Version: 1.10.0
Title: Various Utilities for Microbial Genomics and Metagenomics
Description: A collection of functions for microbial ecology and other applications of genomics and metagenomics. Companion package for the Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016 <doi:10.7287/peerj.preprints.1900v1>).
Author: Luis M. Rodriguez-R [aut, cre]
Maintainer: Luis M. Rodriguez-R <lmrodriguezr@gmail.com>
URL: http://enve-omics.ce.gatech.edu/enveomics/
Depends: R (≥ 2.9), stats, methods, parallel, fitdistrplus, sn, investr
Suggests: tools, vegan, ape, picante, gplots, optparse
License: Artistic-2.0
LazyData: yes
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-02-11 17:38:43 UTC; miguel
Repository: CRAN
Date/Publication: 2025-02-11 17:50:06 UTC

Attribute accessor

Description

Attribute accessor

Usage

## S4 method for signature 'enve.GrowthCurve'
x$name

Arguments

x

Object

name

Attribute name

Attribute accessor

Description

Attribute accessor

Usage

## S4 method for signature 'enve.RecPlot2'
x$name

Arguments

x

Object

name

Attribute name

Attribute accessor

Description

Attribute accessor

Usage

## S4 method for signature 'enve.RecPlot2.Peak'
x$name

Arguments

x

Object

name

Attribute name

Enveomics: Growth Curve S4 Class

Description

Enve-omics representation of fitted growth curves.

Slots

design

(array) Experimental design of the experiment.

models

(list) Fitted growth curve models.

predict

(list) Fitted growth curve values.

call

(call) Call producing this object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) - S4 Class

Description

Enve-omics representation of Recruitment plots. This object can be produced by enve.recplot2 and supports S4 method plot.

Slots

counts

(matrix) Counts as a two-dimensional histogram.

pos.counts.in

(numeric) Counts of in-group hits per position bin.

pos.counts.out

(numeric) Counts of out-group hits per position bin.

id.counts

(numeric) Counts per ID bin.

id.breaks

(numeric) Breaks of identity bins.

pos.breaks

(numeric) Breaks of position bins.

pos.names

(character) Names of the position bins.

seq.breaks

(numeric) Breaks of input sequences.

peaks

(list) Peaks identified in the recplot. Limits of the subject sequences after concatenation.

seq.names

(character) Names of the subject sequences.

id.metric

(character) Metric used as 'identity'.

id.ingroup

(logical) Identity bins considered in-group.

call

(call) Call producing this object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Peak - S4 Class

Description

Enve-omics representation of a peak in the sequencing depth histogram of a Recruitment plot (see enve.recplot2.findPeaks).

Slots

dist

(character) Distribution of the peak. Currently supported: norm (normal) and sn (skew-normal).

values

(numeric) Sequencing depth values predicted to conform the peak.

values.res

(numeric) Sequencing depth values not explained by this or previously identified peaks.

mode

(numeric) Seed-value of mode anchoring the peak.

param.hat

(list) Parameters of the distribution. A list of two values if dist=norm (sd and mean), or three values if dist=sn(omega=scale, alpha=shape, and xi=location). Note that the "dispersion" parameter is always first and the "location" parameter is always last.

n.hat

(numeric) Number of bins estimated to be explained by this peak. This should ideally be equal to the length of values, but it's not an integer.

n.total

(numeric) Total number of bins from which the peak was extracted. I.e., total number of position bins with non-zero sequencing depth in the recruitment plot (regardless of peak count).

err.res

(numeric) Error left after adding the peak (mower) or log-likelihood (em or emauto).

merge.logdist

(numeric) Attempted merge.logdist parameter.

seq.depth

(numeric) Best estimate available for the sequencing depth of the peak (centrality).

log

(logical) Indicates if the estimation was performed in natural logarithm space.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS S4 Class

Description

Enve-omics representation of "Transformed-space Resampling In Biased Sets (TRIBS)". This object represents sets of distances between objects, sampled nearly-uniformly at random in "distance space". Subsampling without selection is trivial, since both the distances space and the selection occur in the same transformed space. However, it's useful to compare randomly subsampled sets against a selected set of objects. This is intended to identify overdispersion or overclustering (see enve.TRIBStest) of a subset against the entire collection of objects with minimum impact of sampling biases. This object can be produced by enve.tribs and supports S4 methods plot and summary.

Slots

distance

(numeric) Centrality measurement of the distances between the selected objects (without subsampling).

points

(matrix) Position of the different objects in distance space.

distances

(matrix) Subsampled distances, where the rows are replicates and the columns are subsampling levels.

spaceSize

(numeric) Number of objects.

selSize

(numeric) Number of selected objects.

dimensions

(numeric) Number of dimensions in the distance space.

subsamples

(numeric) Subsampling levels (as fractions, from 0 to 1).

call

(call) Call producing this object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS Merge

Description

Merges two enve.TRIBS objects generated from the same objects at different subsampling levels.

Usage

enve.TRIBS.merge(x, y)

Arguments

x

First enve.TRIBS object.

y

Second enve.TRIBS object.

Value

Returns an enve.TRIBS object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS Test S4 Class

Description

Test of significance of overclustering or overdispersion in a selected set of objects with respect to the entire set (see enve.TRIBS). This object can be produced by enve.tribs.test and supports S4 methods plot and summary.

Slots

pval.gt

(numeric) P-value for the overdispersion test.

pval.lt

(numeric) P-value for the overclustering test.

all.dist

(numeric) Empiric PDF of distances for the entire dataset (subsampled at selection size).

sel.dist

(numeric) Empiric PDF of distances for the selected objects (without subsampling).

diff.dist

(numeric) Empiric PDF of the difference between all.dist and sel.dist. The p-values are estimating by comparing areas in this PDF greater than and lesser than zero.

dist.mids

(numeric) Midpoints of the empiric PDFs of distances.

diff.mids

(numeric) Midpoints of the empiric PDF of difference of distances.

call

(call) Call producing this object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Prune Iter (Internal Function)

Description

Internal function for enve.prune.dist.

Usage

enve.__prune.iter(t, dist, min_dist, quiet)

Arguments

t

A phylo object.

dist

Cophenetic distance matrix.

min_dist

Minimum distance.

quiet

If running quietly.

Value

Returns a phylo object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Prune Reduce (Internal Function)

Description

Internal function for enve.prune.dist.

Usage

enve.__prune.reduce(t, nodes, min_dist, quiet)

Arguments

t

A phylo object.

nodes

Vector of nodes.

min_dist

Minimum distance.

quiet

If running quietly.

Value

A phylo object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS - Internal Ancillary Function

Description

Internal ancillary function (see enve.tribs).

