Package 'omicsViewer'

Title: Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer
Description: omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript.
Authors: Chen Meng [aut, cre]
Maintainer: Chen Meng <[email protected]>
License: GPL-2
Version: 1.11.0
Built: 2024-10-30 09:09:18 UTC
Source: https://github.com/bioc/omicsViewer

Help Index

convert e (inflection point) to EC50

Description

convert e (inflection point) to EC50

Usage

.e2EC50(b, d, e, f)

Arguments

b

Hill's slope. The Hill's slope refers to the steepness of the curve. It could either be positive or negative.
d

Hihgest response value.
e

Inflection point. The inflection point is defined as the point on the curve where the curvature changes direction or signs. In models where f = 1 (2-4 parameter models), e is EC50.
f

Asymmetry factor. When f=1 we have a symmetrical curve around inflection point and so we have a four-parameters logistic equation.

Note

Only has an effect when using LL.5 and LL2.5 model

model fitted by drc

Description

model fitted by drc

Usage

.modelFormula(x, b, c = 0, d = 1, e, f = 1)

Arguments

x

numerical vector of doses/time points/concentrations
b

Hill's slope. The Hill's slope refers to the steepness of the curve. It could either be positive or negative.
c

Lowest response value.
d

Hihgest response value.
e

Inflection point. The inflection point is defined as the point on the curve where the curvature changes direction or signs. In models where f = 1 (2-4 parameter models), e is EC50.
f

Asymmetry factor. When f=1 we have a symmetrical curve around inflection point and so we have a four-parameters logistic equation.

Details

func(x) = c + (d - c) / (1 + (x/e)^b)^f

Application level 0 module

Description

Function should only be used for the developers

Usage

app_module(
  input,
  output,
  session,
  .dir,
  filePattern = ".(RDS|db|sqlite|sqlite3)$",
  additionalTabs = NULL,
  ESVObj = reactive(NULL),
  esetLoader = readESVObj,
  exprsGetter = getExprs,
  pDataGetter = getPData,
  fDataGetter = getFData,
  imputeGetter = getExprsImpute,
  defaultAxisGetter = getAx,
  appName = "omicsViewer",
  appVersion = packageVersion("omicsViewer")
)

Arguments

input

input
output

output
session

session
.dir

reactive; directory containing the .RDS file of ExpressionSet or SummarizedExperiment
filePattern

file pattern to be displayed.
additionalTabs

additional tabs added to "Analyst" panel
ESVObj

the ESV object given, the drop down list should be disable in the "ui" component.
esetLoader

function to load the eset object, if an RDS file, should be "readRDS"
exprsGetter

function to get the expression matrix from eset
pDataGetter

function to get the phenotype data from eset
fDataGetter

function to get the feature data from eset
imputeGetter

function to get the imputed expression matrix from eset, only used when exporting imputed data to excel
defaultAxisGetter

function to get the default axes to be visualized. It should be a function with two arguments: x - the object loaded to the viewer; what - one of "sx", "sy", "fx" and "fy", representing the sample space x-axis, sample space y-axis, feature space x-axis and feature space y-axis respectively.
appName

name of the application
appVersion

version of the application

Value

do not return any values

Examples

if (interactive()) {
  dir <- system.file("extdata", package = "omicsViewer")
  server <- function(input, output, session) {
    callModule(app_module, id = "app", dir = reactive(dir))
  }
  ui <- fluidPage(
    app_ui("app")
  )
  shinyApp(ui = ui, server = server)
}

Application level 0 UI

Description

Function should only be used for the developers

Usage

app_ui(id, showDropList = TRUE, activeTab = "Feature")

Arguments

id

id
showDropList

logical; whether to show the dropdown list to select RDS file, if the ESVObj is given, this should be set to "FALSE"
activeTab

one of "Feature", "Feature table", "Sample", "Sample table", "Heatmap"

Value

a list of UI components

Examples

if (interactive()) {
  dir <- system.file("extdata", package = "omicsViewer")
  server <- function(input, output, session) {
    callModule(app_module, id = "app", dir = reactive(dir))
  }
  ui <- fluidPage(
    app_ui("app")
  )
  shinyApp(ui = ui, server = server)
}

Convert SummarizedExperiment to ExpressionSet retaining all attributes

Description

Convert SummarizedExperiment to ExpressionSet retaining all attributes

Usage

asEsetWithAttr(x)

Arguments

x

an object of class SummarizedExperiment

Value

an object of class ExpressionSet

Correlating a expression matrix with phenotypical variables

Description

This is a convenience function to perform correlation analysis, the output is in a format ready to be incorporated into object to be visualized by omicsViewer.

Usage

correlationAnalysis(x, pheno, min.value = 12, prefix = "Cor")

Arguments

x

an expression matrix, rows are the features (e.g. proteins), columns are the samples
pheno

a data.frame storing the numerical phenotypical variable to be correlated with the rows (features) in expression matrix.
min.value

the minimum number of samples required in the correlation analysis, if lower than this number, NA will be returned.
prefix

prefix of the names. Usually don't need to be changed by the user. When changes are needed, the prefix should be in a format like [analysis name]|[subset] so the "analysis name" and "subset" can be selected in the omicsViewer.

Value

Every correlation analysis returns a data.frame with five columns: R - pearson correlation coefficient N - number of values used in the analysis P - p-values returned by pearson correlation analysis logP - log transformed p-values range - the range of values in expression matrix used in the analysis

Examples

e1 <- matrix(rnorm(500), 50, 10)
rownames(e1) <- paste0("FT", 1:50)
p1 <- matrix(rnorm(50), 10, 5)
colnames(p1) <- paste0("PH", 1:5)
colnames(e1) <- rownames(p1) <- paste0("S", 1:10)
correlationAnalysis(x = e1, pheno = p1, min.value = 8)

convert a column compressed sparse matrix to a list

Description

convert a column compressed sparse matrix to a list

Usage

csc2list(x)

Arguments

x

a matrix or CsparseMatrix object

Value

a sparse frame in data.frame

Drawing ROC and PR curve

Description

Drawing ROC and PR curve

Usage

draw_roc_pr(value, label)

Arguments

value

a numerical vector indicates the predictions
label

true class labels, could be two or more unique values

Examples

v <- sort(rnorm(100))
l <- sample(1:2, size = 100, replace = TRUE)
draw_roc_pr(v, l)
l <- rep(c("b", "c", "a", "d"), each = 25)
draw_roc_pr(v, l)
draw_roc_pr(v, sample(l))

Fitting dose-response models for omics data matrix

Description

A convenient function to fit dose response models for every row in an omics matrix using drm function in the drc package.

