Generating a class vector to be used for the decision tree analysis.

Description

This function generates a class vector for the input dataset so the decision tree analysis can be implemented afterwards.

Usage

ClassVectoringDT(
  object,
  Clustering = "K-means",
  K,
  First = "CL1",
  Second = "CL2",
  sigDEG,
  quiet = FALSE
)

## S4 method for signature 'DISCBIO'
ClassVectoringDT(
  object,
  Clustering = "K-means",
  K,
  First = "CL1",
  Second = "CL2",
  sigDEG,
  quiet = FALSE
)

Arguments

Value

A data frame.

ClustDiffGenes

Description

Creates a table of cluster differences

Usage

ClustDiffGenes(
  object,
  K,
  pValue = 0.05,
  fdr = 0.01,
  export = FALSE,
  quiet = FALSE,
  filename_up = "Up-DEG-cluster",
  filename_down = "Down-DEG-cluster",
  filename_binom = "binomial-DEGsTable",
  filename_sigdeg = "binomial-sigDEG"
)

## S4 method for signature 'DISCBIO'
ClustDiffGenes(
  object,
  K,
  pValue = 0.05,
  fdr = 0.01,
  export = FALSE,
  quiet = FALSE,
  filename_up = "Up-DEG-cluster",
  filename_down = "Down-DEG-cluster",
  filename_binom = "binomial-DEGsTable",
  filename_sigdeg = "binomial-sigDEG"
)

Arguments

Value

A list containing two tables.

Examples

sc <- DISCBIO(valuesG1msTest)
sc <- Clustexp(sc, cln = 3, quiet = TRUE)
cdiff <- ClustDiffGenes(sc, K = 3, fdr = .3, export = FALSE)
str(cdiff)
cdiff[[2]]

Clustering of single-cell transcriptome data

Description

This functions performs the initial clustering of the RaceID algorithm.

Usage

Clustexp(
  object,
  clustnr = 3,
  bootnr = 50,
  metric = "pearson",
  do.gap = TRUE,
  SE.method = "Tibs2001SEmax",
  SE.factor = 0.25,
  B.gap = 50,
  cln = 0,
  rseed = NULL,
  quiet = FALSE
)

## S4 method for signature 'DISCBIO'
Clustexp(
  object,
  clustnr = 3,
  bootnr = 50,
  metric = "pearson",
  do.gap = TRUE,
  SE.method = "Tibs2001SEmax",
  SE.factor = 0.25,
  B.gap = 50,
  cln = 0,
  rseed = NULL,
  quiet = FALSE
)

Arguments

Value

The DISCBIO-class object input with the cpart slot filled.

Examples

sc <- DISCBIO(valuesG1msTest) # changes signature of data
sc <- Clustexp(sc, cln = 2)

Determining differentially expressed genes (DEGs) between all individual clusters.

Description

This function defines DEGs between all individual clusters generated by either K-means or model based clustering.

Usage

DEGanalysis(
  object,
  K,
  Clustering = "K-means",
  fdr = 0.05,
  name = "Name",
  export = FALSE,
  quiet = FALSE,
  plot = TRUE,
  filename_deg = "DEGsTable",
  filename_sigdeg = "sigDEG",
  ...
)

## S4 method for signature 'DISCBIO'
DEGanalysis(
  object,
  K,
  Clustering = "K-means",
  fdr = 0.05,
  name = "Name",
  export = FALSE,
  quiet = FALSE,
  plot = TRUE,
  filename_deg = "DEGsTable",
  filename_sigdeg = "sigDEG",
  ...
)

Arguments

Value

A list containing two tables.

Determining differentially expressed genes (DEGs) between two particular clusters.

Description

This function defines DEGs between particular clusters generated by either K-means or model based clustering.

Usage

DEGanalysis2clust(
  object,
  K,
  Clustering = "K-means",
  fdr = 0.05,
  name = "Name",
  First = "CL1",
  Second = "CL2",
  export = FALSE,
  quiet = FALSE,
  plot = TRUE,
  filename_deg = "DEGsTable",
  filename_sigdeg = "sigDEG",
  ...
)

## S4 method for signature 'DISCBIO'
DEGanalysis2clust(
  object,
  K,
  Clustering = "K-means",
  fdr = 0.05,
  name = "Name",
  First = "CL1",
  Second = "CL2",
  export = FALSE,
  quiet = FALSE,
  plot = TRUE,
  filename_deg = "DEGsTable",
  filename_sigdeg = "sigDEG",
  ...
)

Arguments

Value

A list containing two tables.

