Calculate the alpha diversity indices

Description

This function will calculate the alpha diversity indices for the total, abundant and rare biospheres.

Usage

alphaDiversity(otutab, siteInCol = FALSE, taxhead = NULL, threshold = 1,
    percent = FALSE, write = FALSE, ...)

Arguments

Details

The rare biosphere is defined by the relative abundance cutoffs (which is the "threshold" argument in this function) (Lynch and Neufeld, 2015). This update (version 0.1.2) removed the dependencies on functions "specnumber", "diversity" and "estimateR" from the R package "vegan" (Oksanen et al, 2013), and removed the "gini" function from package 'reldist' (http://www.stat.ucla.edu/~handcock/RelDist).

Value

The function will return a list of length 3, including indices of observed, shannon, simpson, invsimpson, chao1, chao2, and evenness.

Author(s)

Sizhong Yang <yanglzu@163.com>

References

Lynch MDJ, Neufeld JD (2015). Ecology and exploration of the rare biosphere. Nature Reviews Microbiology 13: 217-229.

Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O'Hara RB et al (2013). vegan: Community Ecology Package. R package version 2.0-7. http://CRAN.R-project.org/package=vegan.

Examples

data(otumothur)

test1 <- alphaDiversity(otutab = otumothur, siteInCol = TRUE,
    taxhead = "taxonomy", threshold = 1, percent = FALSE, write = FALSE)

test2 <- alphaDiversity(otutab = otumothur[,-ncol(otumothur)], siteInCol = TRUE,
    taxhead = NULL, threshold = 1, percent = FALSE, write = FALSE)

Generate barplot with custom controls on the x axis labels

Description

barplot with custom controls on the x axis labels, e.g. rotation.

Usage

bplot(data, srt = 45, yoff = 0.05, dataoff = 0.025, barcol = "grey", grid = TRUE, ...)

Arguments

Examples

data(otumothur)

summaryInfo <- otuReport(otutab = otumothur, siteInCol = TRUE, taxhead = "taxonomy",
    platform = "qiime", percent = FALSE, taxlevel = "phylum", collap = ";")

length(summaryInfo)
names(summaryInfo)
summaryInfo[[1]]

bplot(summaryInfo[["taxaFreqs"]])

calculate the Bray-Curtis dissimilarity

Description

This function will calculate the Bray-Curtis distance matrix for community data.

Usage

calc_bc(df)

Arguments

Details

This function will calculate the dissimilarity and output as lower triangular distance matrix.

Value

The function returns lower triangular distrance matrix.

Author(s)

Sizhong Yang <yanglzu@163.com>

Examples

data(otuqiime)
mat <- calc_bc(t(otuqiime[,-ncol(otuqiime)]))
mat2 <- as.matrix(mat)
dim(mat2)

Contribution of rare/abundant biosphere to the total Bray-Curtis dissimilarity

Description

function to calculate the contribution (in fraction) of the abundant or rare biosphere to the Bray-Curtis dissimilarity of the whole community.

Usage

contrib(otutab, siteInCol = TRUE, taxhead = NULL, threshold = 1, percent = FALSE,
    check = "rare", write = FALSE, plot = FALSE, ...)

Arguments

Details

In this function, the rare biosphere is defined by the relative abundance cutoffs (argument threshold). The Bray-Curtis distance between pairwise samples was partitioned. The Bray-Curtis measure is a scaled summation of abundance differences between two communities and can thus be partitioned for a subset population from the community (Shade et al 2014, Yang et al 2017).

Value

The function will return a data frame of five columns. The first two columns specify the sample names whose Bray-Curtis distance were calculated. The third and forth columns give the distances respectively based on the whole community OTU data or the subset data. The last column gives the contribution (in fraction, not percentage) of the subset data for each pair of samples.

Author(s)

Sizhong Yang <yanglzu@163.com>

References

Shade A, Jones SE, Caporaso JG, Handelsman J, Knight R, Fierer N et al (2014). Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. Mbio 5: e01371-01314.

Yang S, Winkel M, Wagner D, Liebner S (2017). Community structure of rare methanogenic archaea: insight from a single functional group. FEMS Microbiology Ecology: fix126.