Usage

drmMat(
  x,
  fitvar,
  fitvar.name = c("Dose", "Time", "Concentration")[1],
  curveid = NA,
  fct.name = c("LL.4()", "LL.3()", "LL.2()", "LL.5()")[1]
)

Arguments

x

a numerical matrix where the rows are features and columns are samples.
fitvar

a numerical variable has the same length as ncol(x) to indicate the dose/time/concentration conditions.
fitvar.name

the name of the fitvar, a length one character. Will be used as the label for x-axis when drawing the dose curve.
curveid

a numeric vector or factor containing the grouping of the columns in x.
fct.name

the function name, e.g. "LL.4()", "LL.3()", "LL.2()" and "LL.5()", which are defined in the drc package.

Value

a list of drc object

Perform PCA and prepare results for omicsViewer

Description

This is a convenience function to perform PCA on expression matrix, the output of PCA will be in a format ready to be incorporated into object to be visualized by omicsViewer.

Usage

exprspca(x, n = min(8, ncol(x) - 1), prefix = "PCA|All", fillNA = FALSE, ...)

Arguments

x

an expression matrix, where rows are features and samples are on columns.
n

number of components to keep
prefix

prefix of the names. Usually don't need to be changed by the user. When changes are needed, the prefix should be in a format like [analysis name]|[subset] so the "analysis name" and "subset" can be selected in the omicsViewer.
fillNA

logical; whether NA should be filled? If FALSE (default), na.omit will be called before PCA. If TRUE, the missing value will be replaced using fillNA.
...

other parameters passed to prcomp

Value

a data.frame storing the PCA results

Examples

# reading expression
packdir <- system.file("extdata", package = "omicsViewer")
expr <- read.delim(file.path(packdir, "expressionMatrix.tsv"), stringsAsFactors = FALSE)
# call PCA
pc <- exprspca(expr)
head(pc$samples)
head(pc$features)

Add extra columns to the phenoData/colData or featureData/rowData in ExpressionSet/SummarizedExperiment

Description

Add extra columns to the phenoData/colData or featureData/rowData in ExpressionSet/SummarizedExperiment

Add extra columns to the phenoData/colData or featureData/rowData in ExpressionSet/SummarizedExperiment

Add extra columns to the phenoData/colData or featureData/rowData in ExpressionSet/SummarizedExperiment

Usage

extendMetaData(object, newData, where)

## S4 method for signature 'ExpressionSet,data.frame'
extendMetaData(
  object,
  newData,
  where = c("pData", "fData", "colData", "rowData")[1]
)

## S4 method for signature 'SummarizedExperiment,data.frame'
extendMetaData(
  object,
  newData,
  where = c("pData", "fData", "colData", "rowData")[1]
)

## S4 method for signature 'SummarizedExperiment,DFrame'
extendMetaData(
  object,
  newData,
  where = c("pData", "fData", "colData", "rowData")[1]
)

Arguments

object

an object of ExpressionSet-class
newData

a data.frame containing the data to be added
where

where to add the extra columns, should be one of "pData", "fData", "rowData" and "colData".

Value

an object of ExpressionSet-class

Note

The attributes in the pheno data and feature data will be preserved

Examples

est <- Biobase::ExpressionSet(assayData=matrix(runif(1000), nrow=100, ncol=10))
Biobase::pData(est)
est <- extendMetaData(est, data.frame(letter = letters[1:10]), where = "pData")
Biobase::pData(est)

Extracting parameters from drc models

Description

Extracting parameters from drc models

Usage

extractParamDC(mod, prefix = "ResponseCurve")

Arguments

mod

a drc object
prefix

for column header, the column will be named as prefix|curveid|curveparameter

Note

when LL2.X is used, e is estimated as log(e), this function will return e in linear scale instead.

Extracting parameter from a list of drc object

Description

Extracting parameter from a list of drc object and return a data.frame, which can be incorporated into the object visualized by omicsViewer

Usage

extractParamDCList(x, prefix = "ResponseCurve")

Arguments

x

a list of drc object
prefix

for column header

Value

a data.frame

Wrapper of fgseaMultilevel function to take binary gene set matrix as input

Description

Wrapper of fgseaMultilevel function to take binary gene set matrix as input

Usage

fgsea1(gs, stats, gs_desc = NULL, ...)

Arguments

gs

either a data.frame or a (sparse) matrix input. If a data.frame object is given, it should have at least three columns named as "featureId", "gsId" and "weight". If a matrix is given, the matrix is binary matrix where rows are features and columns are gene sets. The values in the matrix should be either 1 or 0 representing the presence and absence of a feature in the genesets, repectively.
stats

ranking stats
gs_desc

description of gene sets, it should be a named vector and the names should be the same as colnames(gs)
...

other parameters passed to fgseaMultilevel

Value

a data.frame of fgsea results

Examples

## not for users
# library(fgsea)
# library(Biobase)
# dat <- readRDS(system.file(package = "omicsViewer", "extdata/demo.RDS"))
# fd <- fData(dat)
# fdgs <- fd[, grep("^GS\|", colnames(fd))]
# res <- fgsea1(fdgs, stats = fd$`t-test|OV_BR|md`, minSize = 5, maxSize = 500)
# res <- fgsea1(
#   fdgs, stats = fd$`t-test|OV_BR|md`,  
#   minSize = 5, maxSize = 500, gs_desc = colnames(fdgs))

Filling NAs in a matrix using constants calculated from user the defined function

Description

This function is usually use to impute missing values in expression matrix, where the rows are feature and columns are samples. This function impute the missing values on the row-wise, that is, every row will be imputed using different constant.