The DISCBIO Class

Description

The DISCBIO class is the central object storing all information generated throughout the pipeline.

Arguments

Details

DISCBIO

Slots

SingleCellExperiment

Representation of the single cell input data, including both cells from regular and ERCC spike-in samples. Data are stored in a SingleCellExperiment object.

expdata

The raw expression data matrix with cells as columns and genes as rows in sparse matrix format. It does not contain ERCC spike-ins.

expdataAll

The raw expression data matrix with cells as columns and genes as rows in sparse matrix format. It can contain ERCC spike-ins.

ndata

Data with expression normalized to one for each cell.

fdata

Filtered data with expression normalized to one for each cell.

distances

A distance matrix.

tsne

A data.frame with coordinates of two-dimensional tsne layout for the K-means clustering.

background

A list storing the polynomial fit for the background model of gene expression variability. It is used for outlier identification.

out

A list storing information on outlier cells used for the prediction of rare cell types.

cpart

A vector containing the final clustering partition computed by K-means.

fcol

A vector contaning the colour scheme for the clusters.

filterpar

A list containing the parameters used for cell and gene filtering based on expression.

clusterpar

A list containing the parameters used for the K-means clustering.

outlierpar

A list containing the parameters used for outlier identification.

kmeans

A list containing the results of running the Clustexp() function.

MBclusters

A vector containing the final clustering partition computed by Model-based clustering.

kordering

A vector containing the Pseudo-time ordering based on k-means clusters.

MBordering

A vector containing the Pseudo-time ordering based on Model-based clusters.

MBtsne

A data.frame with coordinates of two-dimensional tsne layout for the Model-based clustering.

noiseF

A vector containing the gene list resulted from running the noise filtering.

FinalGeneList

A vector containing the final gene list resulted from running the noise filtering or/and the expression filtering.

Examples

class(valuesG1msTest)
G1_reclassified <- DISCBIO(valuesG1msTest)
class(G1_reclassified)
str(G1_reclassified, max.level = 2)
identical(G1_reclassified@expdataAll, valuesG1msTest)

Convert a DISCBIO object to a SingleCellExperiment.

Description

Extract the SingleCellExperiment input data from the corresponding input slot in a DISCBIO-class object

Usage

DISCBIO2SingleCellExperiment(x)

Arguments

Value

a SingleCellExperiment-class object

Examples

g1_disc <- DISCBIO(valuesG1msTest)
class(g1_disc)
g1_sce <- DISCBIO2SingleCellExperiment(g1_disc)
class(g1_sce)

Performing Model-based clustering on expression values

Description

this function first uses principal component analysis (PCA) to reduce dimensionality of original data. It then performs model-based clustering on the transformed expression values.

Usage

Exprmclust(
  object,
  K = 3,
  modelNames = "VVV",
  reduce = TRUE,
  cluster = NULL,
  quiet = FALSE
)

## S4 method for signature 'DISCBIO'
Exprmclust(
  object,
  K = 3,
  modelNames = "VVV",
  reduce = TRUE,
  cluster = NULL,
  quiet = FALSE
)

## S4 method for signature 'data.frame'
Exprmclust(
  object,
  K = 3,
  modelNames = "VVV",
  reduce = TRUE,
  cluster = NULL,
  quiet = FALSE
)

Arguments

Value

If 'object' is of class DISCBIO, the output is the same object with the MBclusters slot filled. If the 'object' is a data frame, the function returns a named list containing the four objects that together correspond to the contents of the MBclusters slot.

Final Preprocessing

Description

This function generates the final filtered normalized dataset.

Usage

FinalPreprocessing(
  object,
  GeneFlitering = "NoiseF",
  export = FALSE,
  quiet = FALSE,
  fileName = "filteredDataset"
)

## S4 method for signature 'DISCBIO'
FinalPreprocessing(
  object,
  GeneFlitering = "NoiseF",
  export = FALSE,
  quiet = FALSE,
  fileName = "filteredDataset"
)

Arguments

Value

The DISCBIO-class object input with the FinalGeneList slot filled.