Examples

data(otuqiime)

result <- contrib(otutab = otuqiime, siteInCol = TRUE, taxhead = "taxonomy",
    threshold = 1, percent = FALSE, check = "abund", plot = FALSE)

names(result)
head(result)

reshape the heirarchical taxonomy

Description

reformat the taxonomy with the last clear assignment and changing the prefix

Usage

fillTax(x, split = ';', prefix=TRUE, fillAll=TRUE)

Arguments

See Also

fillTax2, slimTax

Examples

  test = 'd__Bacteria;p__Planctomycetota;c__Planctomycetes;o__Gemmatales;f__Gemmataceae;g__;s__'
  fillTax(x = test, split = ';', prefix=TRUE, fillAll=TRUE)

reshape the heirarchical taxonomy

Description

this function is similar to fillTax, except that this function has no parameter of "fillAll"

Usage

fillTax2(x, split = ';', prefix=TRUE)

Arguments

See Also

fillTax, slimTax

Examples

  test = 'd__Bacteria;p__Planctomycetota;c__Planctomycetes;o__Gemmatales;f__Gemmataceae;g__;s__'
  fillTax2(x = test, split = ';', prefix=TRUE)

Convert lower triangular distance matrix into data frame

Description

This function will convert lower triangular distance matrix into a 3-column, long-format data frame.

Usage

matrixConvert(triMatrix, colname = c("sp1", "sp2", "dist"))

Arguments

Details

This function will call the "melt" function in the reshape2 package, and convert the pairwise values in the lower triangular distance matrix into long-format data frame.

Value

The function returns long format of data frame, with 3 columns. The first two columns give the pairwise names and the third column contains values in the matrix.

Author(s)

Sizhong Yang <yanglzu@163.com>

Examples

data(otuqiime)
mat <- calc_bc(t(otuqiime[,-ncol(otuqiime)]))
mat.m <- matrixConvert(mat, colname = c("sp1", "sp2", "bray"))

An example OTU table with samples and taxonomy in rows and otus in columns

Description

A data set containing bacterial counts from the North Temperate Lakes Microbial Observatory. Due to the last row contain taxonomy, read.table function with default setting will treat the type of each column as factor.

Usage

data("otu4type")

Format

A data frame with 591 columns (OTUs) and 454 rows (453 samples plus 1 taxonomy).

Details

This data show examples of "SiteInRow" if there is a taxonomy rows. When read in, the column with numeric counts will be marked as "factor". This data set could be transposed to correct type with "typeConvert" function.

Source

see the entire dataset at https://github.com/cran/OTUtable/tree/master/data

Examples

data(otu4type)
sapply(otu4type, class)
new <- typeConvert(otu4type)
sapply(new, class)

collapse a OTU table at given level.

Description

The function will collapse a structured OTU table at given taxonomic level.

Usage

otuCollap(otutab, taxto, siteInCol = TRUE, taxhead = "taxonomy",
    pattern = ";", collap = ";")

Arguments

Details

This function will directly collapse the otu table according to numeric position of structured taxonony. This function can also collapse data with structured format like the example OTU table.

Value

The function will return a collapsed OTU table.

Author(s)

Sizhong Yang <yanglzu@163.com>

See Also

otuReport

Examples

data(otuqiime)
dim(otuqiime)

result <- otuCollap(otutab = otuqiime, taxto = 2, siteInCol = TRUE,
    taxhead = "taxonomy", pattern = ";")

dim(result)

Summarize the community structure and abundance with OTU table

Description

The function will summarize the frequency, abundance at given taxonomic level for the input OTU table. This function could quickly give the summary information for user when adding these values in describing the community structure in a paper.

Usage

otuReport(otutab, siteInCol = TRUE, taxhead = "taxonomy", platform = "mothur",
    pattern = ";", prefix = TRUE, percent = FALSE, taxlevel = "phylum",
    collap = ";")

Arguments

Details

This function was designed according to the structured taxonomy generated by mothur or qiime. So far, the function support the 7 levels of hierarchical taxonomy from kingdom to species.

Value

If the input otu table is in counts, the function will return a list of results summarizing 9 different aspects for the microbial community as follows:

For the relative abundance otu input, the function will omit four summary table regarding absolute reads ("reads", "readSum", "readFrac" and "readFracSum").

Author(s)

Sizhong Yang <yanglzu@163.com>

See Also

subOTU, otuCollap

Examples

# summary the otu table in qiime format

data(otuqiime)

summaryInfo <- otuReport(otutab = otuqiime, siteInCol = TRUE, taxhead = "taxonomy",
    platform = "qiime", pattern = ";", prefix = TRUE, percent = FALSE, taxlevel = "class")

length(summaryInfo)
names(summaryInfo)
summaryInfo[[1]]

# summary otu table in mothur format

data(otumothur)

summaryInfo <- otuReport(otutab = otumothur, siteInCol = TRUE, taxhead = "taxonomy",
    platform = "mothur", pattern = ";", percent = FALSE, taxlevel = "phylum", collap = ";")

length(summaryInfo)
names(summaryInfo)
summaryInfo[[1]]

op <- par(mar = c(8,6,2,1)+0.1)
bplot(summaryInfo[["taxaFreqs"]])
par(op)

# summary otu table of relative abundance

per <- subOTU(otutab = otuqiime, siteInCol = TRUE, taxhead = "taxonomy",
    percent = FALSE, choose = "all", outype = "Relabund", sort = TRUE)

summaryInfo <- otuReport(otutab = per, siteInCol = TRUE, taxhead = "taxonomy",
    platform = "qiime", pattern = ";", percent = TRUE, taxlevel = "class")

length(summaryInfo)
names(summaryInfo)

OTU table generated from 8 lakes over 4 years

Description

A data set containing bacterial counts from the North Temperate Lakes Microbial Observatory, the taxonomy column is in mothur format. The data set is published in Msphere (Linz et al, 2017). The data set is identical to otuqiime except for the taxonomy column in qiime format.