Usage

fillNA(
  x,
  maxfill = quantile(x, probs = 0.15, na.rm = TRUE),
  fillingFun = function(x) min(x, na.rm = TRUE) - log10(2)
)

Arguments

x

a matrix with NA values
maxfill

the maximum filled value, if the value calculated by fillingFun is greater than maxfill, then maxfill will the used to replace NAs.
fillingFun

function to calculate the filling values. It should be a function accept at least one argument "x", which is a row of input expression matrix. The default is function(x) min(x, na.rm = TRUE) - log10(2) corresponds to the "half of lowest detected values" if the expression matrix is log10 transformed. More examples:#' function(x) min(x, na.rm = TRUE) - 1 # half of lowest detected value when expression matrix is in log2 scale function(x) 0 # replace NA by 0

Value

a matrix without NAs

Note

The returned matrix may have -Inf, which may need to be filtered/replaced additionally

Examples

m <- matrix(rnorm(200), 20, 10)
m[sample(1:200, size = 20)] <- NA
mf <- fillNA(m)

Filter out rows of expression matrix

Description

The function is used to filter rows with values of low intensities or do not reproducible presented in replicates.

Usage

filterRow(x, max.quantile = NULL, max.value = NULL, var = NULL, min.rep = 2)

Arguments

x

an expression matrix
max.quantile

a single numerical value between (0, 1), if the row maximum is smaller than this quantile (calculated from the whole matrix), the row will be removed.
max.value

a single numerical value, if the the maximum value of a rwo is smaller than this value, the row will be removed. Only used if max.quantile is set to "NULL".
var

variables has the same length as the column number in x to indicate which sample is from which group
min.rep

the minimum number of replicate in at least one of the groups, if less than this value, the row will be removed.

Value

a logical vector where the TRUE means row to keep

Examples

e1 <- matrix(rnorm(5000, sd = 0.3), 500, 10) + rnorm(500)
f <- filterRow(x = e1, max.quantile = 0.25)
table(f)

Get genes associated with search terms and AutoRIF annotations

Description

Get genes associated with search terms and AutoRIF annotations

Usage

getAutoRIF(term, rif = c("generif", "autorif")[1], filter = TRUE)

Arguments

term

a character vector of terms want to search
rif

either autorif or generif, see "https://maayanlab.cloud/geneshot/"
filter

whether the result should be filtered. The least frequently mentioned genes (most like 1 or 2 times) will be removed.

Value

a data.frame of 4 columns: gene, n, perc, rank.

Note

https://amp.pharm.mssm.edu/geneshot/

References

Alexander Lachmann, Brian M Schilder, Megan L Wojciechowicz, Denis Torre, Maxim V Kuleshov, Alexandra B Keenan, Avi Ma’ayan, Geneshot: search engine for ranking genes from arbitrary text queries, Nucleic Acids Research, Volume 47, Issue W1, 02 July 2019, Pages W571–W577, https://doi.org/10.1093/nar/gkz393

Alexander Lachmann, Brian M Schilder, Megan L Wojciechowicz, Denis Torre, Maxim V Kuleshov, Alexandra B Keenan, Avi Ma’ayan, Geneshot: search engine for ranking genes from arbitrary text queries, Nucleic Acids Research, Volume 47, Issue W1, 02 July 2019, Pages W571–W577, https://doi.org/10.1093/nar/gkz393

Examples

a <- getAutoRIF("mtor signaling")

Parse mqpar.xml file

Description

Getting the experimental informatione (TMT or label free) from mqpar.xml file.

Usage

getMQParams(x)

Arguments

x

the path to mqpar.xml file

Value

a list of MQ paramters

get uniprot reference proteome IDs

Description

get uniprot reference proteome IDs

get uniprot reference proteome IDs

Usage

getUPRefProteomeID(
  domain = c("Eukaryota", "Archaea", "Bacteria", "Viruses")[1]
)

downloadUPRefProteome(
  id,
  domain = c("Eukaryota", "Archaea", "Bacteria", "Viruses")[1],
  destdir = "./"
)

Arguments

domain

the domain, one of "Eukaryota", "Archaea", "Bacteria" or "Viruses"
id

the UP id to download
destdir

destination directory

Value

a character vector of UP ids

a character vector of UP ids

Functions

  • getUPRefProteomeID(): get uniprot reference protein IDs

Annotation of gene/protein function using multiple IDs.

Description

Annotation of gene/protein function using multiple IDs.

Usage

gsAnnotIdList(
  idList,
  gsIdMap,
  minSize = 5,
  maxSize = 500,
  data.frame = FALSE,
  sparse = TRUE
)

Arguments

idList

list of protein IDs, e.g. list(c("ID1", "ID2"), c("ID13"), c("ID4", "ID8", "ID10"))
gsIdMap

a data frame for geneset to id map, it has two columns - id: the ID column - term: annotation terms e.g. gsIdMap <- data.frame( id = c("ID1", "ID2", "ID1", "ID2", "ID8", "ID10"), term = c("T1", "T1", "T2", "T2", "T2", "T2"), stringsAsFactors = FALSE )
minSize

minimum size of gene sets
maxSize

maximum size of gene sets
data.frame

logical; whether to organize the result into data.frame format, see "Value" section.
sparse

logical; whether to return a sparse matrix, only used when data.frame=FALSE

Value

A binary matrix (if data.frame = FALSE), the number of rows is the same with length of idList, the columns are the annotated gene set; or a data.frame (if data.frame = TRUE) with three columns: featureId, gsId, weight.

Examples

terms <- data.frame(
  id = c("ID1", "ID2", "ID1", "ID2", "ID8", "ID10"),
 term = c("T1", "T1", "T2", "T2", "T2", "T2"),
  stringsAsFactors = FALSE
)
features <- list(c("ID1", "ID2"), c("ID13"), c("ID4", "ID8", "ID10"))
gsAnnotIdList(idList = features, gsIdMap = terms, minSize = 1, maxSize = 500)

terms <- data.frame(
id = c("ID1", "ID2", "ID1", "ID2", "ID8", "ID10", "ID4", "ID4"),
term = c("T1", "T1", "T2", "T2", "T2", "T2", "T1", "T2"),
stringsAsFactors = FALSE
)
features <- list(F1 = c("ID1", "ID2", "ID4"), F2 = c("ID13"), F3 = c("ID4", "ID8", "ID10"))
gsAnnotIdList(features, gsIdMap = terms, data.frame = TRUE, minSize = 1)
gsAnnotIdList(features, gsIdMap = terms, data.frame = FALSE, minSize = 1)

Check whether an object has an attribute

Description

Check whether an object has an attribute

Usage

hasAttr(x, attr.name)

Arguments

x

the object
attr.name

a character vector containing the name of attributes to be checked

Value

a logical value/vector has the same length as attr.name

Convert hclust object to/from single character

Description

Convert hclust object to/from single character

Usage

hclust2str(x)

str2hclust(x)

Arguments

x

a character of length one or an hclust object

Value

a character stores the hclust object

a hclust object

Note

The $call element in hclust will not retained in the conversion. The conversion decrease the precision in $height element.

