Package 'DIscBIO'

Title: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics
Description: An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.
Authors: Salim Ghannoum [aut, cph], Alvaro Köhn-Luque [aut, ths], Waldir Leoncio [cre, aut], Damiano Fantini [ctb]
Maintainer: Waldir Leoncio <[email protected]>
License: MIT + file LICENSE
Version: 1.2.2
Built: 2024-10-03 05:01:57 UTC
Source: https://github.com/ocbe-uio/discbio

Help Index

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

x

an object of class Seurat or SingleCellExperiment
...

additional parameters to pass to the function

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

y

y
resp.type

resp type
censoring.status

censoring status

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

object

DISCBIO class object.
Clustering

Clustering has to be one of the following: ["K-means", "MB"]. Default is "K-means"
K

A numeric value of the number of clusters.
First

A string vector showing the first target cluster. Default is "CL1"
Second

A string vector showing the second target cluster. Default is "CL2"
sigDEG

A data frame of the differentially expressed genes (DEGs) generated by running "DEGanalysis()" or "DEGanalysisM()".
quiet

If 'TRUE', suppresses intermediary output

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

object

DISCBIO class object.
K

A numeric value of the number of clusters.
pValue

A numeric value of the p-value. Default is 0.05.
fdr

A numeric value of the false discovery rate. Default is 0.01.
export

A logical vector that allows writing the final gene list in excel file. Default is TRUE.
quiet

if 'TRUE', suppresses intermediate text output
filename_up

Name of the exported "up" file (if 'export=TRUE')
filename_down

Name of the exported "down" file (if 'export=TRUE')
filename_binom

Name of the exported binomial file
filename_sigdeg

Name of the exported sigDEG file

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

object

DISCBIO class object.
clustnr

Maximum number of clusters for the derivation of the cluster number by the saturation of mean within-cluster-dispersion. Default is 20.
bootnr

A numeric value of booststrapping runs for clusterboot. Default is 50.
metric

Is the method to transform the input data to a distance object. Metric has to be one of the following: ["spearman", "pearson", "kendall", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"].
do.gap

A logical vector that allows generating the number of clusters based on the gap statistics. Default is TRUE.
SE.method

The SE.method determines the first local maximum of the gap statistics. The SE.method has to be one of the following:["firstSEmax", "Tibs2001SEmax", "globalSEmax", "firstmax", "globalmax"]. Default is "Tibs2001SEmax"
SE.factor

A numeric value of the fraction of the standard deviation by which the local maximum is required to differ from the neighboring points it is compared to. Default is 0.25.
B.gap

Number of bootstrap runs for the calculation of the gap statistics. Default is 50
cln

Number of clusters to be used. Default is NULL and the cluster number is inferred by the saturation criterion.
rseed

Random integer to enforce reproducible clustering results.
quiet

if 'TRUE', intermediate output is suppressed

Value

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

Examples

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

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

object

DISCBIO class object.
clustering_method

either "k-means" or "model-based" ("k" and "mb" are also accepted)
hmethod

Agglomeration method used for determining the cluster order from hierarchical clustering of the cluster medoids. This should be one of "ward.D", "ward.D2", "single", "complete", "average". Default is "single".
rseed

Random integer to fix random results.
quiet

if 'TRUE', intermediary output is suppressed
plot

if 'TRUE', plots the heatmap; otherwise, just prints cclmo

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

object

DISCBIO class object.
rseed

Random integer to to yield reproducible maps across different runs
max_iter

maximum number of iterations to perform.
epoch

The number of iterations in between update messages.
quiet

if 'TRUE', suppresses intermediate output
...

other parameters to be passed to 'tsne::tsne'

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

x

data.frame or matrix including raw counts (typically, UMIs), where rows are features (genes) and rownames are feature identifiers (SYMBOLs or ENTREZIDs).
verbose

logical, shall messages be printed to inform about conversion advances.

Value

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

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

object

DISCBIO class object.
K

A numeric value of the number of clusters.
Clustering

Clustering has to be one of the following: ["K-means","MB"]. Default is "K-means"
fdr

A numeric value of the false discovery rate. Default is 0.05.
name

A string vector showing the name to be used to save the resulted tables.
export

A logical vector that allows writing the final gene list in excel file. Default is TRUE.
quiet

if 'TRUE', suppresses intermediate text output
plot

if 'TRUE', plots are generated
filename_deg

Name of the exported DEG table
filename_sigdeg

Name of the exported sigDEG table
...

additional parameters to be passed to samr()

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

object

DISCBIO class object.
K

A numeric value of the number of clusters.
Clustering

Clustering has to be one of the following: ["K-means","MB"]. Default is "K-means"
fdr

A numeric value of the false discovery rate. Default is 0.05.
name

A string vector showing the name to be used to save the resulted tables.
First

A string vector showing the first target cluster. Default is "CL1"
Second

A string vector showing the second target cluster. Default is "CL2"
export

A logical vector that allows writing the final gene list in excel file. Default is TRUE.
quiet

if 'TRUE', suppresses intermediate text output
plot

if 'TRUE', plots are generated
filename_deg

Name of the exported DEG table
filename_sigdeg

Name of the exported sigDEG table
...

additional parameters to be passed to samr()

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

object

An DISCBIO object.

