Package 'harrietr'

Return evolutionary distance in long format

Description

This will take an alignment, will calculate the evolutionary distance between all pairs of sequence, and will transform the distance matrix to long format. It will remove upper triangle, and diagonal elements, so you end with only (n)*(n-1)/2 rows, where n are the total number of rows in the distance matrix.

Usage

dist_long(aln, order = NULL, dist = "N", tree = NULL)

Arguments

aln	An object of class matrix, it must be square
order	A character vector of size n with the order of the columns and rows (default: NULL)
dist	A string naming the model to calculate distances (accepted values are those in ape::dist.dna)
tree	An object of class phylo

Details

If a tree is optionally given, a fourth column is returned with the cophenetic distance across all elements of tree. It assumes the tree was generated from the alignment.

Value

A data.frame with three or four columns: (1) iso1; (2) iso2: (3) dist. If a tree is given then a fourth column (evol_dist) containig the distances from the tree is also supplied.

Examples

## Not run: 
data(woodmouse)
dist_df <- dist_long(woodmouse)

## End(Not run)

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Get node support from a tree produced with IQTREE

Description

In IQTREE it is possible to obtain node support values by SH aproximate likelihood ratio tests (SH-aLRT), and ultrafast bootstraps (uBS). Often, we do both, which IQTREE encodes as two numbers separated by a '/' as the internal node label. This function will return a data.frame with the number of the internal nodes, and the support values for each.

Usage

get_node_support(tree)

Arguments

tree	An object of type phylo generated using IQTREE

Value

A data.frame with internal node information, plus two columns: (1) SH-aLRT; and (2) uBS

Examples

## Not run: 
data(woodmouse_iqtree)
get_node_support(woodmouse_iqtree)

## End(Not run)

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harrietr package

Description

harrietr: Wrangle Phylogenetic Distance Matrices and Other Utilities

Details

See the README on CRAN or GitHub

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Add metadata to long distance matrix

Description

This functions takes the output from dist_long, plus a data.frame with metadata, and attaches it to the data.frame output from dist_long. It uses a column in the metadata data.frame as a key to join the two data.frames. So, it requires a column of data in the metadata data.frame to have same ID labels as those in the pairwise comparison table.

Usage

join_metadata(dist, meta, isolate = "ISOLATES", group = "CLUSTER",
  remove_ind = TRUE, measure_col_contains = "dist")

Arguments

dist	A data.frame produced by dist_long function
meta	A data.frame with one column of IDs that match the IDs in dist_long
isolate	A character string with the name of the column in the meta data.frame with the ID data
group	A character string with the name of column containing the grouping variable
remove_ind	A boolean whether to remove all non-essential columns
measure_col_contains	A character string with a pattern that matches up with the measurement columns you wish to retain in the final output (default: 'dist')

Details

The output from dist_long with an additional column containing a factor, with levels composed of joining the categories in the group colum of the metadata data.frame for each pairwise comparison. For example, if one row has distance between samples id1 and id2, and in the grouping column of the metadata id1 is identified as part of group 'A', and id2 is identified as part of group 'B', then the output from that row will 'AB'. If they were from the same group, say 'A', the output would be just 'A'. In this way it is easy to identify pairs of isolates that are from the same group, and pairs of isolates that are from different groups.

Examples

## Not run: 
data(woodmouse)
data(woodmouse_meta)
dist_df <- dist_long(woodmouse)
join_metadata(dist_df, woodmouse_meta, isolate = 'SAMPLE_ID', group = 'CLUSTER', remove_ind = TRUE)

## End(Not run)

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Melt a square distance matrix into long format

Description

This will take a square distance matrix, and will transform in to long format. It will remove upper triangle, and diagonal elements, so you end with only (n)*(n-1)/2 rows, where n are the total number of rows in the distance matrix.

Usage

melt_dist(dist, order = NULL, dist_name = "dist")

Arguments

dist	An object of class matrix, it must be square
order	A character vector of size n with the order of the columns and rows (default: NULL)
dist_name	A string to name the distance column in the output (default: dist)

Value

A data.frame with three columns: (1) iso1; (2) iso2; (3) dist. iso1 and iso2 indicate the pair being compared, and dist indicates the distance between that pair.

Examples

## Not run: 
data(woodmouse)
dist <- ape::dist(woodmouse, model = 'N', as.matrix = TRUE)
dist_df <- melt_dist(dist)

## End(Not run)

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Woodmouse dataset

Description

Woodmouse dataset

Usage

woodmouse

Format

An object of class DNAbin with 15 rows and 965 columns.

Source

"ape" package woodmouse

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Woodmouse IQTREE newick tree

Description

Generated a multiFASTA, and used IQTREE to generate a tree with the following command:

Usage

woodmouse_iqtree

Format

An object of class phylo of length 5.

Details

iqtree -s woodmouse.fasta -m TEST -nt 4 -bb 1000 -alrt 1000

The tree was loaded into 'R' using 'ape::read.tree', and saved to a dataset.

Source

"ape" package woodmouse

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Woodmouse metadata

Description

A dummy metadata table generated to demonstrate the use of add_metadata.

Usage

woodmouse_meta

Format

An object of class tbl_df (inherits from tbl, data.frame) with 15 rows and 2 columns.

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