Package 'QsRutils'

Description

Removes elements present in 'y' from 'x'.

Usage

x %wo% y

Arguments

Value

A vector containing elements of 'x' that are not in 'y'.

Examples

c(1, 2, 3, 4, 5) %wo% c(2, 4)
letters[1:5] %wo% c("b", "d")

Description

Transform a percentage to its arc sine.

Usage

arc_sine(x)

Arguments

Value

The arcsine transformation of x.

Examples

arc_sine(30.1)

Description

Indicate Significance with Stars

Usage

asterix(prob)

Arguments

Details

Returns '***; for p < 0.001, '**' for p < 0.01, '*' for p < 0.05.

Value

Character vector of asterisks indicating significance level.

Examples

asterix(0.039)

Description

Calculates alpha-diversity metrics from n samplings of an OTU table to a constant number of counts per sample.

Usage

avg_alpha(
  otu,
  sampling_depth,
  iterations = 100,
  sum_method = c("median", "mean"),
  ncores = 1
)

Arguments

Details

This implementation focuses on speed: - vectorizes the alpha-metric calculations (avoids repeated calls to vegan::diversity and vegan::specnumber) - uses base R operations (rowSums, logical ops, arithmetic) which are much faster in tight loops - optionally parallelizes replicates across cores (platform-aware).

The OTU data frame supplied must be in typical vegan format: samples as row names and taxa as column names. The minimum row sum must be greater than or equal to the sampling depth.

By default the sum_method is mean. For a similar function in QIIME2, the default sum_method is median.

Value

Returns a data frame with Shannon, Observed, Pielou, Simpson and Inverse Simpson as the column names for each sample as the row names in an OTU table.

Examples

{
data(BCI, package = "vegan")
otu <- BCI[rowSums(BCI) > 400, ]
avg_alpha(otu, sampling_depth = 400, iterations = 100)
}

Description

Determine hits of all orientations of primers to paired sequence files.

Usage

check_primer_hits(
  path,
  fwd_pattern = "_R1.fastq",
  rev_pattern = "_R2.fastq",
  fwd_primer = "GGAAGTAAAAGTCGTAACAAGG",
  rev_primer = "GCTGCGTTCTTCATCGATGC"
)

Arguments

Details

This function is for checking the effectiveness of primer trimming of ITS sequences. Because the ITS region varies in length, it is possible for forward sequences to extend past the reverse primenr region and vice-versa, leadsing to what Robert Edgar calls staggered pairs fi the sequences are merged.

The fwd_pattern and rev_pattern must contain the file extension. The defaults are "_R1.fastq" and "_R2.fastq".

Default primers are ITS5 (forward) and ITS2 (reverse) from White et.al 1990.

ITS5: "GGAAGTAAAAGTCGTAACAAGG"

ITS2: "GCTGCGTTCTTCATCGATGC"

Value

A table of hits to the sequences by all primer orientations

Examples

{
  # Copy example fastq files to a writable temp directory
  src <- system.file("extdata", package = "QsRutils")
  path <- file.path(tempdir(), "QsRutils_example")
  dir.create(path, showWarnings = FALSE)
  file.copy(list.files(src, pattern = "\\.fastq\\.gz$", full.names = TRUE), path)
  check_primer_hits(
    path = path,
    fwd_pattern = "raw_1.fastq.gz",
    rev_pattern = "raw_2.fastq.gz",
    fwd_primer = "GGAAGTAAAAGTCGTAACAAGG",
    rev_primer = "GCTGCGTTCTTCATCGATGC"
  )
}

Description

Tests for Heterogeneity of Variances in make_comparisons Result

Usage

check_var(otu.pc.transformed, group.vector)

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

Arguments

Value

A data frame of class "check_var" with one row per taxon and columns taxon, statistic, df, and p.value from the Fligner-Killeen test. The object can be printed with print().

See Also

make_comparisons

Examples

# Transform species matrix to proportion;
# Check variances for the first three species
# in the dune data set grouped by Management.
data(dune, package = "vegan")
data(dune.env, package = "vegan")
dune <- vegan::decostand(dune, method = "total")
dune <- dune[, 1:3]
dune <- t(dune)
check_var(dune, dune.env$Management)

Description

Generate compact letter displays from Dunn.test results

Usage

cld_dunn(dunn_rslt, significance = 0.05)

Arguments

Value

A list of two items. The first, p_adj_matrix, is an object of class 'dist' giving p values adjusted for multiple comparisons. The second,clds, is a character vector of compact letter displays (CLDs) for each treatment.

