| Type: | Package |
| Title: | Viral Variant Location and Diversity |
| Version: | 1.0.1 |
| Description: | Analysis of minor alleles in Illumina sequencing data of viral genomes. Functions in 'vivaldi' primarily operate on vcf files. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/GreshamLab/vivaldi |
| BugReports: | https://github.com/GreshamLab/vivaldi/issues |
| Imports: | dplyr (≥ 1.0.2), ggplot2 (≥ 3.3.2), glue (≥ 1.4.2), magrittr (≥ 2.0.1), plotly (≥ 4.10.0), seqinr (≥ 4.2-8), tidyr (≥ 1.1.2), tidyselect (≥ 1.1.2), vcfR (≥ 1.12.0) |
| Suggests: | kableExtra, knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Config/testthat/edition: | 3 |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2023-03-21 19:27:42 UTC; esbor |
| Author: | Marissa Knoll [aut], Katherine Johnson [aut], Megan Hockman [aut], Eric Borenstein [aut], Mohammed Khalfan [aut], Elodie Ghedin [aut], David Gresham [aut, cre, cph] |
| Maintainer: | David Gresham <dg107@nyu.edu> |
| Repository: | CRAN |
| Date/Publication: | 2023-03-21 20:10:02 UTC |
add_metadata
Description
Adds metadata information to the vcf dataframe
Usage
add_metadata(df, metadf, by_vcf, by_meta)
Arguments
df |
A rearranged vcf dataframe (arrange_data) |
metadf |
A metadata dataframe |
by_vcf |
A vector of column names in the vcf dataframe that should be used to merge the vcf data with the metadata |
by_meta |
A vector of column names in the metadata dataframe that should be used to merge the metadata with the vcf data |
Value
A vcf dataframe with metadata included
Examples
df <- data.frame(CHROM = c("A", "B"),
POS = c(234, 240),
REF = c("G", "A"),
ALT = c("A", "G")
)
sizes <- data.frame(segment = c("A", "B"),
SegmentSize = c(2280, 2274)
)
df
sizes
# Add a new column of sizes of the segments which are necessary for
# downstream calculations such as transition:transversion (tstv) and dNdS.
add_metadata(df, sizes, c('CHROM'), c('segment'))
af_distribution
Description
Plots distribution of all minor variants
Usage
af_distribution(df)
Arguments
df |
A dataframe that has been arranged (arrange_data) and filtered (filter_variants) |
Value
plots with the distribution of all minor variants
Examples
# Example 1:
df <- data.frame(sample = c("m1", "m2", "m1", "m2", "m1"),
CHROM = c("PB1", "PB1", "PB2", "PB2", "NP"),
POS = c(234, 234, 240, 240, 254),
REF = c("G", "G", "A", "A", "C"),
ALT = c("A", "A", "G", "G", "T"),
minorfreq = c(0.010, 0.022, 0.043, 0.055, 0.011),
majorfreq = c(0.990, 0.978, 0.957, 0.945, 0.989),
minorcount = c(7, 15, 26, 32, 7),
majorcount = c(709, 661, 574, 547, 610),
gt_DP = c(716, 676, 600, 579, 617)
)
af_distribution(df)
# Example 2:
af_distribution(example_filtered_SNV_df)
arrange_data
Description
Reads in a directory of VCF files and converts them into a single dataframe
Usage
arrange_data(
vardir,
reference_fasta,
annotated = "yes",
ntlist = c("A", "G", "T", "C", "-"),
verbose = FALSE
)
Arguments
vardir |
Directory path containing vcf files |
reference_fasta |
Reference fasta file used for alignment |
annotated |
Whether the VCF files are annotated using snpeff "yes" or "no" (default "yes") |
ntlist |
Nucleotides (default A, T, G, C) used for finding multiple alt alleles |
verbose |
set verbosity of the vcfR commands |
Value
A large dataframe containing information from all input VCF files
dNdS_segment
Description