Usage

enve.__tribs(
  rep,
  frx,
  selection,
  dimensions,
  dots,
  dist.method,
  summary.fx,
  dist
)

Arguments

rep

Replicates

frx

Fraction

selection

Selection

dimensions

Dimensions

dots

Sampling points

dist.method

Distance method

summary.fx

Summary function

dist

Distance

Value

A numeric indicating the summary.fx value applied to the distance matrix subset

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Barplot

Description

Creates nice barplots from tab-delimited tables.

Usage

enve.barplot(
  x,
  sizes,
  top = 25,
  colors.per.group = 9,
  bars.width = 4,
  legend.ncol = 1,
  other.col = "#000000",
  add.trend = FALSE,
  organic.trend = FALSE,
  sort.by = median,
  min.report = 101,
  order = NULL,
  col,
  ...
)

Arguments

x

Can be either the input data or the path to the file containing the table.

  • If it contains the data, it must be a data frame or an object coercible to a data frame.

  • If it is a path, it must point to a tab-delimited file containing a header (first row) and row names (first column).

sizes

A numeric vector containing the real size of the samples (columns) in the same order of the input table. If set, the values are assumed to be 100%. Otherwise, the sum of the columns is used.

top

Maximum number of categories to display. Any additional categories will be listed as "Others".

colors.per.group

Number of categories in the first two saturation groups of colors. The third group contains the remaining categories if needed.

bars.width

Width of the barplot with respect to the legend.

legend.ncol

Number of columns in the legend.

other.col

Color of the "Others" category.

add.trend

Controls if semi-transparent areas are to be plotted between the bars to connect the regions (trend regions).

organic.trend

Controls if the trend regions are to be smoothed (curves). By default, trend regions have straight edges. If TRUE, forces add.trend=TRUE.

sort.by

Any function that takes a numeric vector and returns a numeric scalar. This function is applied to each row, and the resulting values are used to sort the rows (decreasingly). Good options include: sd, min, max, mean, median.

min.report

Minimum percentage to report the value in the plot. Any value above 100 indicates that no values are to be reported.

order

Controls how the rows should be ordered.

  • If NULL (default), sort.by is applied per row and the results are sorted decreasingly.

  • If NA, no sorting is performed, i.e., the original order is respected.

  • If a vector is provided, it is assumed to be the custom order to be used (either by numeric index or by row names).

col

Colors to use. If provided, overrides the variables top and colors.per.group, but other.col is still used if the vector is insufficient for all the rows. An additional palette is available with col='coto' (contributed by Luis (Coto) Orellana).

...

Any additional parameters to be passed to barplot.

Value

No return value

Author(s)

Luis M. Rodriguez-R [aut, cre]

Examples

# Load data
data("phyla.counts", package = "enveomics.R", envir = environment())
# Create a barplot sorted by variance with organic trends
enve.barplot(
  phyla.counts, # Counts of phyla in four sites
  sizes = c(250,100,75,200), # Total sizes of the datasets of each site
  bars.width = 2, # Decrease from default, so the names are fully displayed
  organic.trend = TRUE, # Nice curvy background
  sort.by = var # Sort by variance across sites
)

Enveomics: Cliopts

Description

Generates nicely formatted command-line interfaces for functions (closures only).

Usage

enve.cliopts(
  fx,
  rd_file,
  positional_arguments,
  usage,
  mandatory = c(),
  vectorize = c(),
  ignore = c(),
  number = c(),
  defaults = list(),
  o_desc = list(),
  p_desc = ""
)

Arguments

fx

Function for which the interface should be generated.

rd_file

(Optional) .Rd file with the standard documentation of the function.

positional_arguments

(Optional) Number of positional arguments passed to parse_args (package: optparse).

usage

(Optional) Usage passed to OptionParser (package: optparse).

mandatory

Mandatory arguments.

vectorize

Arguments of the function to vectorize (comma-delimited). If numeric, use also number.

ignore

Arguments of the function to ignore.

number

Force these arguments as numerics. Useful for numeric vectors (see vectorize) or arguments with no defaults.

defaults

Defaults to use instead of the ones provided by the formals.

o_desc

Descriptions of the options. Help from rd is ignored for arguments present in this list.

p_desc

Description Description of the function. Help from rd is ignored for the function description unless this value is an empty string.

Value

Returns a list with keys:

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Color Alpha

Description

Modify alpha in a color (or vector of colors).

Usage

enve.col.alpha(col, alpha = 1/2)

Arguments

col

Color or vector of colors. It can be any value supported by col2rgb, such as darkred or #009988.

alpha

Alpha value to add to the color, from 0 to 1.

Value

Returns a color or a vector of colors in hex notation, including alpha.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Examples

# Hexcode for a color by hexcode
enve.col.alpha("#009988", 3/4) # "#009988BF"

# Hexcode for a color by name
enve.col.alpha("white", 1/4) # "#FFFFFF3F"

# Hexcode for a color from other functions
enve.col.alpha(rainbow(3)) # "#FF00007F" "#00FF007F" "#0000FF7F"

Enveomics: Color to Alpha (deprecated)

Description

Takes a vector of colors and sets the alpha.

Usage

enve.col2alpha(x, alpha)

Arguments

x

A vector of any value base colors.

alpha

Alpha level to set, in the [0, 1] range.

Details

DEPRECATED: Use instead enve.col.alpha.

Value

A vector of colors with alpha set.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Data Frame to Dist

Description

Transform a dataframe (or coercible object, like a table) into a dist object.

Usage

enve.df2dist(
  x,
  obj1.index = 1,
  obj2.index = 2,
  dist.index = 3,
  default.d = NA,
  max.sim = 0
)

Arguments

x

A dataframe (or coercible object) with at least three columns:

  1. ID of the object 1,

  2. ID of the object 2, and

  3. distance between the two objects.

obj1.index

Index of the column containing the ID of the object 1.

obj2.index

Index of the column containing the ID of the object 2.

dist.index

Index of the column containing the distance.

default.d

Default value (for missing values).

max.sim

If not zero, assumes that the values are similarity (not distance) and this is the maximum similarity (corresponding to distance 0). Applies transformation: distance = (max.sim - values)/max.sim.

Value

Returns a dist object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Examples

# A sparse matrix representation of similarities as data frame.
# The column "extra_data" is meaningless, only included to illustrate
# the use of the obj*.index parameters
sim <- data.frame(
  extra_data = c(0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.5),
  query      = c("A", "A", "A", "B", "C", "C", "D"),
  subject    = c("A", "B", "C", "B", "C", "B", "A"),
  similarity = c(100,  90,  60, 100, 100,  70,  10)
)
dist <- enve.df2dist(sim, "query", "subject", "similarity", max.sim = 100)
print(dist)

Enveomics: Data Frame to Dist (Group)

Description

Transform a dataframe (or coercible object, like a table) into a dist object, where there are 1 or more distances between each pair of objects.

