Package 'PathoStat'

Title: PathoStat Statistical Microbiome Analysis Package
Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis.
Authors: Solaiappan Manimaran <[email protected]>, Matthew Bendall <[email protected]>, Sandro Valenzuela Diaz <[email protected]>, Eduardo Castro <[email protected]>, Tyler Faits <[email protected]>, Yue Zhao <[email protected]>, Anthony Nicholas Federico <[email protected]>, W. Evan Johnson <[email protected]>
Maintainer: Solaiappan Manimaran <[email protected]>, Yue Zhao <[email protected]>
License: GPL (>= 2)
Version: 1.33.0
Built: 2024-10-30 09:21:39 UTC
Source: https://github.com/bioc/PathoStat

Help Index

Do bootstrap and LOOCV

Description

Do bootstrap and LOOCV

Usage

Bootstrap_LOOCV_LR_AUC(df, targetVec, nboot = 50)

Arguments

df

Row is sample, column is feature. Required
targetVec

y vector. Required
nboot

number of BOOTSTRAP

Value

bootstrap loocv result dataframe

Examples

data('iris')
Bootstrap_LOOCV_LR_AUC(iris[,1:4],
c(rep(1,100), rep(0,50)), nboot = 3)

Given PAM and disease/control annotation, do Chi-square test for each row of PAM

Description

Given PAM and disease/control annotation, do Chi-square test for each row of PAM

Usage

Chisq_Test_Pam(pam, label.vec.num, pvalue.cutoff = 0.05)

Arguments

pam

Input data object that contains the data to be tested. Required
label.vec.num

The target binary condition. Required
pvalue.cutoff

choose p-value cut-off

Value

df.output object

Examples

tmp <- matrix(rbinom(12,1,0.5), nrow = 3)
rownames(tmp) <- c("a", "b", "c")
Chisq_Test_Pam(tmp, c(1,1,0,0))

Return the Relative Abundance (RA) data for the given count OTU table

Description

Return the Relative Abundance (RA) data for the given count OTU table

Usage

findRAfromCount(count_otu)

Arguments

count_otu

Count OTU table

Value

ra_otu Relative Abundance (RA) OTU table

Examples

data_dir <- system.file("data", package = "PathoStat")
infileName <- "pstat_data.rda"
pstat_test <- loadPstat(data_dir, infileName)
ra_otu <- findRAfromCount(phyloseq::otu_table(pstat_test))

Find the Taxonomy Information Matrix

Description

Find the Taxonomy Information Matrix

Usage

findTaxonMat(names, taxonLevels)

Arguments

names

Row names of the taxonomy matrix
taxonLevels

Taxon Levels of all tids

Value

taxmat Taxonomy Information Matrix

Examples

example_data_dir <- system.file("example/data", package = "PathoStat")
pathoreport_file_suffix <- "-sam-report.tsv"
datlist <- readPathoscopeData(example_data_dir, pathoreport_file_suffix,
input.files.name.vec = as.character(1:6))
dat <- datlist$data
ids <- rownames(dat)
tids <- unlist(lapply(ids, FUN = grepTid))
# taxonLevels <- findTaxonomy(tids[1:5])
# taxmat <- findTaxonMat(ids[1:5], taxonLevels)

Find the taxonomy for unlimited tids

Description

Find the taxonomy for unlimited tids

Usage

findTaxonomy(tids)

Arguments

tids

Given taxonomy ids

Value

taxondata Data with the taxonomy information

Examples

example_data_dir <- system.file("example/data", package = "PathoStat")
pathoreport_file_suffix <- "-sam-report.tsv"
datlist <- readPathoscopeData(example_data_dir, pathoreport_file_suffix,
input.files.name.vec = as.character(1:6))
dat <- datlist$data
ids <- rownames(dat)
tids <- unlist(lapply(ids, FUN = grepTid))
# taxonLevels <- findTaxonomy(tids[1:5])

Find the taxonomy for maximum 300 tids

Description

Find the taxonomy for maximum 300 tids

Usage

findTaxonomy300(tids)

Arguments

tids

Given taxonomy ids

Value

taxondata Data with the taxonomy information

Examples

example_data_dir <- system.file("example/data", package = "PathoStat")
pathoreport_file_suffix <- "-sam-report.tsv"
datlist <- readPathoscopeData(example_data_dir, 
pathoreport_file_suffix, input.files.name.vec = as.character(1:6))
dat <- datlist$data
ids <- rownames(dat)
tids <- unlist(lapply(ids, FUN = grepTid))
# taxonLevels <- findTaxonomy300(tids[1:5])

Given PAM and disease/control annotation, do Chi-square test for each row of PAM

Description

Given PAM and disease/control annotation, do Chi-square test for each row of PAM

Usage

Fisher_Test_Pam(pam, label.vec.num, pvalue.cutoff = 0.05)