Examples

# Example cribbed and modified from the kruskal.test documentation
x <- c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects
y <- c(3.8, 2.7, 4.0, 2.4)      # with obstructive airway disease
z <- c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis
x <- c(x, y, z)
g <- factor(rep(1:3, c(5, 4, 5)),
            labels = c("Normal",
                       "COPD",
                       "Asbestosis"))
dunn_rslt <- dunn.test::dunn.test(x, g)
cld_dunn(dunn_rslt, significance = 0.05)

Description

Makes a tibble for adding compact letter assignments to a boxplot using HSD.test results.

Usage

cld_hsd(hsd_rslt, y_pos = "boxtop")

Arguments

Details

hsd_rslt must be created with agricolae::HSD.test

Value

A tibble with columns for treatment groups (x), the y-positions of the treatment CLD (y), and the CLD letters indicating significantly different treatments.

Examples

data("iris")
model <- lm(Petal.Length ~ Species, data = iris)
hsd_rslt <- agricolae::HSD.test(model, trt="Species")
cld_hsd(hsd_rslt)

Description

Clears all warning messages from the base environment.

Usage

clear_warnings()

Details

Sometimes when working in the console R retains a list of warnings such that they keep being reported after the function call which originated them. This function removes them so that they are not a nuisance.

Value

No return value, called for side effects. Clears the stored warning list so that stale warnings are no longer reported in the console.

Examples

{
as.numeric(c("1", "2", "apples"))
summary(warnings())
clear_warnings()
summary(warnings())
}

Description

Calculates the number of combinations of n things drawn r at a time.

Usage

comb(n, r, repetition = FALSE)

Arguments

Value

An integer giving the number of ways a set of r items can be drawn from a set of n items.

Examples

comb(5, 3)
comb(5, 3, repetition = TRUE)

Description

Assembles Comparison Data Frame

Usage

comp_assemble(part1, part2, part3)

Arguments

Details

The data frame returned has taxa as row names. The first three column names are mean (relative abundance of the taxa), sd and F_value for comparisons among groups. The remaining column names are the groups. The group columns show the mean relative abundance for the group plus/minus the standard deviation and a compact letter display for the group. See also the vignette "Compare Relative Abundances Among Treatments."

Value

A summary data frame of differential abundances by taxon and treatment.

Examples

{
data("its.root")
temp1 <- comp_prepare_phyloseq(its.root)
temp2 <- comp_prepare_otu_table(temp1$expt.taxon.pc,
                                grps = "Label",
                                transformation = "sqrt_arc_sine")
temp3 <- comp_means_sd(temp2$otu.pc)
temp4 <- comp_make_f_tests(temp2$otu.pc.trans,
                           grps = temp2$groups,
                           var.equal = TRUE)
temp5 <- comp_comparisons(otu.pc = temp2$otu.pc,
                          otu.pc.trans = temp2$otu.pc.trans,
                          grps = temp2$groups,
                          p.adjust.method = "BH",
                          pool.sd = TRUE)
comp_assemble(temp3, temp4, temp5) |> 
  dplyr::arrange(desc(mean))
}

Description

Calculates the treatment comparison portion of a table comparing relative abundances of each taxon among treatments.

Usage

comp_comparisons(
  otu.pc,
  otu.pc.trans,
  grps,
  p.adjust.method = "BH",
  pool.sd = FALSE
)

Arguments

Details

The row names of the data frame returned are taxa. The columns are of type character and their names are the group names. For each group, the entry gives the mean relative abundance +/- the standard deviation and a compact letter display (CLD) for the group. See also the vignette "Compare Relative Abundances Among Treatments."

Value

A data frame of differences in relative abundances among treatments.

Examples

{
data("its.root")
temp1 <- comp_prepare_phyloseq(its.root)
temp2 <- comp_prepare_otu_table(temp1$expt.taxon.pc,
                               grps = "Label",
                               transformation = "sqrt_arc_sine")
comp_comparisons(otu.pc = temp2$otu.pc,
                 otu.pc.trans = temp2$otu.pc.trans,
                 grps = temp2$groups,
                 p.adjust.method = "BH",
                 pool.sd = TRUE)
}

Description

Calculates omnibus F tests to be included in a table comparing relative abundances of each taxon among treatments.

