Package 'microbial' reference manual

Title:	Do 16s Data Analysis and Generate Figures
Description:	Provides functions to enhance the available statistical analysis procedures in R by providing simple functions to analysis and visualize the 16S rRNA data.Here we present a tutorial with minimum working examples to demonstrate usage and dependencies.
Authors:	Kai Guo [aut, cre], Pan Gao [aut]
Maintainer:	Kai Guo <[email protected]>
License:	GPL-3
Version:	0.0.22
Built:	2024-09-17 02:43:41 UTC
Source:	https://github.com/guokai8/microbial

replace p value with star

Description

replace p value with star

Usage

.getstar(x)
.getstar(x)

Arguments

`x`	a (non-empty) numeric data values

LEfse function

Description

LEfse function

Usage

.lda.fun(df)
.lda.fun(df)

Arguments

`df`	a dataframe with groups and bacteria abundance

calcaute beta diversity

Description

calcaute beta diversity

Usage

betadiv(physeq, distance = "bray", method = "PCoA")
betadiv(physeq, distance = "bray", method = "PCoA")

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`distance`	A string character specifying dissimilarity index to be used in calculating pairwise distances (Default index is "bray".). "unifrac","wunifrac","manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup" , "binomial", "chao", "cao" or "mahalanobis".
`method`	A character string specifying ordination method. All methods available to the `ordinate` function of `phyloseq` are acceptable here as well.

Value

list with beta diversity data.frame and PCs

Author(s)

Kai Guo

Examples

{
data("Physeq")
phy<-normalize(physeq)
res <- betadiv(phy)
}
{
data("Physeq")
phy<-normalize(physeq)
res <- betadiv(phy)
}

PERMANOVA test for phyloseq

Description

PERMANOVA test for phyloseq

Usage

betatest(physeq, group, distance = "bray")
betatest(physeq, group, distance = "bray")

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`group`	(Required). Character string specifying name of a categorical variable that is preferred for grouping the information. information.
`distance`	A string character specifying dissimilarity index to be used in calculating pairwise distances (Default index is "bray".). "unifrac","wunifrac","manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup" , "binomial", "chao", "cao" or "mahalanobis".

Value

PERMANOVA test result

Author(s)

Kai Guo

Examples

{
data("Physeq")
phy<-normalize(physeq)
beta <-betatest(phy,group="SampleType")
}
{
data("Physeq")
phy<-normalize(physeq)
beta <-betatest(phy,group="SampleType")
}

Identify biomarker by using randomForest method

Description

Identify biomarker by using randomForest method

Usage

biomarker(
  physeq,
  group,
  ntree = 500,
  pvalue = 0.05,
  normalize = TRUE,
  method = "relative"
)
biomarker(
  physeq,
  group,
  ntree = 500,
  pvalue = 0.05,
  normalize = TRUE,
  method = "relative"
)

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`group`	group. A character string specifying the name of a categorical variable containing grouping information.
`ntree`	Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
`pvalue`	pvalue threshold for significant results from kruskal.test
`normalize`	to normalize the data before analysis(TRUE/FALSE)
`method`	A list of character strings specifying `method` to be used to normalize the phyloseq object Available methods are: "relative","TMM","vst","log2".

Value

data frame with significant biomarker

Author(s)

Kai Guo

Examples


data("Physeq")
res <- biomarker(physeq,group="group")

data("Physeq")
res <- biomarker(physeq,group="group")

contruction of plylogenetic tree (extreme slow)

Description

contruction of plylogenetic tree (extreme slow)

Usage

buildTree(seqs)
buildTree(seqs)

Arguments

seqs

DNA sequences

Value

tree object

Author(s)

Kai Guo

The physeq data was modified from the (Data) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample (2011)

Description

Published in PNAS in early 2011. This work compared the microbial communities from 25 environmental samples and three known “mock communities” – a total of 9 sample types – at a depth averaging 3.1 million reads per sample. Authors were able to reproduce diversity patterns seen in many other published studies, while also invesitigating technical issues/bias by applying the same techniques to simulated microbial communities of known

References

Caporaso, J. G., et al. (2011). Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS, 108, 4516-4522. PMCID: PMC3063599

Examples

data(Physeq)
data(Physeq)

The physeq data was modified from the (Data) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample (2011)