Usage

enve.df2dist.group(
  x,
  obj1.index = 1,
  obj2.index = 2,
  dist.index = 3,
  summary = median,
  empty.rm = TRUE
)

Arguments

x

A dataframe (or coercible object) with at least three columns:

  1. ID of the object 1,

  2. ID of the object 2, and

  3. distance between the two objects.

obj1.index

Index of the column containing the ID of the object 1.

obj2.index

Index of the column containing the ID of the object 2.

dist.index

Index of the column containing the distance.

summary

Function summarizing the different distances between the two objects.

empty.rm

Remove rows with empty or NA groups.

Value

Returns a dist object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Examples

# A sparse matrix representation of distances as data frame.
# Note that some pairs are repeated.
dist.df <- data.frame(
  query    = c("A", "A", "A", "B", "C", "C", "B", "B", "B"),
  subject  = c("A", "B", "C", "B", "C", "B", "A", "C", "C"),
  distance = c(  0, 0.1, 0.4,   0,   0, 0.4, 0.2, 0.2, 0.1)
)
dist <- enve.df2dist.group(dist.df)
print(dist)

# Use the mean of all repeated occurrences instead of the median.
dist <- enve.df2dist.group(dist.df, summary = mean)

# Simply use the first occurrence for any given pair.
dist <- enve.df2dist.group(dist.df, summary = function(x) head(x, n = 1))

Enveomics: Data Frame to Dist (List)

Description

Transform a dataframe (or coercible object, like a table) into a list of dist objects, one per group.

Usage

enve.df2dist.list(
  x,
  groups,
  obj1.index = 1,
  obj2.index = 2,
  dist.index = 3,
  empty.rm = TRUE,
  ...
)

Arguments

x

A dataframe (or coercible object) with at least three columns:

  1. ID of the object 1,

  2. ID of the object 2, and

  3. distance between the two objects.

groups

Named array where the IDs correspond to the object IDs, and the values correspond to the group.

obj1.index

Index of the column containing the ID of the object 1.

obj2.index

Index of the column containing the ID of the object 2.

dist.index

Index of the column containing the distance.

empty.rm

Remove incomplete matrices.

...

Any other parameters supported by enve.df2dist.

Value

Returns a list of dist objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Growth Curve

Description

Calculates growth curves using the logistic growth function.

Usage

enve.growthcurve(
  x,
  times = 1:nrow(x),
  triplicates = FALSE,
  design,
  new.times = seq(min(times), max(times), length.out = length(times) * 10),
  level = 0.95,
  interval = c("confidence", "prediction"),
  plot = TRUE,
  FUN = function(t, K, r, P0) K * P0 * exp(r * t)/(K + P0 * (exp(r * t) - 1)),
  nls.opt = list(),
  ...
)

Arguments

x

Data frame (or coercible) containing the observed growth data (e.g., O.D. values). Each column is an independent growth curve and each row is a time point. NA's are allowed.

times

Vector with the times at which each row was taken. By default, all rows are assumed to be part of constantly periodic measurements.

triplicates

If TRUE, the columns are assumed to be sorted by sample with three replicates by sample. It requires a number of columns multiple of 3.

design

Experimental design of the data. An array of mode list with sample names as index and the list of column names in each sample as the values. By default, each column is assumed to be an independent sample if triplicates is FALSE, or every three columns are assumed to be a sample if triplicates is TRUE. In the latter case, samples are simply numbered.

new.times

Values of time for the fitted curve.

level

Confidence (or prediction) interval in the fitted curve.

interval

Type of interval to be calculated for the fitted curve.

plot

Should the growth curve be plotted?

FUN

Function to fit. By default: logistic growth with paramenters K: carrying capacity, r: intrinsic growth rate, and P0: Initial population.

nls.opt

Any additional options passed to nls.

...

Any additional parameters to be passed to plot.enve.GrowthCurve.

Value

Returns an enve.GrowthCurve object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Examples

# Load data
data("growth.curves", package = "enveomics.R", envir = environment())

# Generate growth curves with different colors
g <- enve.growthcurve(growth.curves[, -1], growth.curves[, 1],
                      triplicates = TRUE)

# Generate black-and-white growth curves with different symbols
plot(g, pch=15:17, col="black", band.density=45, band.angle=c(-45,45,0))

Enveomics: Pref Score

Description

Estimate preference score of species based on occupancy in biased sample sets

Usage

enve.prefscore(
  x,
  set,
  ignore = NULL,
  signif.thr,
  plot = TRUE,
  col.above = rgb(148, 17, 0, maxColorValue = 255),
  col.equal = rgb(189, 189, 189, maxColorValue = 255),
  col.below = rgb(47, 84, 150, maxColorValue = 255),
  ...
)

Arguments

x

Occupancy matrix (logical or numeric binary) with species as rows and samples as columns

set

Vector indicating samples in the test set. It can be any selection vector: boolean (same length as the number of columns in x), or numeric or character vector with indexes of the x columns.

ignore

Vector indicating species to ignore. It can be any selection vector with respect to the rows in x (see set).

signif.thr

Absolute value of the significance threshold

plot

Indicates if a plot should be generated

col.above

Color for points significantly above zero

col.equal

Color for points not significantly different from zero

col.below

Color for points significantly below zero

...

Any additional parameters supported by plot

Value

Returns a named vector of preference scores.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Prune Dist

Description

Automatically prunes a tree, to keep representatives of each clade.

Usage

enve.prune.dist(
  t,
  dist.quantile = 0.25,
  min_dist,
  quiet = FALSE,
  max_iters = 100,
  min_nodes_random = 40000,
  random_nodes_frx = 1
)

Arguments

t

A phylo object or a path to the Newick file.

dist.quantile

The quantile of edge lengths.

min_dist

The minimum distance to allow between two tips. If not set, dist.quantile is used instead to calculate it.

quiet

Boolean indicating if the function must run without output.

max_iters

Maximum number of iterations.

min_nodes_random

Minimum number of nodes to trigger tip-pairs nodes sampling. This sampling is less reproducible and more computationally expensive, but it's the only solution if the cophenetic matrix exceeds 2^31-1 entries; above that, it cannot be represented in R.

random_nodes_frx

Fraction of the nodes to be sampled if more than min_nodes_random.

Value

Returns a pruned phylo object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plots

Description

Produces recruitment plots provided that BlastTab.catsbj.pl has been previously executed. Requires the gplots library.