Examples

# not for end users
# m <- matrix(rnorm(50), 25)
# hc <- hclust(dist(m))
# plot(hc)
# te <- hclust2str(hc)
# hc2 <- str2hclust(te)
# plot(hc2)

Calculate Jaccard distance from a list

Description

Calculate Jaccard distance from a list

Usage

jaccardList(x)

Arguments

x

a list

Value

an dist object

convert a list to column compressed sparse matrix

Description

convert a list to column compressed sparse matrix

Usage

list2csc(l, dimnames)

Arguments

l

a data.frame with at least two columns - featureId, gsId; optionally a "weight" column.
dimnames

a list of dimnames, should contain at least one element for the row names.

Value

a sparse matrix, CsparseMatrix, column compressed

Function to perform multiple t-tests on an expression matrix

Description

This is a convenience function to perform multiple student's t-test. The output is in a format ready to be incorporated into object to be visualized by omicsViewer. This function use t.test.

Usage

multi.t.test(x, pheno, compare = NULL, fillNA = FALSE, ...)

Arguments

x

an expression matrix, usually log10 transformed.
pheno

phenotype data of x, the number of rows in pheno must equal the number of columns of x. Please refer to examples for more details.
compare

NULL or a matrix with three columns to define the comparisons to do. When a matrix is given, the first column should be one of the column headers in pheno; then the second and third columns should be two values presented (more than once) in the columns of pheno selected by the values in the first column. The samples mapped to the two values are compared. If paired comparisons to be done, the orders of samples should be mapped
fillNA

logical; whether NA should be filled? If FALSE (default), t test will be performed whenever possible. If not possible, then NA will be returned. If TRUE, the missing value will be replaced using fillNA.
...

other parameters passed to t.test

Value

a data.frame stores the t-test results with the follow columns: mean|[selected header in pheno]|[group 1 in test] - The mean value of group 1 n value|[selected header in pheno]|[group 1 in test] - The number of value used in the test for group 1 quantile|[selected header in pheno]|[group 1 in test] - The quantile of means values in group 1 mean|[selected header in pheno]|[group 2 in test] - The mean value of group 2 n value|[selected header in pheno]|[group 2 in test] - The number of value used in the test for group 2 quantile|[selected header in pheno]|[group 2 in test] - The quantile of means values in group 2 ttest|[group 1 in test]_vs_[group 2 in test]|pvalue - The p-value return by t.test ttest|[group 1 in test]_vs_[group 2 in test]|log.pvalue - The -log10 transformed p-value ttest|[group 1 in test]_vs_[group 2 in test]|fdr - The BH method corrected p-values, e.g. FDR ttest|[group 1 in test]_vs_[group 2 in test]|log.fdr - The -log10 transformed FDR ttest|[group 1 in test]_vs_[group 2 in test]|mean.diff - The difference between the means of the two groups, e.g. fold change

Examples

# reading expression
packdir <- system.file("extdata", package = "omicsViewer")
expr <- read.delim(file.path(packdir, "expressionMatrix.tsv"), stringsAsFactors = FALSE)
# reading phenotype data
pd <- read.delim(file.path(packdir, "sampleGeneral.tsv"), stringsAsFactors = FALSE)

## Single t-test
head(pd)
# define comparisons
tests <- c("Origin", "RE", "ME")
tres <- multi.t.test(x = expr, pheno = pd, compare = tests)

## multiple t-test
head(pd)
# define comparisons
tests <- rbind(
c("Origin", "RE", "ME"),
c("Origin", "RE", "LE"),
c('TP53.Status', "MT", "WT")
)
tres <- multi.t.test(x = expr, pheno = pd, compare = tests)

Generating k distinct colors

Description

Mainly used in the shiny app to generate reproducible k distinct colors.

Usage

nColors(k, stop = FALSE)

Arguments

k

a number between 1 to 60 tells how many distinct colors to use
stop

logical; whether the function should return an error message if k is not in the range of 2 to 60. Default FALSE, the function will return NULL.

Value

a vector of hex code for k colors or NULL

Examples

nColors(5)
nColors(1, stop = FALSE)

Normalization using n quantiles

Description

Normalization using n quantiles

Usage

normalize.nQuantiles(x, probs = 0.5, shareFeature = FALSE, ref = 1)

Arguments

x

an expression matrix, usually log transformed
probs

the quantiles to be aligned across samples. If probs is a length 1 numerical vector, the quantiles will aligned. As a special case, probs = 0.5 equals the median centering. If probs' length is > 1, a shift and scaling factor of samples will be calculating by fitting linear models using quantiles of samples, the median and variance of samples will be corrected using the intersect and slope of the fitted model.
shareFeature

logocal; if TRUE, the normalization will be based on the shared features between samples
ref

the columns name or index to specify the reference sample, only used when shareFeature = TRUE

Value

a normalized matrix

Examples

e1 <- matrix(rnorm(5000), 500, 10)
e1[, 6:10] <- 0.3 *e1[, 6:10] + 3
boxplot(e1)
# median centering, no variance correction
e2 <- normalize.nQuantiles(x = e1, probs = 0.5)
boxplot(e2)
# median centering + variance stablization
e3 <- normalize.nQuantiles(x = e1, probs = seq(0.25, 0.75, by = 0.1))
boxplot(e3)

Normalize total sum

Description

Normalize total sum

Usage

normalize.totsum(x)

Arguments

x

a log10 transformed expression matrix

Value

a normalized matrix

Examples

e1 <- matrix(rnorm(5000), 500, 10)
e1[, 6:10] <- e1[, 6:10]+3
boxplot(e1)
e2 <- normalize.totsum(x = e1)
boxplot(e2)

Column-wise normalization of expression matrix

Description

A wrapper function of all column-wise normalization methods

Usage

normalizeColWise(
  x,
  method = c("Median centering", "Median centering (shared ID)", "Total sum",
    "median centering + variance stablization")[1]
)

Arguments

x

an expression matrix where rows are features and columns are samples, usually log transformed.
method

normalization method to use "Median centering" - median centering, see normalize.nQuantiles "Median centering (shared ID)" - median centering using shared features, see normalize.nQuantiles "Total sum" - total sum normalization "median centering + variance stablization" - 10 quantile normalization using 0.25, 0.3, ..., 0.75, see normalize.nQuantiles

Value

a normalized matrix

Examples

e1 <- matrix(rnorm(5000), 100, 50)+10
boxplot(e1)
e2 <- normalizeColWise(x = e1, method = "Median centering")
boxplot(e2)

Normalized expression matrix

Description

A wrapper function of all normalization methods, including row-wise or column-wise normalization.