Usage

comp_make_f_tests(otu.pc.trans, grps, var.equal = FALSE)

Arguments

Details

The row names of the data frame returned are taxa. The column names are 'F', 'Prob', 'sig' and 'F_value'. The column 'sig' includes asterisks indicating the degree of significance and the 'F_value' column is the F value to 2 decimal places plus the asterisk(s) from 'sig'. See also the vignette "Compare Relative Abundances Among Treatments."

Value

A data frame of the F-test results.

Examples

{
data("its.root")
temp1 <- comp_prepare_phyloseq(its.root)
temp2 <- comp_prepare_otu_table(temp1$expt.taxon.pc,
                               grps = "Label",
                               transformation = "sqrt_arc_sine")
temp4 <- comp_make_f_tests(temp2$otu.pc.trans,
                           grps = temp2$groups,
                           var.equal = TRUE)
temp4
}

Description

Calculates means and standard deviation for each taxon to be included in a table comparing relative abundances of each taxon among treatments.

Usage

comp_means_sd(otu.pc)

Arguments

Details

The OTU table should be created with comp_prepare_otu_table. The data frame returned has taxa as row names. For each taxa, 'mean' is the mean relative abundance and 'sd' is the standard deviation. See also the vignette "Compare Relative Abundances Among Treatments."

Value

A data frame with mean relative abundance and standard deviations by taxon.

Examples

{
data("its.root")
temp1 <- comp_prepare_phyloseq(its.root)
temp2 <- comp_prepare_otu_table(temp1$expt.taxon.pc,
                               grps = "Label",
                               transformation = "sqrt_arc_sine")
temp3 <- comp_means_sd(temp2$otu.pc)
temp3
}

Description

Make OTU tables for making comparisons of relative abundances among treatments.

Usage

comp_prepare_otu_table(
  expt.taxon.pc,
  grps = "Treatment",
  transformation = "sqrt_arc_sine"
)

Arguments

Details

The transformation applied may be "none" or a user-supplied function name in quotation marks or any of the built-it transformations("arc_sine", "log_arc_sine", or "sqrt_arc_sine"). The "sqrt_arc_sine" has generally proven most effective. See also the vignette "Compare Relative Abundances Among Treatments."

Value

A list consisting of an OTU table with percentages, an OTU table with transformed data, and a vector of treatment groups.

Examples

{
data("its.root")
temp1 <- comp_prepare_phyloseq(its.root)
temp2 <- comp_prepare_otu_table(temp1$expt.taxon.pc,
                               grps = "Label",
                               transformation = "sqrt_arc_sine")
temp2
}

Description

Prepares a phyloseq object for making comparisons of relative abundances among treatments.

Usage

comp_prepare_phyloseq(expt, taxrank = "Phylum", pc.filter = 0.01)

Arguments

Details

For both returned phyloseq objects, the OTU table has been filtered to include only OTUs initially present at >= pc.filter times the original total count and only taxrank is included in the taxonomy table. For the second object in the list, the OTU table has been transformed to percentages of the total counts per sample. See also the vignette "Compare Relative Abundances Among Treatments."

Value

A list of two modified experiment level phyloseq objects.

Examples

{
data(its.root)
temp1 <- comp_prepare_phyloseq(its.root)
temp1
}

Description

Transform an angle in degrees to radians.

Usage

deg2rad(x)

Arguments

Value

Angle in radians.

Examples

deg2rad(90)

Description

Based on 16S rRNA gene sequences from Iowa loess soil

Usage

expt

Format

A phyloseq object with otu_table, sample_data, tax_table, phylogenetic tree and refseqs.

Depth

0-20 cm, 60-80cm

Site

HNC

Slope

Top, Middle, Bottom

Description

Formats a taxon so that it will be properly italicized in a ggpplot

Usage

format_taxon(x)

Arguments

Details

If a taxon begins with an upper case letter followed by lower case letters and does not contain an underscore, it is wrapped in asterisks. If it begins with an upper case letter followed by lower case letters and contains an underscore, the portion before the underscore is wrapped in asterisks, the underscore removed and any letters following the underscore left alone. If a taxon contains all upper case letters or digits it is not a proper taxon and is left alone.

