Help for package microbial

Type:

Package

Title:

Do 16s Data Analysis and Generate Figures

Version:

0.0.21

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.

License:

GPL-3

Depends:

R (≥ 3.5.0)

Imports:

dplyr, plyr, magrittr, broom, phyloseq, vegan, rlang, ggplot2, ggpubr, DESeq2, SummarizedExperiment, S4Vectors, rstatix, tidyr, phangorn, randomForest, edgeR

Encoding:

UTF-8

LazyData:

true

Suggests:

markdown,dada2,rmarkdown,knitr,tools,Biostrings, DECIPHER, MASS,testthat

VignetteBuilder:

knitr

biocViews:

Software,GraphAndNetwork

RoxygenNote:

7.2.3

NeedsCompilation:

Packaged:

2024-05-15 17:44:20 UTC; bioguo

Author:

Kai Guo [aut, cre], Pan Gao [aut]

Maintainer:

Kai Guo <guokai8@gmail.com>

Repository:

CRAN

Date/Publication:

2024-05-15 18:20:02 UTC

check file format

Description

check file format

Usage

.checkfile(file)

Arguments

file

filename

replace p value with star

Description

replace p value with star

Usage

.getstar(x)

Arguments

x

a (non-empty) numeric data values

LEfse function

Description

LEfse function

Usage

.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")

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)
}

PERMANOVA test for phyloseq

Description

PERMANOVA test for phyloseq

Usage

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

Arguments

physeq

group

(Required). Character string specifying name of a categorical variable that is preferred for grouping the information. information.

distance

Value

PERMANOVA test result

Author(s)

Kai Guo

Examples

{
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"
)

Arguments

physeq

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")

contruction of plylogenetic tree (extreme slow)

Description

contruction of plylogenetic tree (extreme slow)

Usage

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)

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
)

Arguments

physeq

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")

distinguish colors for making figures

Description

distinguish colors for making figures

Usage

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, ...)

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")
}

do t.test

Description

do t.test

Usage

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")
}

do wilcox test

Description

do wilcox test

Usage

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")
}

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")
)

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")

Identify biomarker by using LEfSe method

Description

Identify biomarker by using LEfSe method

Usage

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

Arguments

physeq

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")

light colors for making figures

Description

light colors for making figures

Usage

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)

Arguments

physeq

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)
}

extract otu table

Description

extract otu table

Usage

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, ...)

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 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
)

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)

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
)

Arguments

physeq

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")
}

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
)

Arguments

physeq

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")

plot beta diversity

Description

plot beta diversity

Usage

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

Arguments

physeq

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

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")
}

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
)

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)

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
)

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")

plot the quality for the fastq file

Description

plot the quality for the fastq file

Usage

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"))

Download the reference database

Description

Download the reference database

Usage

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())

filter the phyloseq

Description

filter the phyloseq

Usage

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

Arguments

physeq

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)

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
)

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, ...)

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"))

Arguments

physeq

method

Value

data.frame of alpha diversity

Author(s)

Kai Guo

Examples

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

extract sample information

Description

extract sample information

Usage

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, ...)

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, ...)

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, ...)

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