Usage

normalizeData(
  x,
  colWise = c("None", "Median centering", "Median centering (shared ID)", "Total sum",
    "median centering + variance stablization")[1],
  rowWise = c("None", "Reference", "Batch mean", "Batch reference")[1],
  ref = NULL,
  batch = NULL
)

Arguments

x

an expression matrix where rows are features and columns are samples, usually log transformed.
colWise

column-wise normalization method to use, see normalizeColWise
rowWise

row-wise normalization method to used Reference - using removeVarQC method Batch mean - using rowshift method without reference samples Batch reference - using rowshift method with reference samples
ref

index of reference samples
batch

batch factor

Value

a normalized matrix

Examples

e1 <- matrix(rnorm(5000), 100, 50)+10
boxplot(e1)
e2 <- normalizeData(x = e1, ref = seq(5, 45, by = 10), rowWise = "Reference")
boxplot(e2)

Start omicsViewer

Description

Start omicsViewer

Usage

omicsViewer(
  dir,
  additionalTabs = NULL,
  filePattern = ".(RDS|DB|SQLITE|SQLITE3)$",
  ESVObj = NULL,
  esetLoader = readESVObj,
  exprsGetter = getExprs,
  pDataGetter = getPData,
  fDataGetter = getFData,
  defaultAxisGetter = getAx,
  appName = "omicsViewer",
  appVersion = packageVersion("omicsViewer")
)

Arguments

dir

directory to the ExpressionSet or SummarizedExperiment object. Only give the directory in this argument, not the .rds file.
additionalTabs

additional tabs added to "Analyst" panel
filePattern

file pattern to be displayed.
ESVObj

the ESV object
esetLoader

function to load the eset object, if an RDS file, should be "readRDS"
exprsGetter

function to get the expression matrix from eset
pDataGetter

function to get the phenotype data from eset
fDataGetter

function to get the feature data from eset
defaultAxisGetter

function to get the default axes to be visualized. It should be a function with two arguments: x - the object loaded to the viewer; what - one of "sx", "sy", "fx" and "fy", representing the sample space x-axis, sample space y-axis, feature space x-axis and feature space y-axis respectively.
appName

name of the application
appVersion

version of the application

Value

do not return values

Examples

1
## To start the shiny app: 
# omicsViewer(
#  system.file("extdata", package = "omicsViewer")
# )

Extract function annotation from uniprot .dat file

Description

Extract function annotation from uniprot .dat file

Usage

parseDatTerm(file, outputDir = NULL, ...)

Arguments

file

the .dat or .dat.gz file
outputDir

dir of output file
...

other parameters passed to readLines

Value

a data.frame parse from .dat file

Shiny module for boxplot using plotly - Module

Description

Shiny module for boxplot using plotly - Module

Usage

plot_roc_pr_module(
  input,
  output,
  session,
  reactive_param,
  reactive_checkpoint = reactive(TRUE)
)

Arguments

input

input
output

output
session

session
reactive_param

reactive value; argument pass to draw_roc_pr
reactive_checkpoint

reactive_value; check this value before render any plot/executing any calculation

Value

do not return any values

Examples

if (interactive()) {
  library(shiny)
  
  ui <- fluidPage(
    sliderInput("ngrp", label = "Number of groups", min = 2, max = 5, value = 2),
    plot_roc_pr_ui("testplot")
  )
  
  server <- function(input, output, session) {
    ng <- reactive(
      sample(letters[1:input$ngrp], size = 100, replace = TRUE)
    )
    callModule(
      plot_roc_pr_module, id = "testplot",
      reactive_param = reactive(list(
        x = ng(),
        y = rnorm(100)
      ))
    )
  }
 shinyApp(ui, server)
}

Draw dose-response curves

Description

Draw dose-response curves

Usage

plotDC(mod, ylab = "Abundance", lty = 2, pch = 19, cex = 1, logx = FALSE)

Arguments

mod

an drc object
ylab

ylab in plot function
lty

lty in plot function
pch

pch in plot function
cex

cex in plot function
logx

whether the x-axis should be in log scale

Draw dose response curve given parameters in the omicsViewer object

Description

Draw dose response curve given the feature Data/rowData, phenotype data/colData and expression matrix. The function is usually used in shinyApp.

Usage

plotDCMat(
  expr,
  pd,
  fd,
  featid,
  dose.var,
  curve.var = NULL,
  only.par = FALSE,
  ...
)

Arguments

expr

expression matrix
pd

phenotype data or colData
fd

feature data or rowData
featid

feature id to be visualized
dose.var

the column header indicating the dose/time/concentration
curve.var

the column header indicating the curve ids
only.par

logical value. If true, no plot generated, the function only returns the parameters of models.
...

other parameters passed to plot function, except col, pch, xlab, ylab

Shiny module for boxplot using plotly - Module

Description

Shiny module for boxplot using plotly - Module

Usage

plotly_boxplot_module(
  input,
  output,
  session,
  reactive_param_plotly_boxplot,
  reactive_checkpoint = reactive(TRUE)
)

Arguments

input

input
output

output
session

session
reactive_param_plotly_boxplot

reactive value; argument passed to plotly_boxplot
reactive_checkpoint

reactive_value; check this value before render any plot/executing any calculation