Reads in a dataframe that has been arranged (arrange_data), filtered (filter_variants), and annotated (prepare_annotations), calculates dNdS, and outputs plots
Usage
dNdS_segment(annotation_df, SPLICEFORMS)
Arguments
annotation_df |
A rearranged, filtered, and annotated vcf dataframe - must be for amino-acid specific calculations, cannot be the same as the dataframe used for SNP calculations |
SPLICEFORMS |
A character vector of isoform names |
Value
A plot showing the dN/dS ratio for each splice form (rather than segment) for each sample
Examples
# Sample Data
head(example_filtered_SNV_df)
dim(example_filtered_SNV_df)
# Plot showing the dN/dS ratio for each splice form
SPLICEFORMS = c("H1N1_PB2.1", "H1N1_PB1.1","H1N1_NS.2")
dNdS_segment(example_filtered_SNV_df, SPLICEFORMS)
Example Dataframe The DF_filt_SNVs dataframe created in the vignette
Description
Example Dataframe The DF_filt_SNVs dataframe created in the vignette
Usage
example_filtered_SNV_df
Format
## 'example_filtered_SNV_df' A data frame with 735 rows and 57 columns:
filter_variants
Description
Filters single-nucleotide variants using a coverage and frequency cutoff
Usage
filter_variants(df, coverage_cutoff = 200, frequency_cutoff = 0.03)
Arguments
df |
A rearranged VCF dataframe (rearranged using the arrange_data function) |
coverage_cutoff |
The coverage cutoff for calling a SNV (default: 200x) |
frequency_cutoff |
Frequency cutoff for calling a SNV (default: 3%) |
Value
A filtered VCF dataframe
Examples
df <- data.frame(CHROM = c("A", "B", "C"),
POS = c(234, 240, 255),
ALT_FREQ = c(0.016, 0.049, 0.031),
gt_DP = c(716, 600, 187)
)
df
# Default: filter by 3% frequency threshold and 200 coverage cutoff
filter_variants(df)
# Example 1: A 1% allele frequency threshold and 200 coverage cutoff
filter_variants(df, coverage_cutoff = 200, frequency_cutoff = 0.01)
# Example 2: A 2% allele frequency threshold and 100 coverage cutoff
filter_variants(df, coverage_cutoff = 100, frequency_cutoff = 0.02)
merge_replicates
Description
Merges replicate VCF files into a single dataframe
Usage
merge_replicates(vardf, repdata, nameofrep1, nameofrep2, commoncols)
Arguments
vardf |
Data frame of variants |
repdata |
Data frame of replicate information |
nameofrep1 |
Name of variable representing the first replicate, must be written with quotes |
nameofrep2 |
Name of variable representing the second replicate |
commoncols |
List of columns to merge the replicates by |
Value
a data frame containing replicate information
Examples
df <- data.frame(sample = c("m1", "m2", "m1", "m2", "m1"),
CHROM = c("PB1", "PB1", "PB2", "PB2", "NP"),
POS = c(234, 234, 240, 240, 254),
REF = c("G", "G", "A", "A", "C"),
ALT = c("A", "A", "G", "G", "T"),
minorfreq = c(0.010, 0.022, 0.043, 0.055, 0.011),
majorfreq = c(0.990, 0.978, 0.957, 0.945, 0.989),
minorcount = c(7, 15, 26, 32, 7),
majorcount = c(709, 661, 574, 547, 610),
gt_DP = c(716, 676, 600, 579, 617)
)
# Dataframe shows a pair of replicates and their variants at 3 positions.
df
replicates <- data.frame(filename = c("m1","m2"),
replicate = c("rep1", "rep2"),
sample = c("a_2_iv", "a_2_iv")
)
# Dataframe showing relationship between filename, replicate, and sample name
replicates
# Merge by the following columns
cols = c("sample","CHROM","POS","REF","ALT")
merge_replicates(df, replicates, "rep1", "rep2", cols)