Description

References

Caporaso, J. G., et al. (2011). Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS, 108, 4516-4522. PMCID: PMC3063599

Examples

data(Physeq)
plot_richness(physeq, x="SampleType", measures=c("Observed", "Shannon"))
data(Physeq)
plot_richness(physeq, x="SampleType", measures=c("Observed", "Shannon"))

Calculate differential bacteria with DESeq2

Description

Calculate differential bacteria with DESeq2

Usage

difftest(
  physeq,
  group,
  ref = NULL,
  pvalue = 0.05,
  padj = NULL,
  log2FC = 0,
  gm_mean = TRUE,
  fitType = "local",
  quiet = FALSE
)
difftest(
  physeq,
  group,
  ref = NULL,
  pvalue = 0.05,
  padj = NULL,
  log2FC = 0,
  gm_mean = TRUE,
  fitType = "local",
  quiet = FALSE
)

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`group`	group (DESeq2). A character string specifying the name of a categorical variable containing grouping information.
`ref`	reference group
`pvalue`	pvalue threshold for significant results
`padj`	adjust p value threshold for significant results
`log2FC`	log2 Fold Change threshold
`gm_mean`	TRUE/FALSE calculate geometric means prior to estimate size factors
`fitType`	either "parametric", "local", or "mean" for the type of fitting of dispersions to the mean intensity.
`quiet`	whether to print messages at each step

Value

datafame with differential test with DESeq2

Author(s)

Kai Guo

Examples

 
data("Physeq")
res <- difftest(physeq,group="group")

data("Physeq")
res <- difftest(physeq,group="group")

distinguish colors for making figures

Description

distinguish colors for making figures

Usage

distcolor
distcolor

Format

An object of class character of length 41.

Author(s)

Kai Guo

do anova test and return results as data.frame

Description

do anova test and return results as data.frame

Usage

do_aov(x, group, ...)
do_aov(x, group, ...)

Arguments

`x`	data.frame with sample id as the column name, genes or otu as rownames
`group`	group factor used for comparison
`...`	parameters to anova_test

Author(s)

Kai Guo

Examples

{
data("ToothGrowth")
do_aov(ToothGrowth,group="supp")
}
{
data("ToothGrowth")
do_aov(ToothGrowth,group="supp")
}

do t.test

Description

do t.test

Usage

do_ttest(x, group, ref = NULL, ...)
do_ttest(x, group, ref = NULL, ...)

Arguments

`x`	data.frame with sample id as the column name, genes or otu as rownames
`group`	group factor used for comparison
`ref`	reference group
`...`	parameters to t_test

Author(s)

Kai Guo

Examples

{
data("mtcars")
do_ttest(mtcars,group="vs")
do_ttest(mtcars,group="cyl",ref="4")
}
{
data("mtcars")
do_ttest(mtcars,group="vs")
do_ttest(mtcars,group="cyl",ref="4")
}

do wilcox test

Description

do wilcox test

Usage

do_wilcox(x, group, ref = NULL, ...)
do_wilcox(x, group, ref = NULL, ...)

Arguments

`x`	data.frame with sample id as the column name, genes or otu as rownames
`group`	group factor used for comparison
`ref`	reference group
`...`	parameters to wilcox_test

Author(s)

Kai Guo

Examples

{
data("mtcars")
do_wilcox(mtcars,group="vs")
do_wilcox(mtcars,group="cyl",ref="4")
}
{
data("mtcars")
do_wilcox(mtcars,group="vs")
do_wilcox(mtcars,group="cyl",ref="4")
}

Do the generalized linear model regression

Description

Do the generalized linear model regression

Usage

glmr(
  physeq,
  group,
  factors = NULL,
  ref = NULL,
  family = binomial(link = "logit")
)
glmr(
  physeq,
  group,
  factors = NULL,
  ref = NULL,
  family = binomial(link = "logit")
)

Arguments

`physeq`	phyloseq object
`group`	the group factor to regression
`factors`	a vector to indicate adjuested factors
`ref`	the reference group
`family`	binomial() or gaussian()

Author(s)

Kai Guo

Examples


data("Physeq")
phy<-normalize(physeq)
fit <-glmr(phy,group="SampleType")

data("Physeq")
phy<-normalize(physeq)
fit <-glmr(phy,group="SampleType")

Identify biomarker by using LEfSe method

Description

Identify biomarker by using LEfSe method

Usage

ldamarker(physeq, group, pvalue = 0.05, normalize = TRUE, method = "relative")
ldamarker(physeq, group, pvalue = 0.05, normalize = TRUE, method = "relative")

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`group`	group. A character string specifying the name of a categorical variable containing grouping information.
`pvalue`	pvalue threshold for significant results from kruskal.test
`normalize`	to normalize the data before analysis(TRUE/FALSE)
`method`	A list of character strings specifying `method` to be used to normalize the phyloseq object Available methods are: "relative","TMM","vst","log2".