Usage

data("otumothur")

Format

A data frame with 454 columns (453 samples plus 1 taxonomy) and 591 rows (OTUs). The taxonomy is given in mothur format, with hierarchical taxonomy from kingdom to species separated with semiclone.

Details

The full version of the data set is published in Msphere (Linz et al 2017). The first two letters of sample names denote the sampling site (e.g. "CB"), followed with epilimnion or hypolimnion ("E" or "H") and sampling date ("01OCT07"). The original data set have replicates ("R1" and "R2"), this data set only extracted the subset of "R2" (with extension ".R2" in sample names). The data set is identical to otuqiime except that the taxonomy is in format generated by software 'mothur' (Schloss et al 2009).

Source

see the entire dataset at https://github.com/cran/OTUtable/tree/master/data

References

Linz AM, Crary BC, Shade A, Owens S, Gilbert JA, Knight R et al (2017). Bacterial community composition and dynamics spanning five years in freshwater bog lakes. Msphere 2: e00169-00117. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB et al (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75: 7537-7541.

Examples

data(otumothur)
dim(otumothur)
sapply(otumothur, class)
head(otumothur$taxonomy)

OTU table generated from 8 lakes over 4 years

Description

A data set containing bacterial counts from the North Temperate Lakes Microbial Observatory, the taxonomy column is in format generated by software platfor 'QIIME' (Caporaso et al 2010). The data set is published in Msphere (Linz et al 2017).

Usage

data("otuqiime")

Format

A data frame with 454 columns (453 samples plus 1 taxonomy) and 591 rows (OTUs).

Details

The first two letters of sample names denote the sampling site (e.g. "CB"), followed with epilimnion or hypolimnion ("E" or "H") and sampling date ("01OCT07"). The original data set have replicates ("R1" and "R2"), this data set only extracted the subset of "R2" (with extension ".R2" in sample names).

Source

see https://github.com/cran/OTUtable/tree/master/data

References

Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK et al (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7: 335-336. Linz AM, Crary BC, Shade A, Owens S, Gilbert JA, Knight R et al (2017). Bacterial community composition and dynamics spanning five years in freshwater bog lakes. Msphere 2: e00169-00117.

Examples

data(otuqiime)
dim(otuqiime)
names(otuqiime)[1:10]
rownames(otuqiime)[1:10]
head(otuqiime$taxonomy)

Partition the Bray-Curtis distance dissimilarity

Description

The pbray function partitions the Bray-Curtis distance matrix based on the who community and the subset of the community data.

Usage

pbray(allComm, subComm, tolower = TRUE)

Arguments

Details

The Bray-Curtis dissimilarity is a scaled summation of abundance differences between two communities, it is thus could be partitioned between two samples attributable to a subset of the community (Shade et al., 2014; Yang et al., 2017). Note, the pbray function requires the input for "allComm" and "subComm" keep consistent either in either counts or relative abundance. If the input for "allComm" and "subComm" are the same data, the function will return the Bray-Curtis matrix for the whole community.

Value

The function returns a distance matrix by using the subset community against the whole community data.

Author(s)

Sizhong Yang <yanglzu@163.com>

References

Shade A, Jones SE, Caporaso JG, Handelsman J, Knight R, Fierer N, and Gilbert JA. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. Mbio, 2014, 5(4): e01371-01314.

Yang S, Winkel M, Wagner D, and Liebner S. Community structure of rare methanogenic archaea: insight from a single functional group. FEMS Microbiology Ecology, 2017: fix126.

See Also

function contrib(), matrixConvert().

Examples

data(otumothur)

subotus <- subOTU(otutab = otumothur, taxhead = "taxonomy", siteInCol = TRUE,
    percent = FALSE, choose = "rare", outype = "counts", sort = FALSE)
pRare <- pbray(allComm = t(otumothur[,-454]), subComm = t(subotus[,-454]))
class(pRare)

# convert to long format data frame

longdist <- matrixConvert(pRare, colname = c("sp1", "sp2", "bray"))

Summarize different subgroups in the rare biosphere

Description

The function discriminates different fractions of rare biosphere from the input community data, based on the ratio between the maximum and minimum abundance (for details, please see Yang et al. 2017).