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

x

an object of class DISCBIO

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

object

DISCBIO class object.
K

An integer vector specifying all possible cluster numbers. Default is 3.
modelNames

model to be used in model-based clustering. By default "ellipsoidal, varying volume, shape, and orientation" is used.
reduce

A logical vector that allows performing the PCA on the expression data. Default is TRUE.
cluster

A vector showing the ID of cells in the clusters.
quiet

if 'TRUE', suppresses intermediary output

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

object

DISCBIO class object.
GeneFlitering

GeneFlitering has to be one of the followings: ["NoiseF","ExpF"]. Default is "NoiseF"
export

A logical vector that allows writing the final gene list in excel file. Default is TRUE.
quiet

if 'TRUE', intermediary output is suppressed
fileName

File name for exporting (if 'export = TRUE')

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

object

DISCBIO class object.
K

Number of clusters to be used.
outminc

minimal transcript count of a gene in a clusters to be tested for being an outlier gene. Default is 5.
outlg

Minimum number of outlier genes required for being an outlier cell. Default is 2.
probthr

outlier probability threshold for a minimum of outlg genes to be an outlier cell. This probability is computed from a negative binomial background model of expression in a cluster. Default is 0.001.
thr

probability values for which the number of outliers is computed in order to plot the dependence of the number of outliers on the probability threshold. Default is 2**-(1:40).set
outdistquant

Real number between zero and one. Outlier cells are merged to outlier clusters if their distance smaller than the outdistquant-quantile of the distance distribution of pairs of cells in the orginal clusters after outlier removal. Default is 0.75.
plot

if 'TRUE', produces a plot of -log10prob per K
quiet

if 'TRUE', intermediary output is suppressed

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)

Foldchange of twoclass unpaired sequencing data

Description

Foldchange of twoclass unpaired sequencing data

Usage

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

Arguments

x

x
y

y
depth

depth

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

data

A data frame resulted from running the function ClassVectoringDT.
quiet

If 'TRUE', suppresses intermediary output
plot

If 'FALSE', suppresses plot output

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

data

The resulted data from running the function J48DT.
num.folds

A numeric value of the number of folds for the cross validation assessment. Default is 10.
First

A string vector showing the first target cluster. Default is "CL1"
Second

A string vector showing the second target cluster. Default is "CL2"
quiet

If 'TRUE', suppresses intermediary output

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

object

DISCBIO class object.
Clustering

Clustering has to be one of the following: ["K-means","MB"]. Default is "K-means"
K

A numeric value of the number of clusters
plot

if 'TRUE', plots the mean Jaccard similarities
R

number of bootstrap replicates

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

object

DISCBIO class object.
quiet

if 'TRUE', suppresses intermediary output
export

if 'TRUE', exports order table to csv
filename

Name of the exported file (if 'export=TRUE')

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

data

Protein-protein interaction data frame resulted from running the PPI function.
export

if 'TRUE', exports the analysis table as a csv file
FileName

suffix for the file name (if export = TRUE)

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

data

A gene list.
FileName

A string vector showing the name to be used to save the resulted network. If 'NULL', the network will be saved to a temporary directory
species

The taxonomy name/id. Default is "9606" for Homo sapiens.
plot_width

Plot width
plot_height

Plot height
retries

maximum number of attempts to connect to the STRING api.

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

object

DISCBIO class object.
percentile

A numeric value of the percentile. It is used to validate the ERCC spik-ins. Default is 0.8.
CV

A numeric value of the coefficient of variation. It is used to validate the ERCC spik-ins. Default is 0.5.
geneCol

Color of the genes that did not pass the filtration.
FgeneCol

Color of the genes that passt the filtration.
erccCol

Color of the ERCC spik-ins.
Val

A logical vector that allows plotting only the validated ERCC spike-ins. Default is TRUE. If Val=FALSE will plot all the ERCC spike-ins.
plot

A logical vector that allows plotting the technical noise. Default is TRUE.
export

A logical vector that allows writing the final gene list in excel file. Default is TRUE.
quiet

if 'TRUE', suppresses printed output
filename

Name of the exported file (if 'export=TRUE')