Author(s)

Kai Guo

Examples


data("Physeq")
res <- ldamarker(physeq,group="group")


data("Physeq")
res <- ldamarker(physeq,group="group")

light colors for making figures

Description

light colors for making figures

Usage

lightcolor
lightcolor

Format

An object of class character of length 56.

Author(s)

Kai Guo

Normalize the phyloseq object with different methods

Description

Normalize the phyloseq object with different methods

Usage

normalize(physeq, group, method = "relative", table = FALSE)
normalize(physeq, group, method = "relative", table = FALSE)

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`group`	group (DESeq2). A character string specifying the name of a categorical variable containing grouping information.
`method`	A list of character strings specifying `method` to be used to normalize the phyloseq object Available methods are: "relative","TMM","vst","log2".
`table`	return a data.frame or not

Value

phyloseq object with normalized data

Author(s)

Kai Guo

Examples

{
data("Physeq")
phy<-normalize(physeq)
}
{
data("Physeq")
phy<-normalize(physeq)
}

extract otu table

Description

extract otu table

Usage

otu_table(physeq, ...)
otu_table(physeq, ...)

Arguments

`physeq`	(Required). An integer matrix, otu_table-class, or phyloseq-class.
`...`	parameters for the otu_table function in phyloseq package

Retrieve phylogenetic tree (phylo-class) from object.

Description

Retrieve phylogenetic tree (phylo-class) from object.

Usage

phy_tree(physeq, ...)
phy_tree(physeq, ...)

Arguments

`physeq`	(Required). An instance of phyloseq-class that contains a phylogenetic tree. If physeq is a phylogenetic tree (a component data class), then it is returned as-is.
`...`	parameters for the phy_tree function in phyloseq package

plot alpha diversity

Description

plot alpha diversity

Usage

plotalpha(
  physeq,
  group,
  method = c("Observed", "Simpson", "Shannon"),
  color = NULL,
  geom = "boxplot",
  pvalue = 0.05,
  padj = NULL,
  sig.only = TRUE,
  wilcox = FALSE,
  show.number = FALSE
)
plotalpha(
  physeq,
  group,
  method = c("Observed", "Simpson", "Shannon"),
  color = NULL,
  geom = "boxplot",
  pvalue = 0.05,
  padj = NULL,
  sig.only = TRUE,
  wilcox = FALSE,
  show.number = FALSE
)

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`group`	group (Required). A character string specifying the name of a categorical variable containing grouping information.
`method`	A list of character strings specifying `method` to be used to calculate for alpha diversity in the data. Available methods are: "Observed","Chao1","ACE","Richness", "Fisher", "Simpson", "Shannon", "Evenness","InvSimpson".
`color`	A vector of character use specifying the color
`geom`	different geom to display("boxplot","violin","dotplot")
`pvalue`	pvalue threshold for significant dispersion results
`padj`	adjust p value threshold for significant dispersion results
`sig.only`	display the significant comparsion only(TRUE/ FALSE)
`wilcox`	use wilcoxon test or not
`show.number`	to show the pvalue instead of significant symbol(TRUE/FALSE)

Value

Returns a ggplot object. This can further be manipulated as preferred by user.

Author(s)

Kai Guo

Examples

{
data("Physeq")
plotalpha(physeq,group="SampleType")
}
{
data("Physeq")
plotalpha(physeq,group="SampleType")
}

plot bar for relative abundance for bacteria

Description

plot bar for relative abundance for bacteria

Usage

plotbar(
  physeq,
  level = "Phylum",
  color = NULL,
  group = NULL,
  top = 5,
  return = FALSE,
  fontsize.x = 5,
  fontsize.y = 12
)
plotbar(
  physeq,
  level = "Phylum",
  color = NULL,
  group = NULL,
  top = 5,
  return = FALSE,
  fontsize.x = 5,
  fontsize.y = 12
)

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`level`	the level to plot
`color`	A vector of character use specifying the color
`group`	group (Optional). A character string specifying the name of a categorical variable containing grouping information.
`top`	the number of most abundance bacteria to display
`return`	return the data with the relative abundance
`fontsize.x`	the size of x axis label
`fontsize.y`	the size of y axis label

Value

Returns a ggplot object. This can further be manipulated as preferred by user.

