Package 'Nonpareil'

Title: Metagenome Coverage Estimation and Projections for 'Nonpareil'
Description: Plot, process, and analyze NPO files produced by 'Nonpareil' <http://enve-omics.ce.gatech.edu/nonpareil/>.
Authors: Luis M. Rodriguez-R [aut, cre]
Maintainer: Luis M. Rodriguez-R <[email protected]>
License: Artistic-2.0
Version: 3.5.3
Built: 2024-09-28 07:23:56 UTC
Source: CRAN

Help Index

Alias of Nonpareil.add.curve.

Description

Alias of Nonpareil.add.curve.

Usage

## S4 method for signature 'Nonpareil.Set,ANY'
e1 + e2

Arguments

e1

Nonpareil.Set object (nps).
e2

Nonpareil.Curve object (np).

Get attribute.

Description

Get attribute.

Usage

## S4 method for signature 'Nonpareil.Curve'
x$name

Arguments

x

Nonpareil.Curve object.
name

Attribute.

Get attribute.

Description

Get attribute.

Usage

## S4 method for signature 'Nonpareil.Set'
x$name

Arguments

x

Nonpareil.Set object.
name

Attribute.

Set attribute.

Description

Set attribute.

Usage

## S4 replacement method for signature 'Nonpareil.Curve'
x$name <- value

Arguments

x

Nonpareil.Curve object.
name

Attribute.
value

New value.

Set attribute.

Description

Set attribute.

Usage

## S4 replacement method for signature 'Nonpareil.Set'
x$name <- value

Arguments

x

Nonpareil.Set object.
name

Attribute.
value

New value.

Nonpareil: Metagenome Coverage Estimation and Projections for 'Nonpareil'.

Description

Plot, process, and analyze NPO files produced by 'Nonpareil' http://enve-omics.ce.gatech.edu/nonpareil/.

Citation

If you use Nonpareil, please cite: Rodriguez-R et al. 2018. Nonpareil 3: Fast estimation of metagenomic coverage and sequence diversity. mSystems 3(3): e00039-18. DOI: 10.1128/mSystems.00039-18.

Rodriguez-R & Konstantinidis. 2014. Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets. Bioinformatics 30 (5): 629-635. DOI: 10.1093/bioinformatics/btt584.

For an extended discussion on coverage in metagenomic data, see also:

Rodriguez-R & Konstantinidis. 2014. Estimating coverage in metagenomic data sets and why it matters. The ISME Journal 8: 2349–2351. DOI: 10.1038/ismej.2014.76.

Author(s)

Maintainer: Luis M. Rodriguez-R [email protected]

See Also

Useful links:

Adds a Nonpareil.Curve to a Nonpareil.Set.

Description

Adds a Nonpareil.Curve to a Nonpareil.Set.

Usage

Nonpareil.add.curve(nps, np)

Arguments

nps

Nonpareil.Set object.
np

Nonpareil.Curve object.

Value

Returns the Nonpareil.Set including the newly added Nonpareil.Curve.

Complement function of Nonpareil.f.

Description

Complement function of Nonpareil.f.

Usage

Nonpareil.antif(y, a, b)

Arguments

y

Values of abundance-weighted average coverage.
a

Parameter alpha of the gamma CDF.
b

Parameter beta of the gamma CDF.

Value

Estimated sequencing effort.

Returns the color of the curve.

Description

Returns the color of the curve.

Usage

Nonpareil.col(x, alpha = 1)

Arguments

x

Nonpareil.Curve or Nonpareil.Set object.
alpha

Alpha level of the color from 0 to 1.

Factor to transform redundancy into coverage (internal function).

Description

Factor to transform redundancy into coverage (internal function).

Usage

Nonpareil.coverage_factor(x)

Arguments

x

Nonpareil.Curve object.

Value

A numeric scalar.

Generates a Nonpareil curve from an .npo file

Description

Generates a Nonpareil curve from an .npo file

Usage

Nonpareil.curve(
  file,
  plot = TRUE,
  label = NA,
  col = NA,
  enforce.consistency = TRUE,
  star = 95,
  correction.factor = TRUE,
  weights.exp = NA,
  skip.model = FALSE,
  ...
)

Arguments

file

Path to the .npo file, containing the read redundancy.
plot

Determines if the plot should be produced. If FALSE, it computes the coverage and the model wihtout plotting.
label

Name of the dataset. If NA, it is determined by the file name.
col

Color of the curve. If NA, a random color is assigned (even if plot = FALSE).
enforce.consistency