Examples

#sc <- DISCBIO(valuesG1msTest)
#sc <- NoiseFiltering(sc, percentile = 0.9, CV = 0.2, export = FALSE)
#sc <- FinalPreprocessing(sc, GeneFlitering = "NoiseF", export = FALSE)

Inference of outlier cells

Description

This functions performs the outlier identification for k-means and model-based clustering

Usage

FindOutliers(
  object,
  K,
  outminc = 5,
  outlg = 2,
  probthr = 0.001,
  thr = 2^-(1:40),
  outdistquant = 0.75,
  plot = TRUE,
  quiet = FALSE
)

## S4 method for signature 'DISCBIO'
FindOutliers(
  object,
  K,
  outminc = 5,
  outlg = 2,
  probthr = 0.001,
  thr = 2^-(1:40),
  outdistquant = 0.75,
  plot = TRUE,
  quiet = FALSE
)

Arguments

Value

A named vector of the genes containing outlying cells and the number of cells on each.

Examples

sc <- DISCBIO(valuesG1msTest)
sc <- Clustexp(sc, cln = 2) # K-means clustering
FindOutliers(sc, K = 2)

Human and Mouse Gene Identifiers.

Description

Data.frame including ENTREZID, SYMBOL, and ENSEMBL gene identifiers of human and mouse genes.

Source

Data were imported, modified, and formatted from the Mus.musculus (ver 1.3.1) and the Homo.sapiens (ver 1.3.1) BioConductor libraries.

J48 Decision Tree

Description

The decision tree analysis is implemented over a training dataset, which consisted of the DEGs obtained by either SAMseq or the binomial differential expression.

Usage

J48DT(data, quiet = FALSE, plot = TRUE)

Arguments

Value

Information about the J48 model and, by default, a plot of the decision tree.

Evaluating the performance of the J48 decision tree.

Description

This function evaluates the performance of the generated trees for error estimation by ten-fold cross validation assessment.

Usage

J48DTeval(data, num.folds = 10, First = "CL1", Second = "CL2", quiet = FALSE)

Arguments

Value

Statistics about the J48 model

Jaccard’s similarity

Description

Robustness of the clusters can be assessed by Jaccard’s similarity, which reflects the reproducibility of individual clusters across bootstrapping runs. Jaccard’s similarity is the intersect of two clusters divided by the union.

Usage

Jaccard(object, Clustering = "K-means", K, plot = TRUE, R = 100)

Arguments

Value

A plot of the mean Jaccard similarity coefficient per cluster.

Pseudo-time ordering based on k-means clusters

Description

This function takes the exact output of exprmclust function and construct Pseudo-time ordering by mapping all cells onto the path that connects cluster centers.

Usage

KmeanOrder(
  object,
  quiet = FALSE,
  export = FALSE,
  filename = "Cellular_pseudo-time_ordering_based_on_k-meansc-lusters"
)

## S4 method for signature 'DISCBIO'
KmeanOrder(
  object,
  quiet = FALSE,
  export = FALSE,
  filename = "Cellular_pseudo-time_ordering_based_on_k-meansc-lusters"
)

Arguments

Value

The DISCBIO-class object input with the kordering slot filled.

Note

This function has been replaced by pseudoTimeOrdering(), but it is being kept for legacy purposes. It will, however, be removed from future versions of DIscBIO.

Networking analysis.

Description

This function checks the connectivity degree and the betweenness centrality, which reflect the communication flow in the defined PPI networks

Usage

NetAnalysis(data, export = FALSE, FileName = "NetworkAnalysisTable-1")

Arguments

Value

A network analysis table

Plotting the network.

Description

This function uses STRING API to plot the network.

Usage

Networking(
  data,
  FileName = NULL,
  species = "9606",
  plot_width = 25,
  plot_height = 15,
  retries = 3
)

Arguments

Value

A plot of the network

References

https://string-db.org/api/

Noise Filtering

Description

Given a matrix or data frame of count data, this function estimates the size factors as follows: Each column is divided by the geometric means of the rows. The median (or, if requested, another location estimator) of these ratios (skipping the genes with a # geometric mean of zero) is used as the size factor for this column. Source: DESeq package.