Value

do not return any values

Examples

if (interactive()) {
  
  library(shiny)
  
  ui <- fluidPage(
    plotly_boxplot_ui("testplotly")
  )
  
  server <- function(input, output, session) {
    
    x <- cbind(matrix(rnorm(10000, mean = 3), 1000, 10), matrix(rnorm(20000), 1000, 20))
    x[sample(1:length(x), size = 0.3*length(x))] <- NA
    rownames(x) <- paste("R", 1:nrow(x), sep = "")
    colnames(x) <- paste("C", 1:ncol(x), sep = "")
    callModule(plotly_boxplot_module, id = "testplotly",
               reactive_param_plotly_boxplot = reactive(list(
                 x = x# , i  = c(4, 20, 80)# , highlight = c(1, 4, 5, 20), extvar = 1:30
               ))
    )
  }
  
  shinyApp(ui, server)
}

Shiny module for boxplot using plotly - UI

Description

Function should only be used for the developers

Usage

plotly_boxplot_ui(id)

Arguments

id

id

Value

a tagList of UI components

a tagList of UI components

Examples

if (interactive()) {
  
  library(shiny)
  
  ui <- fluidPage(
    plotly_boxplot_ui("testplotly")
  )
  
  server <- function(input, output, session) {
    
    x <- cbind(matrix(rnorm(10000, mean = 3), 1000, 10), matrix(rnorm(20000), 1000, 20))
    x[sample(1:length(x), size = 0.3*length(x))] <- NA
    rownames(x) <- paste("R", 1:nrow(x), sep = "")
    colnames(x) <- paste("C", 1:ncol(x), sep = "")
    callModule(plotly_boxplot_module, id = "testplotly",
               reactive_param_plotly_boxplot = reactive(list(
                 x = x# , i  = c(4, 20, 80)# , highlight = c(1, 4, 5, 20), extvar = 1:30
               ))
    )
  }
  
  shinyApp(ui, server)
}

Shiny module for scatter plot using plotly - Module

Description

Function should only be used for the developers

Usage

plotly_scatter_module(
  input,
  output,
  session,
  reactive_param_plotly_scatter,
  reactive_regLine = reactive(FALSE),
  reactive_checkpoint = reactive(TRUE),
  htest_var1 = reactive(NULL),
  htest_var2 = reactive(NULL)
)

Arguments

input

input
output

output
session

sesion
reactive_param_plotly_scatter

reactive parammeters for plotly_scatter
reactive_regLine

logical show or hide the regression line
reactive_checkpoint

checkpoint
htest_var1

when the plot is a beeswarmplot, two groups could be selected for two group comparison, this argument gives the default value. Mainly used for restoring the saved session.
htest_var2

see above

Value

a list containing the information about the selected data points

an reactive object containing the information of selected, brushed points.

Examples

if (interactive()) {
  library(shiny)
  
  # two random variables
  x <- rnorm(30)
  y <- x + rnorm(30, sd = 0.5)
  
  # variables mapped to color, shape and size
  cc <- sample(letters[1:4], replace = TRUE, size = 30) 
  shape <- sample(c("S1", "S2", "S3"), replace = TRUE, size = 30)
  sz <- sample(c(10, 20, 30, replace = TRUE, size = 30))
  
  ui <- fluidPage(
    plotly_scatter_ui("test_scatter")
  )
  
  server <- function(input, output, session) {
    v <- callModule(plotly_scatter_module, id = "test_scatter",
                    # reactive_checkpoint = reactive(FALSE),
                    reactive_param_plotly_scatter = reactive(list(
                      x = x, y = y,
                      color = cc,
                      shape = shape,
                      size = sz,
                      tooltips = paste("A", 1:30)
                    )))
    observe(print(v()))
  }
  shinyApp(ui, server)
  
  
  
  # example beeswarm horizontal
  x <- rnorm(30)
  y <- sample(c("x", "y", "z"), size = 30, replace = TRUE)
  shinyApp(ui, server)
  
  # example beeswarm vertical
  x <- sample(c("x", "y", "z"), size = 30, replace = TRUE)
  y <- rnorm(30)
  shinyApp(ui, server)
  
  # return values 
  x <- c(5, 6, 3, 4, 1, 2)
  y <- c(5, 6, 3, 4, 1, 2)
  ui <- fluidPage(
    plotly_scatter_ui("test_scatter")
  )
  server <- function(input, output, session) {
    v <- callModule(plotly_scatter_module, id = "test_scatter",
                    reactive_param_plotly_scatter = reactive(list(
                      x = x, y = y, tooltips = paste("A", 1:6), highlight = 2:4
                    )))
    
    observe(print(v()))
  }
  shinyApp(ui, server)
}

Shiny module for scatter plot using plotly - UI

Description

Function should only be used for the developers

Usage

plotly_scatter_ui(id, height = "400px")

Arguments

id

id
height

figure height

Value

a tagList of UI components

Examples

if (interactive()) {
  library(shiny)
  
  # two random variables
  x <- rnorm(30)
  y <- x + rnorm(30, sd = 0.5)
  
  # variables mapped to color, shape and size
  cc <- sample(letters[1:4], replace = TRUE, size = 30) 
  shape <- sample(c("S1", "S2", "S3"), replace = TRUE, size = 30)
  sz <- sample(c(10, 20, 30, replace = TRUE, size = 30))
  
  ui <- fluidPage(
    plotly_scatter_ui("test_scatter")
  )
  
  server <- function(input, output, session) {
    v <- callModule(plotly_scatter_module, id = "test_scatter",
                    # reactive_checkpoint = reactive(FALSE),
                    reactive_param_plotly_scatter = reactive(list(
                      x = x, y = y,
                      color = cc,
                      shape = shape,
                      size = sz,
                      tooltips = paste("A", 1:30)
                    )))
    observe(print(v()))
  }
  shinyApp(ui, server)
  
  
  
  # example beeswarm horizontal
  x <- rnorm(30)
  y <- sample(c("x", "y", "z"), size = 30, replace = TRUE)
  shinyApp(ui, server)
  
  # example beeswarm vertical
  x <- sample(c("x", "y", "z"), size = 30, replace = TRUE)
  y <- rnorm(30)
  shinyApp(ui, server)
  
  # return values 
  x <- c(5, 6, 3, 4, 1, 2)
  y <- c(5, 6, 3, 4, 1, 2)
  ui <- fluidPage(
    plotly_scatter_ui("test_scatter")
  )
  server <- function(input, output, session) {
    v <- callModule(plotly_scatter_module, id = "test_scatter",
                    reactive_param_plotly_scatter = reactive(list(
                      x = x, y = y, tooltips = paste("A", 1:6), highlight = 2:4
                    )))
    
    observe(print(v()))
  }
  shinyApp(ui, server)
}

Prepare object to be viewed by omicsViewer

Description

This is a convenience function to prepare the data to be visualized using omicsViewer. The result of PCA and t-test could be included directly.