If TRUE, it fails verbosely on insufficient data, otherwise it warns about the inconsistencies and attempts the estimations.
star

Objective coverage in percentage; i.e., coverage value considered near-complete.
correction.factor

Should the overlap-dependent (or kmer-length-dependent) correction factor be applied? If FALSE, redundancy is assumed to equal coverage.
weights.exp

Vector of values to be tested (in order) as exponent of the weights distribution. If the model fails to converge, sometimes manual modifications in this parameter may help. By default (NA), five different values are tested in the following order: For linear sampling, -1.1, -1.2, -0.9, -1.3, -1. For logarithmic sampling (-d option in Nonpareil), 0, 1, -1, 1.3, -1.1, 1.5, -1.5.
skip.model

If set, skips the model estimation altogether.
...

Any additional parameters passed to plot.Nonpareil.Curve.

Value

Returns invisibly a Nonpareil.Curve object

Examples

# Generate a Nonpareil plot
file <- system.file("extdata", "LakeLanier.npo", package = "Nonpareil")
np <- Nonpareil.curve(file)

# Produce the same plot but using powers of 1,000bp as X axis labels
Nonpareil.curve(file, xaxt = "n", xlab = "Sequencing Effort")
axis(
  1L, at = 10L^seq(3L, 12L, by = 3L),
  labels = paste(1L, c("Kbp", "Mbp", "Gbp", "Tbp"))
)

# Show the estimated values
print(np)

# Predict coverage for 20Gbp
predict(np, 20e9)

# Obtain the Nd diversity index
np$diversity

A single Nonpareil curve. This object can be produced by Nonpareil.curve and supports S4 methods plot, summary, print, and predict. For additional details, see help for summary.Nonpareil.Curve.

Description

A single Nonpareil curve. This object can be produced by Nonpareil.curve and supports S4 methods plot, summary, print, and predict. For additional details, see help for summary.Nonpareil.Curve.

Slots

file

Input .npo file.

label

Name of the dataset.

col

Color of the dataset.

L

Read length.

AL

Adjusted read length (same as L for alignment).

R

Number of reads.

LR

Effective sequencing effort used.

overlap

Minimum read overlap.

ksize

K-mer size (for kmer kernel only).

log.sample

Multiplier of the log-sampling (or zero if linear).

kernel

Read-comparison kernel.

version

Nonpareil version used.

x.obs

Rarefied sequencing effort.

x.adj

Adjusted rarefied sequencing effort.

y.red

Rarefied redundancy (observed).

y.cov

Rarefied coverage (corrected).

y.sd

Standard deviation of rarefied coverage.

y.p25

Percentile 25 (1st quartile) of rarefied coverage.

y.p50

Percentile 50 (median) of rarefied coverage.

y.p75

Percentile 75 (3rd quartile) of rarefied coverage.

kappa

Dataset redundancy.

C

Dataset coverage.

consistent

Is the data sufficient for accurate estimation?

star

Coverage considered 'nearly complete'.

has.model

Was the model successfully estimated?

warning

Warnings generated on consistency or model fitting.

LRstar

Projected seq. effort for nearly complete coverage.

modelR

Pearson's R for the estimated model.

diversity

Dataset Nd index of sequence diversity.

model

Fitted sigmoidal model.

call

Call producing this object.

Alias of Nonpareil.set.

Description

Alias of Nonpareil.set.

Usage

Nonpareil.curve.batch(
  files,
  col = NA,
  labels = NA,
  plot = TRUE,
  plot.opts = list(),
  ...
)

Arguments

files

Vector with the paths to the .npo files.
col

Color of the curves (vector). If not passed, values are randomly assigned. Values are recycled.
labels

Labels of the curves (vector). If not passed, values are determined by the filename. Values are recycled.
plot

If TRUE, it generates the Nonpareil curve plots.
plot.opts

Any parameters accepted by plot.Nonpareil.Set as a list.
...

Any additional parameters accepted by Nonpareil.curve.

Function of the projected model.

Description

Function of the projected model.

Usage

Nonpareil.f(x, a, b)

Arguments

x

Values of sequencing effort (in bp).
a

Parameter alpha of the Gamma CDF.
b

Parameter beta of the Gamma CDF.

Value

Predicted values of abundance-weighted average coverage.

Fit the sigmoidal model to the rarefied coverage.

Description

Fit the sigmoidal model to the rarefied coverage.

Usage

Nonpareil.fit_model(np, weights.exp)

Arguments

np

Nonpareil.Curve object.
weights.exp

Numeric; see Nonpareil.curve for details.