Usage

NoiseFiltering(
  object,
  percentile = 0.8,
  CV = 0.3,
  geneCol = "yellow",
  FgeneCol = "black",
  erccCol = "blue",
  Val = TRUE,
  plot = TRUE,
  export = FALSE,
  quiet = FALSE,
  filename = "Noise_filtering_genes_test"
)

## S4 method for signature 'DISCBIO'
NoiseFiltering(
  object,
  percentile = 0.8,
  CV = 0.3,
  geneCol = "yellow",
  FgeneCol = "black",
  erccCol = "blue",
  Val = TRUE,
  plot = TRUE,
  export = FALSE,
  quiet = FALSE,
  filename = "Noise_filtering_genes_test"
)

Arguments

Value

The DISCBIO-class object input with the noiseF slot filled.

Note

This function should be used only if the dataset has ERCC.

Examples

sc <- DISCBIO(valuesG1msTest) # changes signature of data
sd_filtered <- NoiseFiltering(sc, export = FALSE)
str(sd_filtered)

Normalizing and filtering

Description

This function allows filtering of genes and cells to be used in the downstream analysis.

Usage

Normalizedata(
  object,
  mintotal = 1000,
  minexpr = 0,
  minnumber = 0,
  maxexpr = Inf,
  downsample = FALSE,
  dsn = 1,
  rseed = NULL
)

## S4 method for signature 'DISCBIO'
Normalizedata(
  object,
  mintotal = 1000,
  minexpr = 0,
  minnumber = 0,
  maxexpr = Inf,
  downsample = FALSE,
  dsn = 1,
  rseed = NULL
)

Arguments

Value

The DISCBIO-class object input with the ndata and fdata slots filled.

Examples

sc <- DISCBIO(valuesG1msTest) # changes signature of data

# In this case this function is used to normalize the reads
sc_normal <- Normalizedata(
  sc,
  mintotal = 1000, minexpr = 0, minnumber = 0, maxexpr = Inf,
  downsample = FALSE, dsn = 1, rseed = 17000
)
summary(sc_normal@fdata)

Plot PCA symbols

Description

Generates a plot of grouped PCA components

Usage

PCAplotSymbols(object, types = NULL)

## S4 method for signature 'DISCBIO'
PCAplotSymbols(object, types = NULL)

Arguments

Value

Plot of the Principal Components

Defining protein-protein interactions (PPI) over a list of genes,

Description

This function uses STRING-api. The outcome of STRING analysis will be stored in comma-separated values files.

Usage

PPI(data, FileName = NULL, species = "9606")

Arguments

Value

Either CSV files stored in the user's file system and its corresponding 'data.frame' object in R or and R object containing that information.

Plotting pseudo-time ordering or gene expression in Model-based clustering in PCA

Description

The PCA representation can either be used to show pseudo-time ordering or the gene expression of a particular gene.

Usage

PlotMBpca(object, type = "order", g = NULL, n = NULL)

Arguments

Value

A plot of the PCA.

Plotting the Model-based clusters in PCA.

Description

Plot the model-based clustering results

Usage

PlotmclustMB(object)

## S4 method for signature 'DISCBIO'
PlotmclustMB(object)

Arguments

Value

A plot of the PCA.

RPART Decision Tree

Description

The decision tree analysis is implemented over a training dataset, which consisted of the DEGs obtained by either SAMseq or the binomial differential expression.

Usage

RpartDT(data, quiet = FALSE, plot = TRUE)

Arguments

Value

Information about the model and, by default, a plot of the decision tree.

Evaluating the performance of the RPART Decision Tree.

Description

This function evaluates the performance of the generated trees for error estimation by ten-fold cross validation assessment.

Usage

RpartEVAL(data, num.folds = 10, First = "CL1", Second = "CL2", quiet = FALSE)

Arguments

Value

Performance statistics of the model

Volcano Plot

Description

Plotting differentially expressed genes (DEGs) in a particular cluster. Volcano plots are used to readily show the DEGs by plotting significance versus fold-change on the y and x axes, respectively.

Usage

VolcanoPlot(object, value = 0.05, name = NULL, fc = 0.5, FS = 0.4)

Arguments

Value

A volcano plot

Convert Single Cell Data Objects to DISCBIO.

Description

Initialize a DISCBIO-class object starting from a SingleCellExperiment object or a Seurat object

Usage

as.DISCBIO(x, ...)

Arguments

Details

Additional parameters to pass to 'list' include, if x is a Seurat object, "assay", which is a string indicating the assay slot used to obtain data from (defaults to 'RNA')

Value

a DISCBIO-class object

Check format

Description

Check format

Usage

check.format(y, resp.type, censoring.status = NULL)

Arguments

Plotting clusters in a heatmap representation of the cell distances

Description

This functions plots a heatmap of the distance matrix grouped by clusters. Individual clusters are highlighted with rainbow colors along the x and y-axes.