Value

The string with asterisks properly inserted.

Examples

format_taxon("UAB")
format_taxon("Pseudomonas_B")
format_taxon("Pseudomonas")

Description

Generates a random character string of specified length.

Usage

generate_password(n, type = "alpha_numeric")

Arguments

Details

If type equals "alpha_numeric" (the default), only alpha-numeric characters are used to generate the password. If type does not equal "alpha_numeric" then at least one non-alpha-numeric symbol will be included in the password. In either case, the alpha characters used are both upper and lower case.

Value

A character string.

Examples

generate_password(8)

Description

Assign treatment groups based on pairwise t-tests.

Usage

get_groups(ptt.rslt, alpha = 0.05, rm.subset = FALSE)

Arguments

Details

This function aids in making letter assignments as to which treatments are significantly different. Also returns a square matrix of alpha values for all pairwise differences. This square matrix can serve as input to the multcompLetters function of the multcompView package which provides letter assignments. If rm.subset is FALSE, then groups such as {A,B} and {A, B, C} may be reported. This is redundant in the sense the {A, B} is a subset of {A, B, C}. In this case if rm.subset is FALSE, the group {A, B} is not reported.

Value

A list consisting of groups of treatment groups that are not significantly different and a matrix of p values.

See Also

make_letter_assignments

Examples

attach(airquality)
Month <- factor(Month, labels = month.abb[5:9])
ptt.rslt <- pairwise.t.test(Ozone, Month)
detach(airquality)
get_groups(ptt.rslt, alpha = 0.05, rm.subset = FALSE)

Description

Gets the ranges for the width and height of a ggplot panel.

Usage

get_plot_limits(plot)

Arguments

Details

Sometimes when adding text to a ggplot, the text is cutoff if it extends beyond the limits of plot panel. This function provides information enabling the user to extend the panel limits so that the text is not cutoff.

Value

A list: xmin, xmax, ymin, ymax giving the coordinates of the limits of a ggplot panel.

Examples

library(ggplot2)
data(iris)
plt <- ggplot(data=iris, aes(x=Species, y=Petal.Length)) + geom_boxplot()
get_plot_limits(plt)

Description

Calculates Good's coverage from a community data matrix with samples as rows and OTUs as columns.

Usage

goods(com)

Arguments

Value

A table with the headings number of singletons, number of sequences, and Good's coverage for each sample in rows.

References

Good, I. J. 1953. The Population Frequencies of Species and the Estimation of Population Parameters. Biometrika 40:237-264.

Examples

{
data(dune, package = "vegan")
goods(dune)
}

Description

Renames sequences in a multi-fasta file with the MD5 hash of each sequence.

Usage

hash_dna_seqs(seqs)

Arguments

Details

Taxa names for the ASV table returned by the R version of DADA2 are the sequences themselves. This function renames them with the MD5 hash of each sequences so that the results are directly comparable to QIIME2/DADA2 results.

Value

DNA sequences renamed with their hashes.

Examples

seqs <- ">ASV1
AGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAACGGTAACAGGAAG"
hash_dna_seqs(seqs)

Description

Based on ITS2 sequences amplified from corn roots.

Usage

its.root

Format

A phyloseq object with otu_table, sample_data and tax_table. The sample_data variables are:

P

Phosporous level, H or L

Genotype

One of three: 2, 3, and C

Label

A code for treatments: 2HR, 2LR, 3HR, 3LR, CHR, CLR

Description

Transform a percentage to the log of its arc sine.

Usage

log_arc_sine(x)

Arguments

Value

The common logarithm of the arcsine transformation of x.

Examples

log_arc_sine(x = 30.1)

Description

Makes multiple comparisons of the relative abundances of taxa between treatment groups using the pairwise.t.test. Data may be transformed by a user supplied function. Three are included in this package.