Author(s)

Kai Guo

Examples


data("Physeq")
phy<-normalize(physeq)
plotbar(phy,level="Phylum")

data("Physeq")
phy<-normalize(physeq)
plotbar(phy,level="Phylum")

plot beta diversity

Description

plot beta diversity

Usage

plotbeta(
  physeq,
  group,
  shape = NULL,
  distance = "bray",
  method = "PCoA",
  color = NULL,
  size = 3,
  ellipse = FALSE
)
plotbeta(
  physeq,
  group,
  shape = NULL,
  distance = "bray",
  method = "PCoA",
  color = NULL,
  size = 3,
  ellipse = FALSE
)

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`group`	(Required). Character string specifying name of a categorical variable that is preferred for grouping the information. information.
`shape`	shape(Optional) Character string specifying shape of a categorical variable
`distance`	A string character specifying dissimilarity index to be used in calculating pairwise distances (Default index is "bray".). "unifrac","wunifrac","manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup" , "binomial", "chao", "cao" or "mahalanobis".
`method`	A character string specifying ordination method. All methods available to the `ordinate` function of `phyloseq` are acceptable here as well.
`color`	user defined color for group
`size`	the point size
`ellipse`	draw ellipse or not

Value

ggplot2 object

Author(s)

Kai Guo

Examples

{
data("Physeq")
phy<-normalize(physeq)
plotbeta(phy,group="SampleType")
}
{
data("Physeq")
phy<-normalize(physeq)
plotbeta(phy,group="SampleType")
}

plot differential results

Description

plot differential results

Usage

plotdiff(
  res,
  level = "Genus",
  color = NULL,
  pvalue = 0.05,
  padj = NULL,
  log2FC = 0,
  size = 3,
  fontsize.x = 5,
  fontsize.y = 10,
  horiz = TRUE
)
plotdiff(
  res,
  level = "Genus",
  color = NULL,
  pvalue = 0.05,
  padj = NULL,
  log2FC = 0,
  size = 3,
  fontsize.x = 5,
  fontsize.y = 10,
  horiz = TRUE
)

Arguments

`res`	differential test results from diff_test
`level`	the level to plot
`color`	A vector of character use specifying the color
`pvalue`	pvalue threshold for significant results
`padj`	adjust p value threshold for significant results
`log2FC`	log2 Fold Change threshold
`size`	size for the point
`fontsize.x`	the size of x axis label
`fontsize.y`	the size of y axis label
`horiz`	horizontal or not (TRUE/FALSE)

Value

ggplot object

Author(s)

Kai Guo

Examples

 
data("Physeq")
res <- difftest(physeq,group="group")
plotdiff(res,level="Genus",padj=0.001)

data("Physeq")
res <- difftest(physeq,group="group")
plotdiff(res,level="Genus",padj=0.001)

plot LEfSe results from ldamarker function

Description

plot LEfSe results from ldamarker function

Usage

plotLDA(
  x,
  group,
  lda = 2,
  pvalue = 0.05,
  padj = NULL,
  color = NULL,
  fontsize.x = 4,
  fontsize.y = 5
)
plotLDA(
  x,
  group,
  lda = 2,
  pvalue = 0.05,
  padj = NULL,
  color = NULL,
  fontsize.x = 4,
  fontsize.y = 5
)

Arguments

`x`	LEfse results from ldamarker
`group`	a vector include two character to show the group comparsion
`lda`	LDA threshold for significant biomarker
`pvalue`	pvalue threshold for significant results
`padj`	adjust p value threshold for significant results
`color`	A vector of character use specifying the color
`fontsize.x`	the size of x axis label
`fontsize.y`	the size of y axis label

Value

ggplot2 object

Author(s)