Usage

enve.recplot(
  prefix,
  id.min = NULL,
  id.max = NULL,
  id.binsize = NULL,
  id.splines = 0,
  id.metric = "id",
  id.summary = "sum",
  pos.min = 1,
  pos.max = NULL,
  pos.binsize = 1000,
  pos.splines = 0,
  rec.col1 = "white",
  rec.col2 = "black",
  main = NULL,
  contig.col = grey(0.85),
  ret.recplot = FALSE,
  ret.hist = FALSE,
  ret.mode = FALSE,
  id.cutoff = NULL,
  verbose = TRUE,
  ...
)

Arguments

prefix

Path to the prefix of the BlastTab.catsbj.pl output files. At least the files .rec and .lim must exist with this prefix.

id.min

Minimum identity to be considered. By default, the minimum detected identity. This value is a percentage.

id.max

Maximum identity to be considered. By default, 100%.

id.binsize

Size of the identity bins (vertical histograms). By default, 0.1 for identity metrics and 5 for bit score.

id.splines

Smoothing parameter for the splines in the identity histogram. Zero (0) for no splines. A generally good value is 1/2. If non-zero, requires the stats package.

id.metric

Metric of identity to be used (Y-axis). It can be any unambiguous prefix of:

  • "identity"

  • "corrected identity"

  • "bit score"

id.summary

Method used to build the identity histogram (Horizontal axis of the right panel). It can be any unambiguous prefix of:

  • "sum"

  • "average"

  • "median"

  • "90% lower bound"

  • "90% upper bound"

  • "95% lower bound"

  • "95% upper bound"

The last four options correspond to the upper and lower boundaries of the 90% and 95% empirical confidence intervals.

pos.min

Minimum (leftmost) position in the reference (concatenated) genome (in bp).

pos.max

Maximum (rightmost) position in the reference (concatenated) genome (in bp). By default: Length of the genome.

pos.binsize

Size of the position bins (horizontal histograms) in bp.

pos.splines

Smoothing parameter for the splines in the position histogram. Zero (0) for no splines. If non-zero, requires the stats package.

rec.col1

Lightest color in the recruitment plot.

rec.col2

Darkest color in the recruitment plot.

main

Title of the plot.

contig.col

Color of the Contig boundaries. Set to NA to ignore Contig boundaries.

ret.recplot

Indicates if the matrix of the recruitment plot is to be returned.

ret.hist

Ignored, for backwards compatibility.

ret.mode

Indicates if the mode of the identity is to be computed. It requires the modeest package.

id.cutoff

Minimum identity to consider an alignment as "top". By default, it is 0.95 for the identity metrics and 95% of the best scoring alignment for bit score.

verbose

Indicates if the function should report the advance.

...

Any additional graphic parameters to be passed to plot for all panels except the recruitment plot (lower-left).

Value

Returns a list with the following elements:

pos.marks

Midpoints of the position histogram.

id.matrix

Midpoints of the identity histogram.

recplot

Matrix containing the recruitment plot values (if ret.recplot=TRUE).

id.mean

Mean identity.

id.median

Median identity.

id.mode

Mode of the identity (if ret.mode=TRUE). Deprecated.

id.hist

Values of the identity histogram (if ret.hist=TRUE).

pos.hist.low

Values of the position histogram (depth) with "low" identity (i.e., below id.cutoff) (if ret.hist=TRUE).

pos.hist.top

Values of the position histogram (depth) with "top" identity (i.e., above id.cutoff) (if ret.hist=TRUE).

id.max

Value of id.max. This is returned because id.max=NULL may vary.

id.cutoff

Value of id.cutoff. This is returned because id.cutoff=NULL may vary.

seqdepth.mean.top

Average sequencing depth with identity above id.cutoff.

seqdepth.mean.low

Average sequencing depth with identity below id.cutoff.

seqdepth.mean.all

Average sequencing depth without identity filtering.

seqdepth.median.top

Median sequencing depth with identity above id.cutoff.

seqdepth.median.low

Median sequencing depth with identity below id.cutoff.

seqdepth.median.all

Median sequencing depth without identity filtering.

id.metric

Full name of the used identity metric.

id.summary

Full name of the summary method used to build the identity plot.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2)

Description

Produces recruitment plots provided that BlastTab.catsbj.pl has been previously executed.

Usage

enve.recplot2(
  prefix,
  plot = TRUE,
  pos.breaks = 1000,
  pos.breaks.tsv = NA,
  id.breaks = 60,
  id.free.range = FALSE,
  id.metric = c("identity", "corrected identity", "bit score"),
  id.summary = sum,
  id.cutoff = 95,
  threads = 2,
  verbose = TRUE,
  ...
)

Arguments

prefix

Path to the prefix of the BlastTab.catsbj.pl output files. At least the files .rec and .lim must exist with this prefix.

plot

Should the object be plotted?

pos.breaks

Breaks in the positions histogram. It can also be a vector of break points, and values outside the range are ignored. If zero (0), it uses the sequence breaks as defined in the .lim file, which means one bin per contig (or gene, if the mapping is agains genes). Ignored if 'pos.breaks.tsv' is passed.

pos.breaks.tsv

Path to a list of (absolute) coordinates to use as position breaks. This tab-delimited file can be produced by GFF.catsbj.pl, and it must contain at least one column: coordinates of the break positions of each position bin. If it has a second column, this is used as the name of the position bin that ends at the given coordinate (the first row is ignored). Any additional columns are currently ignored. If NA, position bins are determined by pos.breaks.

id.breaks

Breaks in the identity histogram. It can also be a vector of break points, and values outside the range are ignored.

id.free.range

Indicates that the range should be freely set from the observed values. Otherwise, 70-100% is included in the identity histogram (default).

id.metric

Metric of identity to be used (Y-axis). Corrected identity is only supported if the original BLAST file included sequence lengths.

id.summary

Function summarizing the identity bins. Other recommended options include: median to estimate the median instead of total bins, and function(x) mlv(x,method='parzen')$M to estimate the mode.

id.cutoff

Cutoff of identity metric above which the hits are considered in-group. The 95% identity corresponds to the expectation of ANI<95% within species.

threads

Number of threads to use.

verbose

Indicates if the function should report the advance.

...

Any additional parameters supported by plot.enve.RecPlot2.

Value

Returns an object of class enve.RecPlot2.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Kenji Gerhardt [aut]

Enveomics: Recruitment Plot (2) ANI Estimate

Description

Estimate the Average Nucleotide Identity from reads (ANIr) from a recruitment plot.