Usage

make_comparisons(
  expt,
  taxrank = "Phylum",
  grps = "Treatment",
  transformation = "none",
  pc.filter = 0.01,
  p.adjust.method = "BH",
  pool.sd = FALSE
)

Arguments

Details

This is essentially a wrapper around the functions comp_prepare_phyloseq(), comp_prepare_otu_table(), comp_means_sd(), comp_make_f_tests(), comp_comparisons() and comp_assemble(). Transformation may be "none" or a user-supplied function name in quotation marks or any of the built-it transformations ("arc_sine", "log_arc_sine", or "sqrt_arc_sine"). The "sqrt_arc_sine" has generally proven most effective.

Value

A list consisting of three data frames and a vector of treatment groups. The first data frame is comparison.table.giving for each row (taxon) the mean relative abundance, standard deviation, F statistic with significance indicated by asterisks, and then for each treatment group the mean+/-sd with a CLD indicating group membership. The second data frame is taxa.pc giving for each row (taxon) the relative abundance of each treatment group. The third data frame is taxa.pc.transformed. It is like the second data frame but the data has been transformed using the specified function.

See Also

arc_sine, log_arc_sine, sqrt_arc_sine, check_var

Examples

{
data(its.root)
make_comparisons(its.root,
taxrank = "Phylum",
grps = "Label",
transformation = "sqrt_arc_sine",
pc.filter = 0.01,
p.adjust.method ="BH",
pool.sd = TRUE)
}

Description

Makes letter assignments for treatment groups that are not significantly different.

Usage

make_letter_assignments(ptt.rslt, significance = 0.05)

Arguments

Details

Letter assignments are made using Piepho's algorithm.

Value

A named vector of letter assignments. Names are treatment groups.

References

Piepho, H. P. 2004. An algorithm for a letter-based representation of all-pairwise comparisons. Journal of Computational and Graphical Statistics **13**:456-466.

Examples

{
data(iris, package = "datasets")
ptt.rslt <- with(iris, pairwise.t.test(Petal.Length, Species, pool.sd = FALSE))
make_letter_assignments(ptt.rslt, significance = 0.05)
}

Description

Merge two data frames by their row names.

Usage

merge_2_frames(one, two)

Arguments

Details

Merges data frames by common row names. This function differs from merge.data.frames in that the merged data frame returned has row names and not a new column of the row names.

Value

A merged data frame.

Examples

{
common_rows <- paste0("ID_", 1:5)

df1 <- data.frame(
  Value_A = runif(5), 
  Category = sample(c("X", "Y"), 5, replace = TRUE),
  row.names = common_rows
)

df2 <- data.frame(
  Value_B = rnorm(5),
  Flag = sample(c(TRUE, FALSE), 5, replace = TRUE),
  row.names = common_rows
)

merge_2_frames(df1, df2)
}

Description

Makes ordination axis labels that include, if appropriate, the % of the total variance explained by each axis.

Usage

ord_labels(ord)

Arguments

Details

If there are no eigenvalues in ord, or if any eigenvalues are less than 0, each element of the vector returned has the form "DIMn" where n is the axis number. Otherwise, each element of the vector returned has the form Axisn xx.x % where Axis is taken from the vector of eigenvalues in ord if they are named or simply "DIM" if they are not, n is the number of the axis, and xx.x is the % of total variance explained by the axis.

For this function to work correctly, ord should be created in one of the following ways:

1. As an unconstrained ordination using vegan::rda. In this case the labels have the form PCAn xx.x %

2. As a PCoA made with stats::cmdscale. In this case the labels have the form DIMn xx.x %.

3. As a CA made with vegan::ca. In this case the labels have the form CAn xx.x %.

Value

A character vector, each element of which can be used to label the corresponding axis of an ordination plot.

See Also

rda_labels()

Examples

{
# For PCA using rda:
data("dune", package = "vegan")
dune_hel <- vegan::decostand(dune, method = "hellinger")
pca <- vegan::rda(dune_hel)
print("For the PCA case:")
print(ord_labels(pca)[1:2])
cat("\n")
# For PCoA with negative eigenvalues
d <- vegan::vegdist(dune)
pcoa <- stats::cmdscale(d, k = nrow(dune)-1, eig = TRUE, add = FALSE)
print("For the PCoA case with negative eigenvalues:")
print(ord_labels(pcoa)[1:2])
cat("\n")
# For PCoA without negative eigenvalues
pcoa <- stats::cmdscale(d, k = nrow(dune)-1, eig = TRUE, add = TRUE)
print("For the PCoA case without negative eigenvalues:")
print(ord_labels(pcoa)[1:2])
cat("\n")
# For correspondence analysis
ca_ord <- vegan::ca(dune)
(ord_labels(ca_ord))
print("For the CA case:")
print(ord_labels(ca_ord))
}

Description

Makes PCA axis labels that include the

Usage

pca_labels(pca)

Arguments

Details

Each element of the vector returned has the form "PCAn xx.x

Value

A character vector, each element of which can be used to label the corresponding axis of a PCA plot.