Generates a legend for Nonpareil plots.

Description

Generates a legend for Nonpareil plots.

Usage

Nonpareil.legend(np, x, y = 0.3, ...)

Arguments

np

A Nonpareil.Set or Nonpareil.Curve object, or a list of Nonpareil.Curve objects.
x

X coordinate, or any character string accepted by legend (e.g., 'bottomright').
y

Y coordinate.
...

Any other parameters supported by legend().

Value

Returns invisibly a list, same as legend.

Read the data tables and extract direct estimates.

Description

Read the data tables and extract direct estimates.

Usage

Nonpareil.read_data(x, correction.factor)

Arguments

x

Nonpareil.Curve object.
correction.factor

Logical; see Nonpareil.curve for details.

Read the metadata headers.

Description

Read the metadata headers.

Usage

Nonpareil.read_metadata(x)

Arguments

x

Nonpareil.Curve object.

Generates a collection of Nonpareil curves (a Nonpareil.Set object) and (optionally) plots all of them in a single canvas.

Description

Generates a collection of Nonpareil curves (a Nonpareil.Set object) and (optionally) plots all of them in a single canvas.

Usage

Nonpareil.set(
  files,
  col = NA,
  labels = NA,
  plot = TRUE,
  plot.opts = list(),
  ...
)

Arguments

files

Vector with the paths to the .npo files.
col

Color of the curves (vector). If not passed, values are randomly assigned. Values are recycled.
labels

Labels of the curves (vector). If not passed, values are determined by the filename. Values are recycled.
plot

If TRUE, it generates the Nonpareil curve plots.
plot.opts

Any parameters accepted by plot.Nonpareil.Set as a list.
...

Any additional parameters accepted by Nonpareil.curve.

Value

Returns invisibly a Nonpareil.Set object.

Examples

# Generate a Nonpareil plot with multiple curves
files <- system.file(
  "extdata",
  c("HumanGut.npo", "LakeLanier.npo", "IowaSoil.npo"),
  package = "Nonpareil"
)
col <- c("orange","darkcyan","firebrick4")
nps <- Nonpareil.set(
  files, col = col,
  plot.opts = list(plot.observed = FALSE, model.lwd = 2)
)

# Show the estimated values
print(nps)

# Show current coverage (as %)
summary(nps)[, "C"] * 100

# Extract Nd diversity index
summary(nps)[, "diversity"]

# Extract sequencing effort for nearly complete coverage (in Gbp)
summary(nps)[, "LRstar"] / 1e9

# Predict coverage for a sequencing effort of 10Gbp
sapply(nps$np.curves, predict, 10e9)

Collection of Nonpareil.Curve objects. This object can be produced by Nonpareil.curve.batch and supports S4 methods plot, summary, and print.

Description

Collection of Nonpareil.Curve objects. This object can be produced by Nonpareil.curve.batch and supports S4 methods plot, summary, and print.

Slots

np.curves

List of Nonpareil.Curve objects.

call

Call producing this object.

Plot a Nonpareil.Curve object.

Description

Plot a Nonpareil.Curve object.

Usage

## S3 method for class 'Nonpareil.Curve'
plot(
  x,
  col = NA,
  add = FALSE,
  new = !add,
  plot.observed = TRUE,
  plot.model = TRUE,
  plot.dispersion = FALSE,
  plot.diversity = TRUE,
  xlim = c(1000, 1e+13),
  ylim = c(1e-06, 1),
  main = paste("Nonpareil Curve for", x$label),
  xlab = "Sequencing effort (bp)",
  ylab = "Estimated Average Coverage",
  curve.lwd = 2,
  curve.alpha = 0.4,
  model.lwd = 1,
  model.alpha = 1,
  log = "x",
  arrow.length = 0.05,
  arrow.head = arrow.length,
  ...
)

Arguments

x

Nonpareil.Curve object to plot.
col

Color of the curve. If passed, it overrides the colors set in the Nonpareil.Curve object.
add

If TRUE, it attempts to use a previous (active) canvas to plot the curve.
new

Inverse of 'add'.
plot.observed

Indicates if the observed (rarefied) coverage is to be plotted.
plot.model

Indicates if the fitted model is to be plotted.
plot.dispersion

Indicates if (and how) dispersion of the replicates should be plotted. Supported values are:

  • FALSE: no dispersion is plotted (default),

  • 'sd': one standard deviation around the mean,

  • 'ci95': 95

  • 'ci90': 90

  • 'ci50': 50

  • 'iq': Inter-quartile range.