Usage

enve.recplot2.ANIr(x, range = c(0, Inf))

Arguments

x

enve.RecPlot2 object.

range

Range of identities to be considered. By default, the full range is used (note that the upper boundary is Inf and not 100 because recruitment plots can also be built with bit-scores). To use only intra-population matches (with identities), use c(95, 100). To use only inter-population values, use c(0, 95).

Value

A numeric value indicating the ANIr (as percentage).

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Internal Ancillary Function

Description

Internal ancillary function (see enve.recplot2).

Usage

enve.recplot2.__counts(x, pos.breaks, id.breaks, rec.idcol)

Arguments

x

enve.RecPlot2 object

pos.breaks

Position breaks

id.breaks

Identity breaks

rec.idcol

Identity column to use

Value

2-dimensional matrix of counts per identity and position bins.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Kenji Gerhardt [aut]

Enveomics: Recruitment Plot (2) Peak S4 Class - Internal Ancillary Function

Description

Internal ancillary function (see enve.RecPlot2.Peak).

Usage

enve.recplot2.__peakHist(x, mids, counts = TRUE)

Arguments

x

enve.RecPlot2.Peak object

mids

Midpoints

counts

Counts

Value

A numeric vector of counts (histogram)

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Peak Finder - Internal Ancillary Function

Description

Internal ancillary function (see enve.recplot2.findPeaks).

Usage

enve.recplot2.__whichClosestPeak(peak, peaks)

Arguments

peak

Query enve.RecPlot2.Peak object

peaks

list of enve.RecPlot2.Peak objects

Value

A numeric index out of peaks.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Change Cutoff

Description

Change the intra-species cutoff of an existing recruitment plot.

Usage

enve.recplot2.changeCutoff(rp, new.cutoff = 98)

Arguments

rp

enve.RecPlot2 object.

new.cutoff

New cutoff to use.

Value

The modified enve.RecPlot2 object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Compare Identities

Description

Compare the distribution of identities between two enve.RecPlot2 objects.

Usage

enve.recplot2.compareIdentities(
  x,
  y,
  method = "hellinger",
  smooth.par = NULL,
  pseudocounts = 0,
  max.deviation = 0.75
)

Arguments

x

First enve.RecPlot2 object.

y

Second enve.RecPlot2 object.

method

Distance method to use. This should be (an unambiguous abbreviation of) one of:

  • "hellinger" (Hellinger, 1090, doi:10.1515/crll.1909.136.210),

  • "bhattacharyya" (Bhattacharyya, 1943, Bull. Calcutta Math. Soc. 35),

  • "kl" or "kullback-leibler" (Kullback & Leibler, 1951, doi:10.1214/aoms/1177729694), or

  • "euclidean"

smooth.par

Smoothing parameter for cubic spline smoothing. Use 0 for no smoothing. Use NULL to automatically determine this value using leave-one-out cross-validation (see smooth.spline parameter spar).

pseudocounts

Smoothing parameter for Laplace smoothing. Use 0 for no smoothing, or 1 for add-one smoothing.

max.deviation

Maximum mean deviation between identity breaks tolerated (as percent identity). Difference in number of id.breaks is never tolerated.

Value

A numeric indicating the distance between the objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Coordinates

Description

Returns the sequence name and coordinates of the requested position bins.

Usage

enve.recplot2.coordinates(x, bins)

Arguments

x

enve.RecPlot2 object.

bins

Vector of selected bins to return. It can be a vector of logical values with the same length as x$pos.breaks-1 or a vector of integers. If missing, returns the coordinates of all windows.

Value

Returns a data.frame with five columns: name.from (character), pos.from (numeric), name.to (character), pos.to (numeric), and seq.name (character). The first two correspond to sequence and position of the start point of the bin. The next two correspond to the sequence and position of the end point of the bin. The last one indicates the name of the sequence (if defined).

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Core Peak Finder

Description

Finds the peak in a list of peaks that is most likely to represent the "core genome" of a population.

Usage

enve.recplot2.corePeak(x)

Arguments

x

list of enve.RecPlot2.Peak objects.

Value

A enve.RecPlot2.Peak object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Extract Windows

Description

Extract windows significantly below (or above) the peak in sequencing depth.

Usage

enve.recplot2.extractWindows(
  rp,
  peak,
  lower.tail = TRUE,
  significance = 0.05,
  seq.names = FALSE
)

Arguments

rp

Recruitment plot, a enve.RecPlot2 object.

peak

Peak, an enve.RecPlot2.Peak object. If list, it is assumed to be a list of enve.RecPlot2.Peak objects, in which case the core peak is used (see enve.recplot2.corePeak).

lower.tail

If FALSE, it returns windows significantly above the peak in sequencing depth.

significance

Significance threshold (alpha) to select windows.

seq.names

Returns subject sequence names instead of a vector of Booleans. If the recruitment plot was generated with named position bins (e.g, using pos.breaks=0 or a two-column pos.breaks.tsv), it returns a vector of characters (the sequence identifiers), otherwise it returns a data.frame with a name column and two columns of coordinates.

Value

Returns a vector of logicals if seq.names = FALSE. If seq.names = TRUE, it returns a data.frame with five columns: name.from, name.to, pos.from, pos.to, and seq.name (see enve.recplot2.coordinates).

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Peak Finder

Description

Identifies peaks in the population histogram potentially indicating sub-population mixtures.

Usage

enve.recplot2.findPeaks(x, method = "emauto", ...)

Arguments

x

An enve.RecPlot2 object.

method

Peak-finder method. This should be one of:

  • emauto (Expectation-Maximization with auto-selection of components)

  • em (Expectation-Maximization)

  • mower (Custom distribution-mowing method)

...

Any additional parameters supported by enve.recplot2.findPeaks.

Value

Returns a list of enve.RecPlot2.Peak objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) EM Peak Finder - Internal Ancillary Function Expectation

Description

Internal ancillary function (see enve.recplot2.findPeaks.em).

Usage

enve.recplot2.findPeaks.__em_e(x, theta)

Arguments

x

Vector of log-transformed sequencing depths

theta

Parameters list

Value

A list with components ll (numeric) the log-likelihood, and posterior (numeric) the posterior probability.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Em Peak Finder - Internal Ancillary Function Maximization

Description

Internal ancillary function (see enve.recplot2.findPeaks.em).

Usage

enve.recplot2.findPeaks.__em_m(x, posterior)

Arguments

x

Vector of log-transformed sequencing depths

posterior

Posterior probability

Value

A list with components mu (numeric) the estimated mean, sd (numeric) the estimated standard deviation, and alpha (numeric) the estimated alpha parameter.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) EMauto Peak Finder - Internal Ancillary Function

Description

Internal ancillary function (see enve.recplot2.findPeaks.emauto).