Examples

{
data(dune, package = "vegan")
dune_hel <- vegan::decostand(dune, method = "hellinger")
pca_ord <- vegan::rda(dune_hel)
pca_labels(pca_ord)
}

Description

Retuns the number of permutaions of n things taken r at a time.

Usage

perm(n, r, repetition = FALSE)

Arguments

Value

An integer giving how many ways m things can be drawn n at a time.

Examples

perm(10, 5)
perm(10, 5, repetition = TRUE)

Description

Used in Case 3 of the vignette make_comparisons

Usage

plot_df

Format

A data file in long format used for a ggplot. The sample_data variables are:

Treatment

A code for genotype (2, 3, or C), P level (H or L) and sample type (R)

Family

One of the families in Gigasporaceae

Percent

Percent of total counts for family and treatment combination.

Description

Extracts a QIIME2/DADA2 transition stats file and returns a plot.

Usage

plot_transition_stats(trans_stats.qza)

Arguments

Details

Beginning with QIIME2 version 2025.7 the DADA2 plugin requires output of a compressed (qza) file of the transition stats. This function makes a ggplot plot from the data in that file. It is useful in determining how well DADA2 has corrected read errors.

Value

A ggplot of the transition probabilities

Examples

qza <- system.file("extdata", "base-transition-stats.qza", package = "QsRutils")
plot_transition_stats(qza)

Description

Allows subsetting of a phyloseq object according to the relative abundance of OTUs in a minimal number of samples. Returns a logical vector of OTUs that are at least n% of the sequences in at least m samples.

Usage

prop_filter(x, n, m)

Arguments

Details

The functions creates a logical vector to be used in subsetting a phyloseq object according to the relative abundance of OTUs in a given number of samples. For example, if n = 1 and m = 2, then the OTUs to be kept must represent at least 1% of the sequences in at least 2 samples. The vector is then used as an argument to the phyloseq object 'prune_taxa'.

Value

A logical vector of OTUs to keep.

Examples

{
data("its.root")
prop_filter(x = its.root, n = 1, m = 5)
}

Description

The QsRutils package contains functions I have written to make some aspects of using phyloseq and vegan simpler. I originally called the package MyRutils, but that does not make much sense if I am posting it publically!

Description

Transform an angle in radians to degrees.

Usage

rad2deg(x)

Arguments

Value

Angle in degrees.

Examples

rad2deg(pi * 0.5)

Description

Makes RDA axis labels that include the axis, that is by the constrained portion of the analysis.

Usage

rda_labels(rda)

Arguments

Details

Each element of the vector returned has the form "RDAn xx.x n is the number of the RDA axis and xx.x is the explained by the axis. The percent of total variance is for the constrained portion only.

Value

A character vector, each element of which can be used to label the corresponding axis of an RDA plot.

See Also

ord_labels()

Examples

{
# Using vegan::rda()
data(dune, package = "vegan")
data(dune.env, package = "vegan")
dune.Manure <- vegan::rda(dune ~ Manure, dune.env)
print("For the rda case:")
print(rda_labels(dune.Manure)[1:2])
cat("\n")
# Using vegan::cca()
data(varespec, package = "vegan")
data(varechem, package = "vegan")
vare.cca <- vegan::cca(varespec ~ Al + P*(K + Baresoil), data=varechem)
print("For the cca case:")
print(rda_labels(vare.cca)[1:2])
}

Description

Roots an unrooted tree in a phyloseq object

Usage

root_phyloseq_tree(phylo)

Arguments

Details

The tree is rooted by the longest terminal branch.