Usage

prepOmicsViewer(
  expr,
  pData,
  fData,
  PCA = TRUE,
  ncomp = min(8, ncol(expr)),
  pca.fillNA = TRUE,
  t.test = NULL,
  ttest.fillNA = FALSE,
  ...,
  gs = NULL,
  stringDB = NULL,
  surv = NULL,
  SummarizedExperiment = TRUE
)

Arguments

expr

expression matrix where the rows are feature and columns are samples, matrix should be log10 transformed and have unique row and column names
pData

phenotype data
fData

feature data
PCA

pca
ncomp

number of components to keep
pca.fillNA

logical, whether the NA should be filled with a constant in PCA.
t.test

will be passed to the compare argument in multi.t.test
ttest.fillNA

logical, whether the NA should be filled with a constant in t-test.
...

arguments passed to t.test, such as paired.
gs

gene-set data, please refer to examples for more details about the format
stringDB

the IDs that can be used in the STRING database (https://string-db.org/) query.
surv

survival data, please refer to examples for more details about the format
SummarizedExperiment

logical; whether to return an object of class SummarizedExperiment. If set to FALSE, the function will return an ExpressionSet object.

Value

an object of ExpressionSet or SummarizedExperiment that can be visualized using omicsViewer

Examples

packdir <- system.file("extdata", package = "omicsViewer")
# reading expression
expr <- read.delim(file.path(packdir, "expressionMatrix.tsv"), stringsAsFactors = FALSE)
colnames(expr) <- make.names(colnames(expr))
rownames(expr) <- make.names(rownames(expr))
# reading feature data
fd <- read.delim(file.path(packdir, "featureGeneral.tsv"), stringsAsFactors = FALSE)
# reading phenotype data
pd <- read.delim(file.path(packdir, "sampleGeneral.tsv"), stringsAsFactors = FALSE)

#  reading other datasets
drugData <- read.delim(file.path(packdir, "sampleDrug.tsv"))
# survival data
# this data is from cell line, the survival data are fake data to 
# show how to use the survival data in #' omicsViewer
surv <- read.delim(file.path(packdir, "sampleSurv.tsv"))
# gene set information
genesets <- read_gmt(file.path(packdir, "geneset.gmt"), data.frame = TRUE)
gsannot <- gsAnnotIdList(idList = rownames(fd), gsIdMap = genesets, data.frame = TRUE)

# Define t-test to be done, a matrix nx3
# every row define a t-test, the format
# [column header] [group 1 in the test] [group 2 in the test]
tests <- rbind(
 c("Origin", "RE", "ME"),
 c("Origin", "RE", "LE"),
 c('TP53.Status', "MT", "WT")
 )
# prepare column for stringDB query
strid <- sapply(strsplit(fd$Protein.ID, ";|-"), "[", 1)
###
d <- prepOmicsViewer(
  expr = expr, pData = pd, fData = fd, 
  PCA = TRUE, pca.fillNA = TRUE,
  t.test = tests, ttest.fillNA = FALSE, 
  gs = gsannot, stringDB = strid, surv = surv)
# feature space - default x axis
attr(d, "fx") <- "ttest|RE_vs_ME|mean.diff"
# feature space - default y axis
attr(d, "fy") <- "ttest|RE_vs_ME|log.fdr"
# sample space - default x axis
attr(d, "sx") <- "PCA|All|PC1("
# sample space - default y axis
attr(d, "sy") <- "PCA|All|PC2("
# Save object and view
# saveRDS(d, file = "dtest.RDS")
##  to open the viewer
# omicsViewer("./")

Reading gene set .gmt file

Description

Frequently the .gmt files are downloaed from MSigDB database

Usage

read_gmt(x, id = NA, data.frame = FALSE)

Arguments

x

the name/path of the gmt file to be read
id

the id used in gene sets, if is not NA, it should be either "SYMBOL" or "ENTREZ". Usually only used when reading the .gmt file downloaded from MSigDB.
data.frame

logical; whether to organize the data in data.frame format. Default is FALSE, a list will be returned.

Value

a list or data frame of gene set. When data.frame = TRUE, the returned object is a data.frame with two columns: id and term.

Examples

file <- system.file("extdata", package = "omicsViewer")
file <- file.path(file, "geneset.gmt")
gs <- read_gmt(file)

Reading proteinGroup table of MaxQuant output

Description

A convenience function to read the proteinGroups table of MaxQuant output. The function organize the result into different tables, e.g. iBAQ.

Usage

read.proteinGroups(x, quant = c("LF", "TMT")[1])

Arguments

x

the proteinGroup.txt file returned by MaxQuant search
quant

the quantification method, LF or TMT

Value

a list of tables extracted from proteinGroups.txt file

Read protein groups output of maxquant output and split it to columns

Description

Read protein groups output of maxquant output and split it to columns

Usage

read.proteinGroups.lf(file)

Arguments

file

Maxquant proteinGroup.txt file path

Value

a list of tables extracted from proteinGroups.txt file

Read the object of SummarizedExperiment or ExpressetSet to be visualized using omicsViewer

Description

This function accept a path to a sqlite database or RDS object. If an RDS file to be read, The function is similar to readRDS. It reads the object to R working environment and perform extra two things. 1. If the loaded data an class of SummarizedExperiment, it will be converted to ExpressionSet; 2. If the gene set annotatio is in matrix format, the gene set annotation is converted to data.frame format.

Usage

readESVObj(x)

Arguments

x

the path of an object of SummarizedExperiment or ExpressionSet, passed to readRDS

Value

an object of class ExpressionSet or SummarizedExperiment to be visualzied.

Examples

file <- system.file("extdata/demo.RDS", package = "omicsViewer")
obj <- readESVObj(file)

Removing variance of reference samples

Description

This normalization removes the variance in reference samples. The method do not need to specific the batch assignment but cannot work with data contains less than five common reference samples. A typical use of this normalization is to correct some drifting effect in mass spec based label free proteomics or untargeted metabolomics experiment. Usually, this is a very strong normalization should only be used with good reasons.

Usage

removeVarQC(x, ref, positive = TRUE, ...)