plot.diversity

If TRUE, the diversity estimate is plotted as a small arrow below the Nonpareil curve.
xlim

Limits of the sequencing effort (X-axis).
ylim

Limits of the coverage (Y-axis).
main

Title of the plot.
xlab

Label of the X-axis.
ylab

Label of the Y-axis.
curve.lwd

Line width of the rarefied coverage.
curve.alpha

Alpha value (from 0 to 1) of the rarefied coverage.
model.lwd

Line width of the model.
model.alpha

Alpha value (from 0 to 1) of the model.
log

Axis to plot in logarithmic scale. Supported values are:

  • 'x': sequencing effort (default),

  • 'y': coverage,

  • 'xy': both logarithmic, or

  • '': both linear.

arrow.length

If plot.diversity = TRUE, it determines the length of the arrow to display the divsersity (as a fraction of the ylim range).
arrow.head

If plot.diversity = TRUE, it determines the length of the arrow head to display the diversity index (in inches).
...

Additional graphical parameters.

Value

Retuns invisibly a Nonpareil.Curve object (same as x input). For additional details see help for summary.Nonpareil.Curve.

Plot a Nonpareil.Set object.

Description

Plot a Nonpareil.Set object.

Usage

## S3 method for class 'Nonpareil.Set'
plot(
  x,
  col = NA,
  labels = NA,
  main = "Nonpareil Curves",
  legend.opts = list(),
  ...
)

Arguments

x

Nonpareil.Set object to plot.
col

Color of the curves (vector). If passed, it overrides the colors set in the Nonpareil.Curve objects. Values are recycled.
labels

Labels of the curves (vector). If passed, it overrides the labels set in the Nonpareil.Curve objects. Values are recycled.
main

Title of the plot.
legend.opts

Any additional parameters passed to Nonpareil.legend. If FALSE, the legend is not displayed.
...

Any additional parameters passed to plot.Nonpareil.Curve.

Value

Returns invisibly a Nonpareil.Set object (same as x input).

Predict the coverage for a given sequencing effort.

Description

Predict the coverage for a given sequencing effort.

Usage

## S3 method for class 'Nonpareil.Curve'
predict(object, lr = object$LR, ...)

Arguments

object

Nonpareil.Curve object.
lr

Sequencing effort for the prediction (in bp).
...

Additional parameters ignored.

Value

Returns the expected coverage at the given sequencing effort.

Prints and returns invisibly a summary of the Nonpareil.Curve results.

Description

Prints and returns invisibly a summary of the Nonpareil.Curve results.

Usage

## S3 method for class 'Nonpareil.Curve'
print(x, ...)

Arguments

x

Nonpareil.Set object.
...

Additional parameters ignored.

Value

Returns the summary invisibly. See help for summary.Nonpareil.Curve for additional information.

Prints and returns invisibly a summary of the Nonpareil.Set results.

Description

Prints and returns invisibly a summary of the Nonpareil.Set results.

Usage

## S3 method for class 'Nonpareil.Set'
print(x, ...)

Arguments

x

Nonpareil.Set object.
...

Additional parameters ignored.

Value

Returns the summary invisibly. See help for summary.Nonpareil.Curve and summary.Nonpareil.Set for additional information.

Returns a summary of the Nonpareil.Curve results.

Description

Returns a summary of the Nonpareil.Curve results.

Usage

## S3 method for class 'Nonpareil.Curve'
summary(object, ...)

Arguments

object

Nonpareil.Curve object.
...

Additional parameters ignored.

Value

Returns a matrix with the following values for the dataset:

  • kappa: "Redundancy" value of the entire dataset.

  • C: Average coverage of the entire dataset.

  • LRstar: Estimated sequencing effort required to reach the objective average coverage (star, 95

  • LR: Actual sequencing effort of the dataset.

  • modelR: Pearson's R coefficient betweeen the rarefied data and the projected model.

  • diversity: Nonpareil sequence-diversity index (Nd). This value's units are the natural logarithm of the units of sequencing effort (log-bp), and indicates the inflection point of the fitted model for the Nonpareil curve. If the fit doesn't converge, or the model is not estimated, the value is zero (0).

Returns a summary of the Nonpareil.Set results.

Description

Returns a summary of the Nonpareil.Set results.

Usage

## S3 method for class 'Nonpareil.Set'
summary(object, ...)

Arguments

object

Nonpareil.Set object.
...

Additional parameters ignored.

Value

Returns a matrix with different values for each dataset. For additional details on the values returned, see help for summary.Nonpareil.Curve.