Kai Guo

Examples


data("Physeq")
res <- ldamarker(physeq,group="group")
plotLDA(res,group=c("A","B"),lda=5,pvalue=0.05)

data("Physeq")
res <- ldamarker(physeq,group="group")
plotLDA(res,group=c("A","B"),lda=5,pvalue=0.05)

plot the biomarker from the biomarker function with randomForest

Description

plot the biomarker from the biomarker function with randomForest

Usage

plotmarker(
  x,
  level = "Genus",
  top = 30,
  rotate = FALSE,
  dot.size = 8,
  label.color = "black",
  label.size = 6
)
plotmarker(
  x,
  level = "Genus",
  top = 30,
  rotate = FALSE,
  dot.size = 8,
  label.color = "black",
  label.size = 6
)

Arguments

`x`	biomarker results from randomForest
`level`	the bacteria level to display
`top`	the number of important biomarker to draw
`rotate`	TRUE/FALSE
`dot.size`	size for the dot
`label.color`	label color
`label.size`	label size

Value

ggplot2 object

Author(s)

Kai Guo

Examples


data("Physeq")
res <- biomarker(physeq,group="group")
plotmarker(res,level="Genus")

data("Physeq")
res <- biomarker(physeq,group="group")
plotmarker(res,level="Genus")

plot the quality for the fastq file

Description

plot the quality for the fastq file

Usage

plotquality(file, n = 5e+05, aggregate = FALSE)
plotquality(file, n = 5e+05, aggregate = FALSE)

Arguments

`file`	(Required). character. File path(s) to fastq or fastq.gz file(s).
`n`	(Optional). Default 500,000. The number of records to sample from the fastq file.
`aggregate`	(Optional). Default FALSE. If TRUE, compute an aggregate quality profile for all fastq files provided.

Value

figure

Examples


plotquality(system.file("extdata", "sam1F.fastq.gz", package="dada2"))

plotquality(system.file("extdata", "sam1F.fastq.gz", package="dada2"))

filter the phyloseq

Description

filter the phyloseq

Usage

prefilter(physeq, min = 10, perc = 0.05)
prefilter(physeq, min = 10, perc = 0.05)

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`min`	Numeric, the threshold for mininal Phylum shown in samples
`perc`	Numeric, input the percentage of samples for which to filter low counts.

Value

filter phyloseq object

Author(s)

Kai Guo

Examples


data("Physeq")
physeqs<-prefilter(physeq)

data("Physeq")
physeqs<-prefilter(physeq)

Download the reference database

Description

Download the reference database

Usage

preRef(ref_db, path = ".")
preRef(ref_db, path = ".")

Arguments

`ref_db`	the reference database
`path`	path for the database

Value

the path of the database

Author(s)

Kai Guo

Examples


preRef(ref_db="silva",path=tempdir())

preRef(ref_db="silva",path=tempdir())

Perform dada2 analysis

Description

Perform dada2 analysis

Usage

processSeq(
  path = ".",
  truncLen = c(0, 0),
  trimLeft = 0,
  trimRight = 0,
  minLen = 20,
  maxLen = Inf,
  sample_info = NULL,
  train_data = "silva_nr99_v138_train_set.fa.gz",
  train_species = "silva_species_assignment_v138.fa.gz",
  outpath = NULL,
  saveobj = FALSE,
  buildtree = FALSE,
  verbose = TRUE
)
processSeq(
  path = ".",
  truncLen = c(0, 0),
  trimLeft = 0,
  trimRight = 0,
  minLen = 20,
  maxLen = Inf,
  sample_info = NULL,
  train_data = "silva_nr99_v138_train_set.fa.gz",
  train_species = "silva_species_assignment_v138.fa.gz",
  outpath = NULL,
  saveobj = FALSE,
  buildtree = FALSE,
  verbose = TRUE
)

Arguments

`path`	working dir for the input reads
`truncLen`	(Optional). Default 0 (no truncation). Truncate reads after truncLen bases. Reads shorter than this are discarded.
`trimLeft`	(Optional). The number of nucleotides to remove from the start of each read.
`trimRight`	(Optional). Default 0. The number of nucleotides to remove from the end of each read. If both truncLen and trimRight are provided, truncation will be performed after trimRight is enforced.
`minLen`	(Optional). Default 20. Remove reads with length less than minLen. minLen is enforced after trimming and truncation.
`maxLen`	Optional). Default Inf (no maximum). Remove reads with length greater than maxLen. maxLen is enforced before trimming and truncation.
`sample_info`	(Optional).sample information for the sequence
`train_data`	(Required).training database
`train_species`	(Required). species database
`outpath`	(Optional).the path for the filtered reads and th out table
`saveobj`	(Optional).Default FALSE. save the phyloseq object output.
`buildtree`	build phylogenetic tree or not(default: FALSE)
`verbose`	(Optional). Default TRUE. Print verbose text output.