Usage

enve.recplot2.findPeaks.__emauto_one(x, comp, do_crit, best, verbose, ...)

Arguments

x

enve.RecPlot2 object.

comp

Components.

do_crit

Function estimating the criterion.

best

Best solution thus far.

verbose

If verbose.

...

Additional parameters for enve.recplot2.findPeaks.em.

Value

Updated solution with the same structure as best.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Mowing Peak Finder - Internal Ancillary Function 1

Description

Internal ancillary function (see enve.recplot2.findPeaks.mower).

Usage

enve.recplot2.findPeaks.__mow_one(
  lsd1,
  min.points,
  quant.est,
  mlv.opts,
  fitdist.opts,
  with.skewness,
  optim.rounds,
  optim.epsilon,
  n.total,
  merge.logdist,
  verbose,
  log
)

Arguments

lsd1

Vector of log-transformed sequencing depths

min.points

Minimum number of points

quant.est

Quantile estimate

mlv.opts

List of options for mlv

fitdist.opts

List of options for fitdist

with.skewness

If skewed-normal should be used

optim.rounds

Maximum number of optimization rounds

optim.epsilon

Minimum difference considered negligible

n.total

Global number of windows

merge.logdist

Attempted merge.logdist parameter

verbose

If verbose

log

If log-transformed depths

Value

Return an enve.RecPlot2.Peak object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Mowing Peak Finder - Internal Ancillary Function 2

Description

Internal ancillary function (see enve.recplot2.findPeaks.mower).

Usage

enve.recplot2.findPeaks.__mower(peaks.opts)

Arguments

peaks.opts

List of options for enve.recplot2.findPeaks.__mow_one

Value

A list of enve.RecPlot2.Peak objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Em Peak Finder

Description

Identifies peaks in the population histogram using a Gaussian Mixture Model Expectation Maximization (GMM-EM) method.

Usage

enve.recplot2.findPeaks.em(
  x,
  max.iter = 1000,
  ll.diff.res = 1e-08,
  components = 2,
  rm.top = 0.05,
  verbose = FALSE,
  init,
  log = TRUE
)

Arguments

x

An enve.RecPlot2 object.

max.iter

Maximum number of EM iterations.

ll.diff.res

Maximum Log-Likelihood difference to be considered as convergent.

components

Number of distributions assumed in the mixture.

rm.top

Top-values to remove before finding peaks, as a quantile probability. This step is useful to remove highly conserved regions, but can be turned off by setting rm.top=0. The quantile is determined after removing zero-coverage windows.

verbose

Display (mostly debugging) information.

init

Initialization parameters. By default, these are derived from k-means clustering. A named list with vectors for mu, sd, and alpha, each of length components.

log

Logical value indicating if the estimations should be performed in natural logarithm units. Do not change unless you know what you're doing.

Value

Returns a list of enve.RecPlot2.Peak objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Emauto Peak Finder

Description

Identifies peaks in the population histogram using a Gaussian Mixture Model Expectation Maximization (GMM-EM) method with number of components automatically detected.

Usage

enve.recplot2.findPeaks.emauto(
  x,
  components = seq(1, 5),
  criterion = "aic",
  merge.tol = 2L,
  verbose = FALSE,
  ...
)

Arguments

x

An enve.RecPlot2 object.

components

A vector of number of components to evaluate.

criterion

Criterion to use for components selection. Must be one of: aic (Akaike Information Criterion), bic or sbc (Bayesian Information Criterion or Schwarz Criterion).

merge.tol

When attempting to merge peaks with very similar sequencing depth, use this number of significant digits (in log-scale).

verbose

Display (mostly debugging) information.

...

Any additional parameters supported by enve.recplot2.findPeaks.em.

Value

Returns a list of enve.RecPlot2.Peak objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Mowing Peak Finder

Description

Identifies peaks in the population histogram potentially indicating sub-population mixtures, using a custom distribution-mowing method.

Usage

enve.recplot2.findPeaks.mower(
  x,
  min.points = 10,
  quant.est = c(0.002, 0.998),
  mlv.opts = list(method = "parzen"),
  fitdist.opts.sn = list(distr = "sn", method = "qme", probs = c(0.1, 0.5, 0.8), start =
    list(omega = 1, alpha = -1), lower = c(0, -Inf, -Inf)),
  fitdist.opts.norm = list(distr = "norm", method = "qme", probs = c(0.4, 0.6), start =
    list(sd = 1), lower = c(0, -Inf)),
  rm.top = 0.05,
  with.skewness = TRUE,
  optim.rounds = 200,
  optim.epsilon = 1e-04,
  merge.logdist = log(1.75),
  verbose = FALSE,
  log = TRUE
)

Arguments

x

An enve.RecPlot2 object.

min.points

Minimum number of points in the quantile-estimation-range (quant.est) to estimate a peak.

quant.est

Range of quantiles to be used in the estimation of a peak's parameters.

mlv.opts

Ignored. For backwards compatibility.

fitdist.opts.sn

Options passed to fitdist to estimate the standard deviation if with.skewness=TRUE. Note that the start parameter will be ammended with xi=estimated mode for each peak.

fitdist.opts.norm

Options passed to fitdist to estimate the standard deviation if with.skewness=FALSE. Note that the start parameter will be ammended with mean=estimated mode for each peak.

rm.top

Top-values to remove before finding peaks, as a quantile probability. This step is useful to remove highly conserved regions, but can be turned off by setting rm.top=0. The quantile is determined after removing zero-coverage windows.

with.skewness

Allow skewness correction of the peaks. Typically, the sequencing-depth distribution for a single peak is left-skewed, due partly (but not exclusively) to fragmentation and mapping sensitivity. See Lindner et al 2013, Bioinformatics 29(10):1260-7 for an alternative solution for the first problem (fragmentation) called "tail distribution".

optim.rounds

Maximum rounds of peak optimization.

optim.epsilon

Trace change at which optimization stops (unless optim.rounds is reached first). The trace change is estimated as the sum of square differences between parameters in one round and those from two rounds earlier (to avoid infinite loops from approximation).

merge.logdist

Maximum value of |log-ratio| between centrality parameters in peaks to attempt merging. The default of ~0.22 corresponds to a maximum difference of 25%.

verbose

Display (mostly debugging) information.

log

Logical value indicating if the estimations should be performed in natural logarithm units. Do not change unless you know what you're doing.

Value

Returns a list of enve.RecPlot2.Peak objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Sequencing Depth

Description

Calculate the sequencing depth of the given window(s).