Usage

clustheatmap(
  object,
  clustering_method = "k-means",
  hmethod = "single",
  rseed = NULL,
  quiet = FALSE,
  plot = TRUE
)

## S4 method for signature 'DISCBIO'
clustheatmap(
  object,
  clustering_method = "k-means",
  hmethod = "single",
  rseed = NULL,
  quiet = FALSE,
  plot = TRUE
)

Arguments

Value

Unless otherwise specified, a heatmap and a vector of the underlying cluster order.

Computing tSNE

Description

This function is used to compute the t-Distributed Stochastic Neighbor Embedding (t-SNE).

Usage

comptSNE(
  object,
  rseed = NULL,
  max_iter = 5000,
  epoch = 500,
  quiet = FALSE,
  ...
)

## S4 method for signature 'DISCBIO'
comptSNE(
  object,
  rseed = NULL,
  max_iter = 5000,
  epoch = 500,
  quiet = FALSE,
  ...
)

Arguments

Value

The DISCBIO-class object input with the tsne slot filled.

Examples

sc <- DISCBIO(valuesG1msTest) # changes signature of data
sc <- Clustexp(sc, cln = 2) # data must be clustered before plottin
sc <- comptSNE(sc, max_iter = 30)
head(sc@tsne)

Automatic Feature Id Conversion.

Description

Attempt to automatically convert non-ENSEMBL feature identifiers to ENSEMBL identifiers. Features are included as rownames of the input data.frame (or matrix). It is assumed that feature identifiers (i.e., rownames of x) come from human or mouse genomes, and are either OFFICIAL SYMBOLS or ENTREZIDS. If less than 20 is identified, an error will be thrown.

Usage

customConvertFeats(x, verbose = TRUE)

Arguments

Value

a data.frame where rownames are ENSEMBL IDs. The new feature IDs are automatically imputed based on existing feature IDs (SYMBOLs or ENTREZIDs).

Foldchange of twoclass unpaired sequencing data

Description

Foldchange of twoclass unpaired sequencing data

Usage

foldchange.seq.twoclass.unpaired(x, y, depth)

Arguments

Highlighting gene expression in the t-SNE map

Description

The t-SNE map representation can also be used to analyze expression of a gene or a group of genes, to investigate cluster specific gene expression patterns

Usage

plotExptSNE(object, g, n = NULL)

## S4 method for signature 'DISCBIO'
plotExptSNE(object, g, n = NULL)

Arguments

Value

t-SNE plot for one particular gene

Plotting Gap Statistics

Description

Plotting Gap Statistics

Usage

plotGap(object, y_limits = NULL)

## S4 method for signature 'DISCBIO'
plotGap(object, y_limits = NULL)

Arguments

Value

A plot of the gap statistics

tSNE map with labels

Description

Visualizing k-means or model-based clusters using tSNE maps

Usage

plotLabelstSNE(object)

## S4 method for signature 'DISCBIO'
plotLabelstSNE(object)

Arguments

Value

Plot containing the ID of the cells in each cluster

Plotting the pseudo-time ordering in the t-SNE map

Description

The tSNE representation can also be used to show the pseudo-time ordering.

Usage

plotOrderTsne(object)

## S4 method for signature 'DISCBIO'
plotOrderTsne(object)

Arguments

Value

A plot of the pseudo-time ordering.

Silhouette Plot for K-means clustering

Description

The silhouette provides a representation of how well each point is represented by its cluster in comparison to the closest neighboring cluster. It computes for each point the difference between the average similarity to all points in the same cluster and to all points in the closest neighboring cluster. This difference it normalize such that it can take values between -1 and 1 with higher values reflecting better representation of a point by its cluster.

Usage

plotSilhouette(object, K)

## S4 method for signature 'DISCBIO'
plotSilhouette(object, K)

Arguments

Value

A silhouette plot

tSNE map for K-means clustering with symbols

Description

Visualizing the K-means clusters using tSNE maps

Usage

plotSymbolstSNE(object, types = NULL, legloc = "bottomright")

## S4 method for signature 'DISCBIO'
plotSymbolstSNE(object, types = NULL, legloc = "bottomright")

Arguments

Value

Plot of tsne objet slot, grouped by gene.

tSNE map

Description

Visualizing the k-means or model-based clusters using tSNE maps

Usage

plottSNE(object)

## S4 method for signature 'DISCBIO'
plottSNE(object)

Arguments

Value

A plot of t-SNEs.