Arguments

pam

Input data object that contains the data to be tested. Required
label.vec.num

The target binary condition. Required
pvalue.cutoff

choose p-value cut-off

Value

df.output object

Examples

tmp <- matrix(rbinom(12,1,0.5), nrow = 3)
rownames(tmp) <- c("a", "b", "c")
Fisher_Test_Pam(tmp, c(1,1,0,0))

Format taxonomy table for rendering

Description

Format taxonomy table for rendering

Usage

formatTaxTable(ttable)

Arguments

ttable

Taxonomy table

Value

Formatted table suitable for rendering with. DT::renderDataTable

transform cpm counts to presence-absence matrix

Description

transform cpm counts to presence-absence matrix

Usage

GET_PAM(df)

Arguments

df

Input data object that contains the data to be tested. Required

Value

df.output object

Examples

GET_PAM(data.frame(a = c(1,3,0), b = c(0,0.1,2)))

Getter function to get the shinyInput option

Description

Getter function to get the shinyInput option

Usage

getShinyInput()

Value

shinyInput option

Examples

getShinyInput()

Getter function to get the shinyInputCombat option

Description

Getter function to get the shinyInputCombat option

Usage

getShinyInputCombat()

Value

shinyInputCombat option

Examples

getShinyInputCombat()

Getter function to get the shinyInputOrig option

Description

Getter function to get the shinyInputOrig option

Usage

getShinyInputOrig()

Value

shinyInputOrig option

Examples

getShinyInputOrig()

Use Lasso to do feature selection

Description

Use Lasso to do feature selection

Usage

getSignatureFromMultipleGlmnet(df.input, target.vec, nfolds = 10,
  logisticRegression = TRUE, nRun = 100, alpha = 1)

Arguments

df.input

Row is sample, column is feature. Required
target.vec

y vector. Required
nfolds

glmnet CV nfolds
logisticRegression

doing logistic regression or linear regression.
nRun

number of glmnet runs
alpha

same as in glmnet

Value

signature

Examples

data('iris')
getSignatureFromMultipleGlmnet(iris[,1:4],
c(rep(1,100), rep(0,50)), nfolds = 3, nRun = 10)

Greps the tid from the given identifier string

Description

Greps the tid from the given identifier string

Usage

grepTid(id)

Arguments

id

Given identifier string

Value

tid string

Examples

grepTid("ti|700015|org|Coriobacterium_glomerans_PW2")

Loads all data from a set of PathoID reports. For each column in the PathoID report, construct a matrix where the rows are genomes and the columns are samples. Returns a list where each element is named according to the PathoID column. For example, ret[["Final.Best.Hit.Read.Numbers"]] on the result of this function will get you the final count matrix. Also includes elements "total_reads" and "total_genomes" from the first line of the PathoID report.

Description

Loads all data from a set of PathoID reports. For each column in the PathoID report, construct a matrix where the rows are genomes and the columns are samples. Returns a list where each element is named according to the PathoID column. For example, ret[["Final.Best.Hit.Read.Numbers"]] on the result of this function will get you the final count matrix. Also includes elements "total_reads" and "total_genomes" from the first line of the PathoID report.

Usage

loadPathoscopeReports(reportfiles, nrows = NULL)

Arguments

reportfiles

Paths to report files
nrows

Option to read first N rows of PathoScope reports

Value

Returns a list where each element is named according to the PathoID column. For example, ret[["Final.Best.Hit.Read.Numbers"]] on the result of this function will get you the final count matrix. Also includes elements "total_reads" and "total_genomes" from the first line of the PathoID report.

Examples

input_dir <- system.file("example/data", package = "PathoStat")
reportfiles <- list.files(input_dir, pattern = "*-sam-report.tsv",
full.names = TRUE)

Load the R data(.rda) file with pathostat object

Description

Load the R data(.rda) file with pathostat object

Usage

loadPstat(indir = ".", infileName = "pstat_data.rda")

Arguments

indir

Input Directory of the .rda file
infileName

File name of the .rda file

Value

pstat pathostat object (NULL if it does not exist)

Examples

data_dir <- system.file("data", package = "PathoStat")
infileName <- "pstat_data.rda"
pstat <- loadPstat(data_dir, infileName)

Compute log2(counts per mil reads) and library size for each sample

Description

Compute log2(counts per mil reads) and library size for each sample

Usage

log2CPM(qcounts, lib.size = NULL)

Arguments

qcounts

quantile normalized counts
lib.size

default is colsums(qcounts)

Value

list containing log2(quantile counts per mil reads) and library sizes

Examples

log2CPM(matrix(1:12, nrow = 3))

LOOCV

Description

LOOCV

Usage

LOOAUC_simple_multiple_noplot_one_df(df, targetVec)

