to 1 - bernoulli, 2 - binomial (support heterozygous SNPs) |
levels |
Existing transformation levels, if not provided, it will be inferred from the SNP |
get |
What to return, either "levels" or "transformed", or both |
Value
A vector of either the transformation levels or the transformed SNP or a list containing both
translate_position
Description
translate_position translate the scambled position in the alignment
to the original position or vice versa
Usage
translate_position(position, positions_table, to = "original")
Arguments
position |
the position to translate |
positions_table |
the table containing the original and scrambled positions |
to |
the direction to translate, either "original" or "scrambled" |
Value
the translated position
view_mcc
Description
view_mcc is used to present the result of selected SNPs set
based on the MCC score
Usage
view_mcc(result, ...)
Arguments
result |
is the result from |
... |
other optional parameters |
Value
formatted result list to be saved or presented.
view_mcc_multi
Description
view_mcc_multi is used to present the result of selected SNPs set
based on the multi-MCC score
Usage
view_mcc_multi(result, ...)
Arguments
result |
is the result from |
... |
other optional parameters |
Value
formatted result list to be saved or presented.
view_percent
Description
view_percent is used to present the result of selected
SNPs set based on Simpson's Index.
Usage
view_percent(result, ...)
Arguments
result |
is the result from |
... |
other optional parameters |
Value
formatted result list to be saved or presented.
view_simpson
Description
view_simpson is used to present the result of selected
SNPs set based on Simpson's Index.
Usage
view_simpson(result, ...)
Arguments
result |
is the result from |
... |
other optional parameters |
Value
formatted result list to be saved or presented.
write_fasta
Description
write_fasta is used to write
the named character vectors to fasta file.
Usage
write_fasta(seqc, filename)
Arguments
seqc |
a list containing list of nucleotides. To keep it simple, use provided read_fasta to import the fasta file. |
filename |
filename of the output file |
Value
will write the alignments to file