# The dataframe now contains the 2 variants at positions 234 & 240 that were
# detected in both sequencing replicates whereas the variant at position 254
# was only in a single replicate so it was removed during the merge.
plot_shannon
Description
Reads in a dataframe that has been arranged (arrange_data), filtered (filter_variants), and piped through the Shannon calculations (shannon_entropy) and outputs plots
Usage
plot_shannon(shannon_df)
Arguments
shannon_df |
A dataframe that has been arranged (arrange_data), filtered (filter_variants), and piped through the Shannon calculations (shannon_entropy) |
Details
The 'plot_shannon()' function takes the variant dataframe and generates three plots. 1. The Shannon entropy, or amount of diversity, at each position in the genome at which a variant was found. 2. The Shannon entropy summed over each segment 3. The Shannon entropy summed over each genome A higher value indicates more diversity.
Value
Three plots showing the nt Shannon, chrom Shannon, and full genome Shannon calculations
Examples
# Sample dataframe
df <- data.frame(sample = c("m1", "m2", "m1", "m2", "m1"),
CHROM = c("PB1", "PB1", "PB2", "PB2", "NP"),
POS = c(234, 234, 240, 240, 254),
minorfreq = c(0.010, 0.022, 0.043, 0.055, 0.011),
majorfreq = c(0.990, 0.978, 0.957, 0.945, 0.989),
SegmentSize = c(2280, 2280, 2274, 2274, 1809)
)
df
genome_size = 13133
# Modify the dataframe to add 5 new columns of shannon entropy data:
# 1. shannon_ntpos
# 2. chrom_shannon
# 3. genome_shannon
# 4. shannon_chrom_perkb
# 5. genome_shannon_perkb
shannon_df = shannon_entropy(df, genome_size)
# Plot
plot_shannon(shannon_df)
position_allele_freq
Description
Reads in a dataframe that has been arranged (arrange_data) and filtered (filter_variants) and outputs plots
Usage
position_allele_freq(vardf, segment, nt)
Arguments
vardf |
A rearranged (arrange_data) and filtered (filtered_variants) vcf dataframe |
segment |
Name of segment (must be in quotes) |
nt |
Position on segment (must be in quotes) |
Value
A plot showing the the frequencies of the major and minor allele at the given position across all samples
Examples
position_allele_freq(example_filtered_SNV_df,"H1N1_NP", "1247")
prepare_annotations
Description
Separates the SNPeff annotations found in an annotated and rearranged VCF dataframe (arranged using arrange_data)
Usage
prepare_annotations(df)
Arguments
df |
A rearranged and annotated VCF dataframe |
Value
A dataframe containing each annotation on a separate column
Examples
# Example: Shows the separation of the ANN column based on | delimiter.
test <- data.frame( ANN = c("A|B|C|D|E|F|G|H|I|J|K|L|M|N|O|P"))
# The ANN column will be split based on the strings in `snpeff_info()` and
# an additional "error" column.
snpeff_info()
# Split the SNPeff annotations in "ANN" column and save to dataframe `df`
df <- prepare_annotations(df)
# The one "ANN" column is split into 16 columns
dim(test)
dim(df)
read_reference_fasta_dna
Description
Imports reference fasta, generates a dataframe with chroms and chrom lengths
Usage
read_reference_fasta_dna(reference_fasta)
Arguments
reference_fasta |
The name and location of the reference fasta file used for alignment |
Value
A dataframe containing the chroms and chrom lengths of a reference fasta
shannon_entropy
Description
Takes a rearranged vcf dataframe and calculates the Shannon entropy
Usage
shannon_entropy(df, genome_size)
Arguments
df |
A rearranged vcf dataframe (arrange_data) |
genome_size |
Size of whole genome being used |
Details
Shannon entropy is a commonly used metric to describe the amount of genetic diversity in sequencing data. It is calculated by considering the frequency of the ALT and REF allele at every position and then summing those values over 1) a segment or 2) the entire genome. These values can then be normalized by sequence length (kb) in order to compare across different segments or samples.
