Package 'vivaldi' reference manual

Title:	Viral Variant Location and Diversity
Description:	Analysis of minor alleles in Illumina sequencing data of viral genomes. Functions in 'vivaldi' primarily operate on vcf files.
Authors:	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 <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.1
Built:	2025-03-12 05:18:00 UTC
Source:	https://github.com/greshamlab/vivaldi

add_metadata

Description

Adds metadata information to the vcf dataframe

Usage

add_metadata(df, metadf, by_vcf, by_meta)
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'))

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)
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)

# 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
)
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)
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)
# 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
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)
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)


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)
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.

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)
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)

# 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)
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")
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)
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)

# 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)
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)
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)

# 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$sample))
shared_snv_plot(vardf, samples = unique(DF_filt$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)

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, annotated = "yes")
shared_snv_table(vardf, annotated = "yes")

Arguments

`vardf`	A rearranged (arrange_data) and filtered (filtered_variants) vcf dataframe
`annotated`	Whether the VCF files are annotated using snpeff "yes" or "no" (default "yes")

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)

# 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()
snpeff_info()

Value

Returns vector containing information in snpeff annotations

Examples

snpeff_info()
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)
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)

# 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)
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)

# 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)
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)

# 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)
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")

# 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)
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)
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)
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)
tstv_ratio(example_filtered_SNV_df, 13000)

Package 'vivaldi'

Help Index

add_metadata

Description

Usage

Arguments

Value

Examples

af_distribution

Description

Usage

Arguments

Value

Examples

arrange_data

Description

Usage

Arguments

Value

dNdS_segment

Description

Usage

Arguments

Value

Examples

Example Dataframe The DF_filt_SNVs dataframe created in the vignette

Description

Usage

Format

filter_variants

Description

Usage

Arguments

Value

Examples

merge_replicates

Description

Usage

Arguments

Value

Examples

plot_shannon

Description

Usage

Arguments

Details

Value

Examples

position_allele_freq

Description

Usage

Arguments

Value

Examples

prepare_annotations

Description

Usage

Arguments

Value

Examples

read_reference_fasta_dna

Description

Usage

Arguments

Value

shannon_entropy

Description

Usage

Arguments

Details

Value

Examples

shared_snv_plot

Description

Usage

Arguments

Value

Examples

shared_snv_table

Description