Arguments

df

Row is sample, column is feature. Required
targetVec

y vector. Required

Value

mean auc

Examples

data('iris')
LOOAUC_simple_multiple_noplot_one_df(iris[,1:4],
c(rep(1,100), rep(0,50)))

LOOCV with ROC curve

Description

LOOCV with ROC curve

Usage

LOOAUC_simple_multiple_one_df(df, targetVec)

Arguments

df

Row is sample, column is feature. Required
targetVec

y vector. Required

Value

the ROC

Examples

data('iris')
LOOAUC_simple_multiple_one_df(iris[,1:4],
c(rep(1,100), rep(0,50)))

PathoStat class to store PathoStat input data including phyloseq object

Description

Contains all currently-supported BatchQC output data classes:

Details

slots:

average_count

a single object of class otu_tableOrNULL

besthit_count

a single object of class otu_tableOrNULL

highconf_count

a single object of class otu_tableOrNULL

lowconf_count

a single object of class otu_tableOrNULL

Examples

otumat = matrix(sample(1:100, 100, replace = TRUE), nrow = 10, ncol = 10)
rownames(otumat) <- paste0("OTU", 1:nrow(otumat))
colnames(otumat) <- paste0("Sample", 1:ncol(otumat))
taxmat = matrix(sample(letters, 70, replace = TRUE), 
nrow = nrow(otumat), ncol = 7)
rownames(taxmat) <- rownames(otumat)
colnames(taxmat) <- c("Domain", "Phylum", "Class", 
"Order", "Family", "Genus", "Species")
OTU = phyloseq::otu_table(otumat, taxa_are_rows = TRUE)
TAX = phyloseq::tax_table(taxmat)
physeq = phyloseq::phyloseq(OTU, TAX)
pathostat1(physeq)

Compute percentage

Description

Compute percentage

Usage

percent(x, digits = 2, format = "f")

Arguments

x

a number or a vector
digits

how many digit of percentage
format

numeric format, "f" for float

Value

the percentage

Examples

pecent.vec <- percent(c(0.9, 0.98))

Convert phyloseq OTU count data into DGEList for edgeR package

Description

Further details.

Usage

phyloseq_to_edgeR(physeq, group, method = "RLE", ...)

Arguments

physeq

(Required).
group

(Required). A character vector or factor giving the experimental group/condition for each sample/library.
method

(Optional).
...

Additional arguments passed on to

Value

dispersion

Examples

data_dir_test <- system.file("data", package = "PathoStat")
pstat_test <- loadPstat(indir=data_dir_test,
infileName="pstat_data_2_L1.rda")
phyloseq_to_edgeR(pstat_test, group="Sex")

Plot PCA

Description

Plot PCA

Usage

plotPCAPlotly(df.input, condition.color.vec,
  condition.color.name = "condition", condition.shape.vec = NULL,
  condition.shape.name = "condition", columnTitle = "Title",
  pc.a = "PC1", pc.b = "PC2")

Arguments

df.input

Input data object that contains the data to be plotted. Required
condition.color.vec

color vector. Required
condition.color.name

color variable name. Required
condition.shape.vec

shape vector. Required
condition.shape.name

shape variable name. Required
columnTitle

Title to be displayed at top of heatmap.
pc.a

pc.1
pc.b

pc.2

Value

the plot

Examples

data('iris')
plotPCAPlotly(t(iris[,1:4]),
condition.color.vec = c(rep(1,100), rep(0,50)),
condition.shape.vec = c(rep(0,100), rep(1,50)))

Plot PCoA

Description

Plot PCoA

Usage

plotPCoAPlotly(physeq.input, condition.color.vec,
  condition.color.name = "condition", condition.shape.vec = NULL,
  condition.shape.name = "condition", method = "bray",
  columnTitle = "Title", pc.a = "Axis.1", pc.b = "Axis.2")

Arguments

physeq.input

Input data object that contains the data to be plotted. Required
condition.color.vec

color vector. Required
condition.color.name

color variable name. Required
condition.shape.vec

shape vector. Required
condition.shape.name

shape variable name. Required
method

which distance metric
columnTitle

Title to be displayed at top of heatmap.
pc.a

pc.1
pc.b

pc.2

Value

the plot

Examples

data_dir_test <- system.file("data", package = "PathoStat")
pstat_test <- loadPstat(indir=data_dir_test,
infileName="pstat_data_2_L1.rda")
plotPCoAPlotly(pstat_test, condition.color.vec = rbinom(33,1,0.5),
condition.shape.vec = rbinom(33,1,0.5))

pathostat object generated from example pathoscope report files

Description

This example data consists of 33 samples from a diet study with 11 subjects taking 3 different diets in random order