Value
A dataframe with Shannon entropy/kb calculations for the chroms and entire genome
Examples
# Sample dataframe
df <- data.frame(sample = c("m1", "m2", "m1", "m2", "m1"),
CHROM = c("PB1", "PB1", "PB2", "PB2", "NP"),
minorfreq = c(0.010, 0.022, 0.043, 0.055, 0.011),
majorfreq = c(0.990, 0.978, 0.957, 0.945, 0.989),
SegmentSize = c(2280, 2280, 2274, 2274, 1809)
)
df
genome_size = 13133
# MOdify the dataframe to add 5 new columns of shannon entropy data:
# 1. shannon_ntpos
# 2. chrom_shannon
# 3. genome_shannon
# 4. shannon_chrom_perkb
# 5. genome_shannon_perkb
shannon_entropy(df, genome_size)
shared_snv_plot
Description
Reads in a dataframe that has been arranged (arrange_data) and filtered (filter_variants) and outputs plots
Usage
shared_snv_plot(vardf, samples = unique(DF_filt$sample))
Arguments
vardf |
A rearranged (arrange_data) and filtered (filtered_variants) vcf dataframe |
samples |
A vector of samples to be compared (default:all samples in DF_filt) |
Value
A plot showing the location of variants and the number of samples that contain each variant
Examples
samples = c("a_1_fb", "a_1_iv", "a_2_fb", "a_2_iv", "a_3_fb", "a_3_iv", "b_1_fb", "b_1_iv")
shared_snv_plot(example_filtered_SNV_df, samples)
shared_snv_table
Description
Reads in a dataframe that has been arranged (arrange_data) and filtered (filter_variants) and outputs a table
Usage
shared_snv_table(vardf)
Arguments
vardf |
A rearranged (arrange_data) and filtered (filtered_variants) vcf dataframe |
Details
The 'shared_snv_table()' function takes the variant dataframe and creates a new table, listing the variants in descending order of frequency how many samples they are found in. This function is meant to simplify further investigation of visual patterns in the previous plot.
Value
A table listing variants in order by how many samples they are found in
Examples
# Sample dataframe has 57 columns
dim(example_filtered_SNV_df)
# Simplify sample dataframe
df <- shared_snv_table(example_filtered_SNV_df)
# Dataframe created has 15 columns
df
dim(df)
snpeff_info
Description
Returns vector containing information in snpeff annotations
Usage
snpeff_info()
Value
Returns vector containing information in snpeff annotations
Examples
snpeff_info()
snv_genome
Description
Reads in a dataframe that has been arranged (arrange_data) and filtered (filter_variants) and outputs plots
Usage
snv_genome(vardf)
Arguments
vardf |
A rearranged (arrange_data) and filtered (filtered_variants) vcf dataframe |
Value
A bar plot showing the number of variants per sample colored by their SNPEff annotation
Examples
# Example 1: Simple dataframe
df <- data.frame(sample = c("m1", "m1", "m1", "m1", "m1",
"m2", "m2", "m2", "m2", "m2"),
annotation = c("downstrean_gene_variant", "synonymous_variant",
"synonymous_variant", "stop_gained",
"missense_variant", "downstrean_gene_variant",
"downstrean_gene_variant", "synonymous_variant",
"stop_gained", "missense_variant")
)
df
snv_genome(df)
# Example 2: Sample dataframe
snv_genome(example_filtered_SNV_df)
snv_location
Description
Reads in the vcf dataframe and generates a plot showing the frequency and location of SNVs
Usage
snv_location(df)
Arguments
df |
A rearranged dataframe |
Value
A plot showing the location and frequency of SNVs found across samples
Examples