Usage

enve.recplot2.seqdepth(x, sel, low.identity = FALSE)

Arguments

x

enve.RecPlot2 object.

sel

Window(s) for which the sequencing depth is to be calculated. If not passed, it returns the sequencing depth of all windows.

low.identity

A logical indicating if the sequencing depth is to be estimated only with low-identity matches. By default, only high-identity matches are used.

Value

Returns a numeric vector of sequencing depths (in bp/bp).

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2) Window Depth Threshold

Description

Identifies the threshold below which windows should be identified as variable or absent.

Usage

enve.recplot2.windowDepthThreshold(
  rp,
  peak,
  lower.tail = TRUE,
  significance = 0.05
)

Arguments

rp

Recruitment plot, an enve.RecPlot2 object.

peak

Peak, an enve.RecPlot2.Peak object. If list, it is assumed to be a list of enve.RecPlot2.Peak objects, in which case the core peak is used (see enve.recplot2.corePeak).

lower.tail

If FALSE, it returns windows significantly above the peak in sequencing depth.

significance

Significance threshold (alpha) to select windows.

Value

Returns a float. The units are depth if the peaks were estimated in linear scale, or log-depth otherwise (peak$log).

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Selection vector

Description

Normalizes a selection vector sel to a logical vector with indexes from dim.names.

Usage

enve.selvector(sel, dim.names)

Arguments

sel

A vector of numbers, characters, or booleans.

dim.names

A vector of names from which to select.

Value

Returns a logical vector with the same length as dim.name.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS

Description

Subsample any objects in "distance space" to reduce the effect of sample-clustering. This function was originally designed to subsample genomes in "phylogenetic distance space", a clear case of strong clustering bias in sampling, by Luis M. Rodriguez-R and Michael R Weigand.

Usage

enve.tribs(
  dist,
  selection = labels(dist),
  replicates = 1000,
  summary.fx = median,
  dist.method = "euclidean",
  subsamples = seq(0, 1, by = 0.01),
  dimensions = ceiling(length(selection) * 0.05),
  metaMDS.opts = list(),
  threads = 2,
  verbosity = 1,
  points,
  pre.tribs
)

Arguments

dist

Distances as a dist object.

selection

Objects to include in the subsample. By default, all objects are selected.

replicates

Number of replications per point.

summary.fx

Function to summarize the distance distributions in a given replicate. By default, the median distance is estimated.

dist.method

Distance method between random points and samples in the transformed space. See dist.

subsamples

Subsampling fractions.

dimensions

Dimensions to use in the NMDS. By default, 5% of the selection length.

metaMDS.opts

Any additional options to pass to metaMDS, as list.

threads

Number of threads to use.

verbosity

Verbosity. Use 0 to run quietly, increase for additional information.

points

Optional. If passed, the MDS step is skipped and this object is used instead. It can be the $points slot of class metaMDS (from vegan). It must be a matrix or matrix-coercible object, with samples as rows and dimensions as columns.

pre.tribs

Optional. If passed, the points are recovered from this object (except if points is also passed. This should be an enve.TRIBS object estimated on the same objects (the selection is unimportant).

Value

Returns an enve.TRIBS object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS Test

Description

Estimates the empirical difference between all the distances in a set of objects and a subset, together with its statistical significance.

Usage

enve.tribs.test(dist, selection, bins = 50, ...)

Arguments

dist

Distances as dist object.

selection

Selection defining the subset.

bins

Number of bins to evaluate in the range of distances.

...

Any other parameters supported by enve.tribs, except subsamples.

Value

Returns an enve.TRIBStest object.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Truncate

Description

Removes the n highest and lowest values from a vector, and applies summary function. The value of n is determined such that the central range is used, corresponding to the f fraction of values.

Usage

enve.truncate(x, f = 0.95, FUN = mean)

Arguments

x

A vector of numbers.

f

The fraction of values to retain.

FUN

Summary function to apply to the vectors. To obtain the truncated vector itself, use c.

Value

Returns the summary (FUN) of the truncated vector.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Bacterial growth curves for three Escherichia coli mutants

Description

This data set provides time (first column) and three triplicated growth curves as optical density at 600nm (OD_600nm) for different mutants of E. coli.

Usage

growth.curves

Format

A data frame with 16 rows (times) and 10 rows (times and OD_600nm).

Counts of microbial phyla in four sites

Description

This data set gives the counts of phyla in three different sites.

Usage

phyla.counts

Format

A data frame with 9 rows (phyla) and 4 rows (sites).

Enveomics: Plot of Growth Curve

Description

Plots an enve.GrowthCurve object.

Usage

## S3 method for class 'enve.GrowthCurve'
plot(
  x,
  col,
  samples,
  pt.alpha = 0.9,
  ln.alpha = 1,
  ln.lwd = 1,
  ln.lty = 1,
  band.alpha = 0.4,
  band.density = NULL,
  band.angle = 45,
  xp.alpha = 0.5,
  xp.lwd = 1,
  xp.lty = 1,
  pch = 19,
  new = TRUE,
  legend = new,
  add.params = FALSE,
  ...
)

Arguments

x

An enve.GrowthCurve object to plot.

col

Base colors to use for the different samples. Can be recycled. By default, grey for one sample or rainbow colors for more than one.

samples

Vector of sample names to plot. By default: plot all samples.

pt.alpha

Color alpha for the observed data points, using col as a base.

ln.alpha

Color alpha for the fitted growth curve, using col as a base.

ln.lwd

Line width for the fitted curve.

ln.lty

Line type for the fitted curve.

band.alpha

Color alpha for the confidence interval band of the fitted growth curve, using col as a base.

band.density

Density of the filling pattern in the interval band. If NULL, a solid color is used.

band.angle

Angle of the density filling pattern in the interval band. Ignored if band.density is NULL.

xp.alpha

Color alpha for the line connecting individual experiments, using col as a base.

xp.lwd

Width of line for the experiments.

xp.lty

Type of line for the experiments.

pch

Point character for observed data points.

new

Should a new plot be generated? If FALSE, the existing canvas is used.

legend

Should the plot include a legend? If FALSE, no legend is added. If TRUE, a legend is added in the bottom-right corner. Otherwise, a legend is added in the position specified as xy.coords.

add.params

Should the legend include the parameters of the fitted model?

...

Any other graphic parameters.

Value

No return value.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Recruitment Plot (2)

Description

Plots an enve.RecPlot2 object.