Prepare Example Dataset

Description

Internal function that prepares a pre-treated dataset for use in several examples

Usage

prepExampleDataset(dataset, save = TRUE)

Arguments

Details

This function serves the purpose of treating datasets such as valuesG1msReduced to reduce examples of other functions by bypassing some analysis steps covered in the vignettes.

Value

Two rda files, ones for K-means clustering and another for Model-based clustering.

Author(s)

Waldir Leoncio

Pseudo-time ordering

Description

This function takes the exact output of exprmclust function and construct Pseudo-time ordering by mapping all cells onto the path that connects cluster centers.

Usage

pseudoTimeOrdering(
  object,
  quiet = FALSE,
  export = FALSE,
  filename = "Cellular_pseudo-time_ordering"
)

## S4 method for signature 'DISCBIO'
pseudoTimeOrdering(
  object,
  quiet = FALSE,
  export = FALSE,
  filename = "Cellular_pseudo-time_ordering"
)

Arguments

Value

The DISCBIO-class object input with the kordering slot filled.

Rank columns

Description

Ranks the elements within each col of the matrix x and returns these ranks in a matrix

Usage

rankcols(x)

Arguments

Note

this function is equivalent to 'samr::rankcol', but uses 'apply' to rank the colums instead of a compiled Fortran function which was causing our DEGanalysis functions to freeze in large datasets.

Reformat Siggenes Table

Description

Reformats the Siggenes table output from the SAMR package

Usage

reformatSiggenes(table)

Arguments

Author(s)

Waldir Leoncio

See Also

replaceDecimals

Replace Decimals

Description

Replaces decimals separators between comma and periods on a character vector

Usage

replaceDecimals(x, from = ",", to = ".")

Arguments

Note

This function was especially designed to be used with retormatSiggenes

See Also

reformatSiggenes

Resampling

Description

Corresponds to 'samr::resample'

Usage

resa(x, d, nresamp = 20)

Arguments

Value

xresamp: an rank array with dim #gene*#sample*nresamp

Retries a URL

Description

Retries a URL

Usage

retrieveURL(data, species, outputFormat, maxRetries = 3, successCode = 200)

Arguments

Value

either the output of httr::GET or an error message

Author(s)

Waldir Leoncio

Significance analysis of microarrays

Description

This function is an adaptation of 'samr::samr'

Usage

sammy(
  data,
  resp.type = c("Quantitative", "Two class unpaired", "Survival", "Multiclass",
    "One class", "Two class paired", "Two class unpaired timecourse",
    "One class timecourse", "Two class paired timecourse", "Pattern discovery"),
  assay.type = c("array", "seq"),
  s0 = NULL,
  s0.perc = NULL,
  nperms = 100,
  center.arrays = FALSE,
  testStatistic = c("standard", "wilcoxon"),
  time.summary.type = c("slope", "signed.area"),
  regression.method = c("standard", "ranks"),
  return.x = FALSE,
  knn.neighbors = 10,
  random.seed = NULL,
  nresamp = 20,
  nresamp.perm = NULL,
  xl.mode = c("regular", "firsttime", "next20", "lasttime"),
  xl.time = NULL,
  xl.prevfit = NULL
)

Arguments

Estimate sequencing depths

Description

Estimate sequencing depths

Usage

samr.estimate.depth(x)

Arguments

Value

depth: estimated sequencing depth. a vector with len sample.

Single-cells data from a myxoid liposarcoma cell line

Description

A sample of single cells from a myxoid liposarcoma cell line. Columns refer to samples and rows refer to genes. The last rows refer to external RNA controls consortium (ERCC) spike-ins. This dataset is part of a larger dataset containing 94 single cells. The complete dataset is fully compatible with this package and an rda file can be obtained at https://github.com/ocbe-uio/DIscBIO/blob/dev/data/valuesG1ms.rda

Twoclass Wilcoxon statistics

Description

Twoclass Wilcoxon statistics

Usage

wilcoxon.unpaired.seq.func(xresamp, y)

Arguments

Value

the statistic.