# Example 1:
df <- data.frame(sample = c("m1", "m1", "m1", "m1", "m1",
"m2", "m2", "m2", "m2", "m2"),
CHROM = c("PB1", "PB1", "PB2", "PB2", "PB2",
"PB1", "PB1", "PB2", "PB2", "PB2"),
POS = c(234, 266, 117, 134, 180,
234, 266, 199, 88, 180),
major = c("G", "G", "A", "A", "C",
"G", "G", "A", "G", "C"),
minor = c("A", "A", "G", "G", "T",
"A", "A", "G", "A", "T"),
ALT_TYPE = c("minor", "minor", "minor", "minor", "minor",
"minor", "minor", "minor", "major", "minor"),
minorfreq = c(0.010, 0.022, 0.043, 0.055, 0.011,
0.010, 0.022, 0.043, 0.055, 0.011),
majorfreq = c(0.990, 0.978, 0.957, 0.945, 0.989,
0.990, 0.978, 0.957, 0.945, 0.989)
)
df
snv_location(df)
# Example 2:
snv_location(example_filtered_SNV_df)
snv_segment
Description
Reads in a dataframe that has been arranged (arrange_data) and filtered (filter_variants) and outputs plots
Usage
snv_segment(vardf)
Arguments
vardf |
A rearranged (arrange_data) and filtered (filtered_variants) vcf dataframe |
Value
A bar plot showing the number of variants colored by their SNPEff annotation
Examples
# Example 1: Simple data
df <- data.frame(sample = c("m1", "m1", "m1", "m1", "m1",
"m2", "m2", "m2", "m2", "m2"),
CHROM = c("PB1", "PB1", "PB2", "PB2", "PB2",
"PB1", "PB1", "PB2", "PB2", "PB2"),
annotation = c("downstrean_gene_variant", "synonymous_variant",
"synonymous_variant", "stop_gained", "missense_variant",
"downstrean_gene_variant", "downstrean_gene_variant",
"synonymous_variant", "stop_gained", "missense_variant")
)
df
snv_segment(df)
# Example 2: Sample data
snv_segment(example_filtered_SNV_df)
tally_it
Description
Groups the input vcf data frame using a list of variables and tallies the number of occurrences
Usage
tally_it(df, groupit, new_colname)
Arguments
df |
A rearranged vcf dataframe (arrange_data) |
groupit |
A vector containing column names that data should be grouped by |
new_colname |
The name of the count column |
Value
A dataframe with columns from the 'groupit' vector and the number of times each unique grouping occurs in the data
Examples
# Sample dataframe of 7 variants across 2 samples
df <- data.frame(
sample = c( "sample1", "sample1", "sample1", "sample2",
"sample2", "sample2", "sample2"),
CHROM = c("PB1", "PB2", "PB2", "LEO", "LEO", "LEO", "ALE"),
SegmentSize = c(2280, 2274, 2274, 1701, 1701, 1701, 1888 ),
minorfreq = c(0.04422785, 0.03738175, 0.01390202, 0.02927786,
0.03071955, 0.02626025, 0.02875321)
)
# Example 1: to get the sum of variants on every segment:
groupit = c('sample','CHROM', "SegmentSize")
tally_it(df, groupit, "snv_count")
# Example 2: to get the count across genomes:
groupit = c('sample')
tally_it(df, groupit, "snv_count")
tstv_plot
Description
Plots Ts/Tv ratios
Usage
tstv_plot(df)
Arguments
df |
TsTv dataframe generated using the tstv_ratio function |
Value
two plots showing the K2P and simple Ts/Tv ratios
Examples
df <- tstv_ratio(example_filtered_SNV_df,1300)
tstv_plot(df)
tstv_ratio
Description
Inputs a filtered and rearranged vcf dataframe and calculates the transition/transversion ratio
Usage
tstv_ratio(df, genome_size)
Arguments
df |
The filtered and rearranged variant dataframe |
genome_size |
Size of whole genome being used |
Value
A dataframe containing the calculated transition/transversion ratio (R or basic_tstv)
Examples
tstv_ratio(example_filtered_SNV_df, 13000)