Usage

rareBiosphere(otutab, siteInCol = TRUE, taxhead= NULL, percent = FALSE,
    threshold = 1, cutRatio = 100, cutPERare = 5, ...)

Arguments

Details

The rare biopshere constitutes different fractions of rarity. The conditionally rare taxa (CRT) are generally rare across samples but can become abundant in some samples. In contrast, the permanently rare taxa (PERare) are persistently low in abundance. Different levels of rarity may have practical implications and suggest potential roles in metabolism and ecological functions (Lynch and Neufeld, 2015; Yang et al., 2017).

This function will filter the rare biophere from the input OTU table and then discriminate different rare fractions based on the ratio between the maximum and minimum non-zero absolute abundance, which has been as an technical alternative of skewness (please see Yang et al. 2017 for details).

The function only works with otu table with absolute abundance, for the absolute reads enable to identify of absolute singletons and doubletons.

Value

The function returns a list of 4 data frames.

Author(s)

Sizhong Yang <yanglzu@163.com>

References

Lynch MDJ, Neufeld JD (2015). Ecology and exploration of the rare biosphere. Nature Reviews Microbiology 13: 217-229.

Yang S, Winkel M, Wagner D, Liebner S (2017). Community structure of rare methanogenic archaea: insight from a single functional group. FEMS Microbiology Ecology: fix126.

Examples

data(otumothur)

example <- rareBiosphere(otutab = otumothur, siteInCol = TRUE, taxhead = "taxonomy",
    percent = FALSE, threshold = 1, cutRatio = 100, cutPERare = 5)
length(example)
names(example)
head(example[["summaryTable"]])
head(example[["CRT"]])

example2 <- rareBiosphere(otutab = otumothur[,-454], siteInCol = TRUE,
    taxhead = NULL, percent=FALSE, threshold = 1, cutRatio = 100, cutPERare = 5)
length(example2)
names(example2)

reshape heirarchical taxonomy

Description

The function will trunck the heirarchical taxonomy by "from" and "to".

Usage

slimTax(x, from, to, separator =';', jump=FALSE)

Arguments

Details

This function will reformat the taxonomy by the taxonomic ranks specified by "from" and "to".

Value

The function will return the reshaped taxonomy.

Author(s)

Sizhong Yang <yanglzu@163.com>

See Also

fillTax

Examples

test = 'd__Bacteria;p__Planctomycetota;c__Planctomycetes;o__Gemmatales;f__Gemmataceae;g__;s__'
slimTax(test, from=2, to=5)

data(otuqiime)
dim(otuqiime)

result <- slimTax(x = as.character(otuqiime$taxonomy), from=2, to=5, separator =';', jump=FALSE)

dim(result)

Subset a OTU table

Description

This function subset a OTU table according to the specified threshold of mean relative abundance.

Usage

subOTU(otutab, siteInCol = TRUE, taxhead = NULL, percent = TRUE, choose = "rare",
       threshold = 1, outype = "Relabund", sort = TRUE, write = FALSE)

Arguments

Details

The function will subset the OTU table to abundant (choose = "abundant") or rare biosphere (choose = "rare") according to the given relative abundance threshold of rare biosphere (Lynch and Neufeld, 2015). It will also keep the whole community without subsetting, if choose = "all". The output could be relative abundance (outype = "Relabund") or absolute counts (outype = "counts"). If sort is TRUE, the output result will be sorted by the descending order of mean relative abundance across samples.

Value

This function will return an OTU table (data frame) according to the specified arguments.

Author(s)

Sizhong Yang <yanglzu@163.com>

References

Lynch MDJ, Neufeld JD (2015). Ecology and exploration of the rare biosphere. Nature Reviews Microbiology 13: 217-229.

Examples

data(otuqiime)

example1 <- subOTU(otutab = otuqiime, siteInCol = TRUE, taxhead ="taxonomy",
    percent = FALSE, choose = "abundant", threshold = 1, outype = "Relabund")
dim(example1)

example2 <- subOTU(otutab = otuqiime[,-454], siteInCol = TRUE, taxhead = NULL,
    percent = FALSE, choose = "rare", threshold = 1, outype = "counts")
dim(example2)

Transpose the data frame if there is data type conversion.

Description

function to transpose the data frame if there is data type conversion.

Usage

typeConvert(otutab,taxhead = NULL)

Arguments

Details

This function is to convert the numeric values into right type so that the downstream numeric calculation could be processed without type error.

Value

This function returns a transposed OTU table. In the source OTU table the numeric values showing "character" or "factor" will be converted to right types.

Author(s)

Sizhong Yang <yanglzu@163.com>

Examples

data(otu4type)
sapply(otu4type, class)
new <- typeConvert(as.data.frame(t(otu4type)), taxhead = "taxonomy")
sapply(new, class)