Usage

## S3 method for class 'enve.RecPlot2'
plot(
  x,
  layout = matrix(c(5, 5, 2, 1, 4, 3), nrow = 2),
  panel.fun = list(),
  widths = c(1, 7, 2),
  heights = c(1, 2),
  palette = grey((100:0)/100),
  underlay.group = TRUE,
  peaks.col = "darkred",
  use.peaks,
  id.lim = range(x$id.breaks),
  pos.lim = range(x$pos.breaks),
  pos.units = c("Mbp", "Kbp", "bp"),
  mar = list(`1` = c(5, 4, 1, 1) + 0.1, `2` = c(ifelse(any(layout == 1), 1, 5), 4, 4, 1)
    + 0.1, `3` = c(5, ifelse(any(layout == 1), 1, 4), 1, 2) + 0.1, `4` =
    c(ifelse(any(layout == 1), 1, 5), ifelse(any(layout == 2), 1, 4), 4, 2) + 0.1, `5` =
    c(5, 3, 4, 1) + 0.1, `6` = c(5, 4, 4, 2) + 0.1),
  pos.splines = 0,
  id.splines = 1/2,
  in.lwd = ifelse(is.null(pos.splines) || pos.splines > 0, 1/2, 2),
  out.lwd = ifelse(is.null(pos.splines) || pos.splines > 0, 1/2, 2),
  id.lwd = ifelse(is.null(id.splines) || id.splines > 0, 1/2, 2),
  in.col = "darkblue",
  out.col = "lightblue",
  id.col = "black",
  breaks.col = "#AAAAAA40",
  peaks.opts = list(),
  ...
)

Arguments

x

enve.RecPlot2 object to plot.

layout

Matrix indicating the position of the different panels in the layout, where:

  • 0: Empty space

  • 1: Counts matrix

  • 2: position histogram (sequencing depth)

  • 3: identity histogram

  • 4: Populations histogram (histogram of sequencing depths)

  • 5: Color scale for the counts matrix (vertical)

  • 6: Color scale of the counts matrix (horizontal)

Only panels indicated here will be plotted. To plot only one panel simply set this to the number of the panel you want to plot.

panel.fun

List of functions to be executed after drawing each panel. Use the indices in layout (as characters) as keys. Functions for indices missing in layout are ignored. For example, to add a vertical line at the 3Mbp mark in both the position histogram and the counts matrix: list('1'=function() abline(v=3), '2'=function() abline(v=3)). Note that the X-axis in both panels is in Mbp by default. To change this behavior, set pos.units accordingly.

widths

Relative widths of the columns of layout.

heights

Relative heights of the rows of layout.

palette

Colors to be used to represent the counts matrix, sorted from no hits to the maximum sequencing depth.

underlay.group

If TRUE, it indicates the in-group and out-group areas couloured based on in.col and out.col. Requires support for semi-transparency.

peaks.col

If not NA, it attempts to represent peaks in the population histogram in the specified color. Set to NA to avoid peak-finding.

use.peaks

A list of enve.RecPlot2.Peak objects, as returned by enve.recplot2.findPeaks. If passed, peaks.opts is ignored.

id.lim

Limits of identities to represent.

pos.lim

Limits of positions to represent (in bp, regardless of pos.units).

pos.units

Units in which the positions should be represented (powers of 1,000 base pairs).

mar

Margins of the panels as a list, with the character representation of the number of the panel as index (see layout).

pos.splines

Smoothing parameter for the splines in the position histogram. Zero (0) for no splines. Use NULL to automatically detect by leave-one-out cross-validation.

id.splines

Smoothing parameter for the splines in the identity histogram. Zero (0) for no splines. Use NULL to automatically detect by leave-one-out cross-validation.

in.lwd

Line width for the sequencing depth of in-group matches.

out.lwd

Line width for the sequencing depth of out-group matches.

id.lwd

Line width for the identity histogram.

in.col

Color associated to in-group matches.

out.col

Color associated to out-group matches.

id.col

Color for the identity histogram.

breaks.col

Color of the vertical lines indicating sequence breaks.

peaks.opts

Options passed to enve.recplot2.findPeaks, if peaks.col is not NA.

...

Any other graphic parameters (currently ignored).

Value

Returns a list of enve.RecPlot2.Peak objects (see enve.recplot2.findPeaks). If peaks.col=NA or layout doesn't include 4, returns NA.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS Plot

Description

Plot an enve.TRIBS object.

Usage

## S3 method for class 'enve.TRIBS'
plot(
  x,
  new = TRUE,
  type = c("boxplot", "points"),
  col = "#00000044",
  pt.cex = 1/2,
  pt.pch = 19,
  pt.col = col,
  ln.col = col,
  ...
)

Arguments

x

enve.TRIBS object to plot.

new

Should a new canvas be drawn?

type

Type of plot. The points plot shows all the replicates, the boxplot plot represents the values found by boxplot.stats. as areas, and plots the outliers as points.

col

Color of the areas and/or the points.

pt.cex

Size of the points.

pt.pch

Points character.

pt.col

Color of the points.

ln.col

Color of the lines.

...

Any additional parameters supported by plot.

Value

No return value.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS Plot Test

Description

Plots an enve.TRIBStest object.

Usage

## S3 method for class 'enve.TRIBStest'
plot(
  x,
  type = c("overlap", "difference"),
  col = "#00000044",
  col1 = col,
  col2 = "#44001144",
  ylab = "Probability",
  xlim = range(attr(x, "dist.mids")),
  ylim = c(0, max(c(attr(x, "all.dist"), attr(x, "sel.dist")))),
  ...
)

Arguments

x

enve.TRIBStest object to plot.

type

What to plot. overlap generates a plot of the two contrasting empirical PDFs (to compare against each other), difference produces a plot of the differences between the empirical PDFs (to compare against zero).

col

Main color of the plot if type=difference.

col1

First color of the plot if type=overlap.

col2

Second color of the plot if type=overlap.

ylab

Y-axis label.

xlim

X-axis limits.

ylim

Y-axis limits.

...

Any other graphical arguments.

Value

No return value.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: Summary of Growth Curve

Description

Summary of an enve.GrowthCurve object.

Usage

## S3 method for class 'enve.GrowthCurve'
summary(object, ...)

Arguments

object

An enve.GrowthCurve object.

...

No additional parameters are currently supported.

Value

No return value.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS Summary

Description

Summary of an enve.TRIBS object.

Usage

## S3 method for class 'enve.TRIBS'
summary(object, ...)

Arguments

object

enve.TRIBS object.

...

No additional parameters are currently supported.

Value

No return value.

Author(s)

Luis M. Rodriguez-R [aut, cre]

Enveomics: TRIBS Summary Test

Description

Summary of an enve.TRIBStest object.

Usage

## S3 method for class 'enve.TRIBStest'
summary(object, ...)

Arguments

object

enve.TRIBStest object.

...

No additional parameters are currently supported.

Value

No return value.

Author(s)

Luis M. Rodriguez-R [aut, cre]