Arguments

x

an expression matrix
ref

the index of reference samples
positive

logical; force only positive values in the resulted matrix
...

if given, normalize.nQuantiles will be called first, the arguments here will be passed to normalize.nQuantiles

Value

a normalized matrix

Examples

e1 <- matrix(rnorm(5000), 100, 50)+10
e2 <- removeVarQC(x = e1, ref = seq(5, 45, by = 10))
boxplot(e2)

Row-wise normalization of expression matrix with or without reference sample

Description

Row-wise normalization of expression matrix with or without reference sample

Usage

rowshift(x, batch, ref = NULL, useMean = FALSE)

Arguments

x

an expression matrix where rows are features, e.g. genes, proteins and columns are samples. The values in the matrix are usually log transformed.
batch

a factor or vector has the same length as ncol(x) to indicate the batch assignment of samples.
ref

a logical vector has the same length as ncol(x) to indicated which columns are the common references among batches. If it is NULL (by default), the mean of all channels will be used as batch reference. When NA present in the reference channels, the mean values will be used in correction.
useMean

logical; whether to use means of batches, usually set to TRUE when no reference available

Value

a matrix (hopefully without/with less batch effect)

Examples

e1 <- matrix(rnorm(5000), 500, 10)
e1[, 6:10] <- e1[, 6:10] + 3
boxplot(e1)
f <- rep(c("a", "b"), each = 5)
e2 <- rowshift(x = e1, batch = f)
boxplot(e2)

Save the xcmsViewer result object as sqlite database

Description

Save the xcmsViewer result object as sqlite database

Usage

saveOmicsViewerDb(obj, db.file, overwrite = TRUE)

## S4 method for signature 'SummarizedExperiment,character'
saveOmicsViewerDb(obj, db.file, overwrite = TRUE)

## S4 method for signature 'ExpressionSet,character'
saveOmicsViewerDb(obj, db.file, overwrite = TRUE)

Arguments

obj

an object of class ExpressionSet or SummarizedExperiment
db.file

a character indicate file name of the database file
overwrite

logical. whether the database should be overwritten if exist already.

Value

the directory where the database saved

Examples

f <- system.file("extdata", "demo.RDS", package = "omicsViewer")
es <- readRDS(f)
# The following line will write a database file on your disk
# saveOmicsViewerDb(es, db.file = "./omicsViewerData.db")

The three-step selector - the module function

Description

The selector is used to select columns of phenotype and feature data. Function should only be used for the developers.

Usage

triselector_module(
  input,
  output,
  session,
  reactive_x,
  reactive_selector1 = reactive(NULL),
  reactive_selector2 = reactive(NULL),
  reactive_selector3 = reactive(NULL),
  label = "Group Label:"
)

Arguments

input

input
output

output
session

session
reactive_x

an nx3 matrix
reactive_selector1

default value for selector 1
reactive_selector2

default value for selector 2
reactive_selector3

default value for selector 3
label

of the triselector

Value

an reactive object containing the selected values

Examples

if (interactive()) {
  library(shiny)
  library(Biobase)
  
  file <- system.file("extdata/demo.RDS", package = "omicsViewer")
  dat <- readRDS(file)
  fData <- fData(dat)
  triset <- stringr::str_split_fixed(colnames(fData), '\\|', n= 3)
  
  ui <- fluidPage(
    triselector_ui("tres"),
    triselector_ui("tres2")
  )
  server <- function(input, output, session) {
    v1 <- callModule(triselector_module, id = "tres", reactive_x = reactive(triset),
                     reactive_selector1 = reactive("ttest"),
                     reactive_selector2 = reactive("RE_vs_ME"),
                     reactive_selector3 = reactive("mean.diff")
    )
    v2 <- callModule(triselector_module, id = "tres2", reactive_x = reactive(triset),
                     reactive_selector1 = reactive("ttest"),
                     reactive_selector2 = reactive("RE_vs_ME"),
                     reactive_selector3 = reactive("log.fdr"))
    observe({
      print("/////////////////////////")
      print(v1())
    })
  }
  
  shinyApp(ui, server)
}

The three-step selector - the ui function

Description

Function should only be used for the developers

Usage

triselector_ui(id, right_margin = "20")

Arguments

id

id
right_margin

margin on the right side, in px. For example, "20" translates to "20px".

Value

a tagList of UI components

Examples

if (interactive()) {
  library(shiny)
  library(Biobase)
  
  file <- system.file("extdata/demo.RDS", package = "omicsViewer")
  dat <- readRDS(file)
  fData <- fData(dat)
  triset <- stringr::str_split_fixed(colnames(fData), '\\|', n= 3)
  
  ui <- fluidPage(
    triselector_ui("tres"),
    triselector_ui("tres2")
  )
  server <- function(input, output, session) {
    v1 <- callModule(triselector_module, id = "tres", reactive_x = reactive(triset),
                     reactive_selector1 = reactive("ttest"),
                     reactive_selector2 = reactive("RE_vs_ME"),
                     reactive_selector3 = reactive("mean.diff")
    )
    v2 <- callModule(triselector_module, id = "tres2", reactive_x = reactive(triset),
                     reactive_selector1 = reactive("ttest"),
                     reactive_selector2 = reactive("RE_vs_ME"),
                     reactive_selector3 = reactive("log.fdr"))
    observe({
      print("/////////////////////////")
      print(v1())
    })
  }
  
  shinyApp(ui, server)
}

Create a nx3 matrix that can be use for triselector given a meta and expression table

Description

only used inside reactive

Usage

trisetter(meta, expr = NULL, combine)

Arguments

meta

a meta data, usually either phenotype data or feature data
expr

expression matrix, optional.
combine

how the meta and expression to be combined. Should be either "pheno" or "feature' or "none".

Value

a nx3 matrix

a data.frame with 3 columns

MQ folder validator Validate whether a folder is a MQ output folder

Description

MQ folder validator Validate whether a folder is a MQ output folder

Usage

validMQFolder(dir)

Arguments

dir

the directory to check

Details

from the root level, these files exist: mqpar.xml [[combined/]txt/]proteinGroups.txt

Value

a list containing the info about MQ folder check

variable selector

Description

variable selector

Usage

varSelector(x, expr, meta, alternative = NULL)

Arguments

x

variable return by triselector, a list of length three named as "analysis", "subset" and "variable'
expr

the expression matrix
meta

a meta matrix
alternative

alternative value to be returned when nothing to select

Value

the selected values in input argument x