Value

The same phyloseq object with a rooted tree

Examples

{
data("expt")
expt.rooted <- root_phyloseq_tree(expt)
ape::is.rooted(phyloseq::phy_tree(expt.rooted))
}

Description

Calculates the standard error of a numeric vector

Usage

se(x)

Arguments

Details

NA values are ignored.

Value

The standard error of the numeric vector

Examples

x <- c(1,2,3,4,5, NA)
se(x)

Description

Transform a percentage to the square root of its arc sine.

Usage

sqrt_arc_sine(x)

Arguments

Value

The square root of the arcsine transformation of x.

Examples

sqrt_arc_sine(30.1)

Description

Normalize sample counts using scaling with ranked subsampling (SRS)

Usage

srs_p(p)

Arguments

Details

This is an alternative to "rarefying" an OTU table to a constant sample size. The phyloseq object submitted must be pruned to the desired sample size before using this function.

Value

a phyloseq object including an OTU table, all sample sums equal.

Author(s)

John Quensen

References

Beule L, Karlovsky P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ. 2020;8:e9593.

Examples

{
data("expt")
print("Sample sums before srs_p")
print(phyloseq::sample_sums(expt))
cat("\n")
expt_srs <- srs_p(p =  expt)
print("Sample sums after srs_p")
print(phyloseq::sample_sums(expt_srs))
}

Description

Subset physeq by refseq lengths

Usage

subset_by_refseq_lengths(p, min_len = 252, max_len = 255)

Arguments

Details

Sometimes, due to sequencing errors, reference sequences have lengths less than and/or greater than the expected amplicon length. This function offers an easy way of removing such extraneous reference sequences from an experiment level phyloseq object. The default values for min_len and max_len are fro the V4 region of the 16S rRNA gene.

Value

A phyloseq object filtered to have reference sequences within the length range specified.

Examples

{
data("expt")

print("Refseq length range before subsetting:")
expt@refseq@ranges@width |> 
  summary() |> 
  print()
cat("\n")

expt_filt <- subset_by_refseq_lengths(p = expt,
                                      min_len = 400,
                                      max_len = 420)
                                     
 print("Refseq length range after subsetting:")
expt_filt@refseq@ranges@width |> 
  summary() |> 
  print()
}

Description

Subsets a distance matrix.

Usage

subset_dist(physeq, d.matrix)

Arguments

Details

Some distance matrices take a long time to calculate for large data sets. This is especially true of unifrac and generalized unifrac distances calculated by GUniFracs. If distances are first calculated from data in a large experiment level phyloseq object and then it is desired to perform PERMANOVA (with adonis) on a subset of that object, this function provides a means of sub-setting the distance matrix so that it does not have to be calculated again for the subset data. The arguments are the distance matrix for the original phyloseq object and the smaller phyloseq object subset from the original.

Value

A distance matrix of smaller dimensions.

References

Chen J, Bittinger K, Charlson ES et al. (2012) Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics, 28, 2106-2113.

Examples

{
otu <- veganotu(its.root)
d <- vegan::vegdist(otu)
print("Before subsetting:")
print(dim(as.matrix(d)))
cat("\n")
p <- phyloseq::subset_samples(its.root, P_Location == "LR")
d_sub <- subset_dist(p, d)
print("After subsetting:")
print(dim(as.matrix(d_sub)))
}

Description

Applies any vegan decostand standardization method to a phyloseq OTU table.

Usage

vegan_stand(physeq, method = "hellinger", ...)

Arguments

Value

Returns a phyloseq object with transformed OTU table.

Examples

{
data("expt")
print("Before standardization, first 5 rows and columns:")
print(veganotu(expt)[1:5, 1:5])
cat("\n")

expt_mod <- vegan_stand(expt, method = "hellinger")
print("After standardization, first 5 rows and columns:")
print(veganotu(expt_mod)[1:5, 1:5])
}

Description

Extracts a vegan compatible OTU table from a phyloseq object.

Usage

veganotu(physeq)

Arguments

Value

A matrix with samples in rows and OTUs in columns.

Examples

{
data("expt")
# Show only first 5 columns and rows:
veganotu(physeq = expt)[1:5, 1:5]
}

Description

Extracts a sample data table from a phyloseq object.

Usage

vegansam(physeq)

Arguments

Value

A data frame with samples in rows and factors and/or variables in columns.

Examples

{
data("expt")
vegansam(physeq = expt)
}