Value

list include count table, summary table, taxonomy information and phyloseq object

Author(s)

Kai Guo

Melt phyloseq data object into large data.frame

Description

Melt phyloseq data object into large data.frame

Usage

psmelt(physeq, ...)
psmelt(physeq, ...)

Arguments

`physeq`	A sample_data-class, or a phyloseq-class object with a sample_data. If the sample_data slot is missing in physeq, then physeq will be returned as-is, and a warning will be printed to screen.
`...`	parameters for the subset_samples function in phyloseq package

calculat the richness for the phyloseq object

Description

calculat the richness for the phyloseq object

Usage

richness(physeq, method = c("Observed", "Simpson", "Shannon"))
richness(physeq, method = c("Observed", "Simpson", "Shannon"))

Arguments

`physeq`	A `phyloseq` object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
`method`	A list of character strings specifying `method` to be used to calculate for alpha diversity in the data. Available methods are: "Observed","Chao1","ACE","Richness", "Fisher", "Simpson", "Shannon", "Evenness","InvSimpson".

Value

data.frame of alpha diversity

Author(s)

Kai Guo

Examples

{
data("Physeq")
rich <-richness(physeq,method=c("Simpson", "Shannon"))
}
{
data("Physeq")
rich <-richness(physeq,method=c("Simpson", "Shannon"))
}

extract sample information

Description

extract sample information

Usage

sample_data(physeq, ...)
sample_data(physeq, ...)

Arguments

`physeq`	(Required). A data.frame-class, or a phyloseq-class object.
`...`	parameters for the sample_data function in phyloseq package

Subset the phyloseq based on sample

Description

Subset the phyloseq based on sample

Usage

subset_samples(physeq, ...)
subset_samples(physeq, ...)

Arguments

`physeq`	A sample_data-class, or a phyloseq-class object with a sample_data. If the sample_data slot is missing in physeq, then physeq will be returned as-is, and a warning will be printed to screen.
`...`	parameters for the subset_samples function in phyloseq package

Subset species by taxonomic expression

Description

Subset species by taxonomic expression

Usage

subset_taxa(physeq, ...)
subset_taxa(physeq, ...)

Arguments

`physeq`	A sample_data-class, or a phyloseq-class object with a sample_data. If the sample_data slot is missing in physeq, then physeq will be returned as-is, and a warning will be printed to screen.
`...`	parameters for the subset_taxa function in phyloseq package

extract taxonomy table

Description

extract taxonomy table

Usage

tax_table(physeq, ...)
tax_table(physeq, ...)

Arguments

`physeq`	An object among the set of classes defined by the phyloseq package that contain taxonomyTable.
`...`	parameters for the tax_table function in phyloseq package

Package 'microbial'

Help Index

check file format

Description

Usage

Arguments

replace p value with star

Description

Usage

Arguments

LEfse function

Description

Usage

Arguments

calcaute beta diversity

Description

Usage

Arguments

Value

Author(s)

Examples

PERMANOVA test for phyloseq

Description

Usage

Arguments

Value

Author(s)

Examples

Identify biomarker by using randomForest method

Description

Usage

Arguments

Value

Author(s)

Examples

contruction of plylogenetic tree (extreme slow)

Description

Usage

Arguments

Value

Author(s)

The physeq data was modified from the (Data) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample (2011)

Description

References

Examples

The physeq data was modified from the (Data) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample (2011)

Description

References

Examples

Calculate differential bacteria with DESeq2

Description

Usage

Arguments

Value

Author(s)

Examples

distinguish colors for making figures

Description

Usage

Format

Author(s)

do anova test and return results as data.frame

Description

Usage

Arguments

Author(s)

Examples

do t.test

Description

Usage

Arguments

Author(s)

Examples

do wilcox test

Description

Usage

Arguments

Author(s)

Examples

Do the generalized linear model regression