Usage

pstat

Format

pathostat object extension of phyloseq-class experiment-level object:

otu_table

OTU table with 41 taxa and 33 samples

sample_data

Sample Data with 33 samples by 18 sample variables

tax_table

Taxonomy Table with 41 taxa by 9 taxonomic ranks

sample_data

Phylogenetic Tree with 41 tips and 40 internal nodes

Value

pathostat object

Reads the data from PathoScope reports and returns a list of final guess relative abundance and count data

Description

Reads the data from PathoScope reports and returns a list of final guess relative abundance and count data

Usage

readPathoscopeData(input_dir = ".",
  pathoreport_file_suffix = "-sam-report.tsv", use.input.files = FALSE,
  input.files.path.vec = NULL, input.files.name.vec = NULL)

Arguments

input_dir

Directory where the tsv files from PathoScope are located
pathoreport_file_suffix

PathoScope report files suffix
use.input.files

whether input dir to pathoscope files or directly pathoscope files
input.files.path.vec

vector of pathoscope file paths
input.files.name.vec

vector of pathoscope file names

Value

List of final guess relative abundance and count data

Examples

example_data_dir <- system.file("example/data", package = "PathoStat")
pathoreport_file_suffix <- "-sam-report.tsv"
datlist <- readPathoscopeData(example_data_dir, pathoreport_file_suffix,
input.files.name.vec = as.character(1:6))

Statistical Microbiome Analysis on the pathostat input and generates a html report and produces interactive shiny app plots

Description

Statistical Microbiome Analysis on the pathostat input and generates a html report and produces interactive shiny app plots

Usage

runPathoStat(pstat = NULL, report_dir = ".",
  report_option_binary = "111111111", interactive = TRUE)

Arguments

pstat

phyloseq extension pathostat object
report_dir

Output report directory path
report_option_binary

9 bits Binary String representing the plots to display and hide in the report
interactive

when TRUE, opens the interactive shinyApp

Value

outputfile The output file with all the statistical plots

Examples

runPathoStat(interactive = FALSE)

Save the pathostat object to R data(.rda) file

Description

Save the pathostat object to R data(.rda) file

Usage

savePstat(pstat, outdir = ".", outfileName = "pstat_data.rda")

Arguments

pstat

pathostat object
outdir

Output Directory of the .rda file
outfileName

File name of the .rda file

Value

outfile .rda file

Examples

data_dir_test <- system.file("data", package = "PathoStat")
pstat_test <- loadPstat(indir=data_dir_test,
infileName="pstat_data_2_L1.rda")
outfile <- savePstat(pstat_test)

Setter function to set the shinyInput option

Description

Setter function to set the shinyInput option

Usage

setShinyInput(x)

Arguments

x

shinyInput option

Value

shinyInput option

Examples

setShinyInput(NULL)

Setter function to set the shinyInputCombat option

Description

Setter function to set the shinyInputCombat option

Usage

setShinyInputCombat(x)

Arguments

x

shinyInputCombat option

Value

shinyInputCombat option

Examples

setShinyInputCombat(NULL)

Setter function to set the shinyInputOrig option

Description

Setter function to set the shinyInputOrig option

Usage

setShinyInputOrig(x)

Arguments

x

shinyInputOrig option

Value

shinyInputOrig option

Examples

setShinyInputOrig(NULL)

Summarize sample

Description

Creates a table of summary metrics

Usage

summarizeTable(pstat)

Arguments

pstat

Input pstat

Value

A data.frame object of summary metrics.

Examples

data_dir_test <- system.file("data", package = "PathoStat")
pstat_test <- loadPstat(indir=data_dir_test,
infileName="pstat_data_2_L1.rda")
st.tmp <- summarizeTable(pstat_test)

Find the taxonomy for the given taxon id name

Description

Find the taxonomy for the given taxon id name

Usage

TranslateIdToTaxLevel(pstat, input.id.vec, tax.level)

Arguments

pstat

pathostat object
input.id.vec

names containing id
tax.level

target taxon level

Value

target taxon level names

Examples

data_dir_test <- system.file("data", package = "PathoStat")
pstat_test <- loadPstat(indir=data_dir_test,
infileName="pstat_data_2_L1.rda")
names.new <- TranslateIdToTaxLevel(pstat_test,
c("ti|862962|org|Bacteroides_fragilis_638R",
"ti|697329|org|Ruminococcus_albus_7" ),
"genus")

Mann-whitney test for a dataframe

Description

Mann-whitney test for a dataframe

Usage

Wilcox_Test_df(df, label.vec.num, pvalue.cutoff = 0.05)

Arguments

df

Input data object that contains the data to be tested. Required
label.vec.num

The target binary condition. Required
pvalue.cutoff

choose p-value cut-off

Value

df.output object

Examples

data('iris')
Wilcox_Test_df(t(iris[,1:4]),
c(rep(1,100), rep(0,50)))