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

object

DISCBIO class object.
mintotal

minimum total transcript number required. Cells with less than mintotal transcripts are filtered out. Default is 1000.
minexpr

minimum required transcript count of a gene in at least minnumber cells. All other genes are filtered out. Default is 0.
minnumber

minimum number of cells that are expressing each gene at minexpr transcripts. Default is 0.
maxexpr

maximum allowed transcript count of a gene in at least a single cell after normalization or downsampling. All other genes are filtered out. Default is Inf.
downsample

A logical vector. Default is FALSE. If downsample is set to TRUE, then transcript counts are downsampled to mintotal transcripts per cell, instead of the normalization. Downsampled versions of the transcript count data are averaged across dsn samples
dsn

A numeric value of the number of samples to be used to average the downsampled versions of the transcript count data. Default is 1 which means that sampling noise should be comparable across cells. For high numbers of dsn the data will become similar to the median normalization.
rseed

Random integer to enforce reproducible clustering. results

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

object

DISCBIO class object.
types

If types=NULL then the names of the cells will be grouped automatically. Default is NULL

Value

Plot of the Principal Components

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

object

DISCBIO class object.
g

Individual gene name or vector with a group of gene names corresponding to a subset of valid row names of the ndata slot of the DISCBIO object.
n

String of characters representing the title of the plot. Default is NULL and the first element of g is chosen.

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

object

DISCBIO class object.
y_limits

2-length numeric vector with the limits for the gap plot

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

object

DISCBIO class object.

Value

Plot containing the ID of the cells in each cluster

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

object

DISCBIO class object.
type

either 'order' to plot pseudo-time ordering or 'exp' to plot gene expression
g

Individual gene name or vector with a group of gene names corresponding to a subset of valid row names of the ndata slot of the DISCBIO object. Ignored if 'type="order"'.
n

String of characters representing the title of the plot. Default is NULL and the first element of g is chosen. Ignored if 'type="order"'.

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

object

DISCBIO class object.

Value

A plot of the PCA.

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

object

DISCBIO class object.

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

object

DISCBIO class object.
K

A numeric value of the number of clusters

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

object

DISCBIO class object.
types

If types=NULL then the names of the cells will be grouped automatically. Default is NULL
legloc

A keyword from the list "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center". Default is "bottomright"

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

object

DISCBIO class object.

Value

A plot of t-SNEs.

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

data

A gene list.
FileName

A string vector showing the name to be used to save the resulted table. If null, no file will be exported
species

The taxonomy name/id. Default is "9606" for Homo sapiens.

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.

Prepare Example Dataset

Description

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

Usage

prepExampleDataset(dataset, save = TRUE)

Arguments

dataset

Dataset used for transformation
save

save results?

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

object

DISCBIO class object.
quiet

if 'TRUE', suppresses intermediary output
export

if 'TRUE', exports order table to csv
filename

Name of the exported file (if 'export=TRUE')

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

x

x

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

table

output from 'samr::samr.compute.siggenes.table'

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

x

vector of characters
from

decimal separator on input file
to

decimal separator for output file

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

x

data matrix. nrow=#gene, ncol=#sample
d

estimated sequencing depth
nresamp

number of resamplings

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

data

A gene list
species

The taxonomy name/id. Default is "9606" for Homo sapiens
outputFormat

format of the output. Can be "highres_image", "tsv", "json", "tsv-no-header", "xml"
maxRetries

maximum number of attempts to connect to the STRING api.
successCode

Status code number that represents success

Value

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

Author(s)

Waldir Leoncio

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

data

The exact output of the exprmclust function.
quiet

If 'TRUE', suppresses intermediary output
plot

If 'FALSE', suppresses plot output

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

data

The resulted data from running the function J48DT.
num.folds

A numeric value of the number of folds for the cross validation assessment. Default is 10.
First

A string vector showing the first target cluster. Default is "CL1"
Second

A string vector showing the second target cluster. Default is "CL2"
quiet

If 'TRUE', suppresses intermediary output

Value

Performance statistics of the model

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

data

Data object with components x- p by n matrix of features, one observation per column (missing values allowed); y- n-vector of outcome measurements; censoring.status- n-vector of censoring censoring.status (1= died or event occurred, 0=survived, or event was censored), needed for a censored survival outcome
resp.type

Problem type: "Quantitative" for a continuous parameter (Available for both array and sequencing data); "Two class unpaired" (for both array and sequencing data); "Survival" for censored survival outcome (for both array and sequencingdata); "Multiclass": more than 2 groups (for both array and sequencing data); "One class" for a single group (only for array data); "Two class paired" for two classes with paired observations (for both array and sequencing data); "Two class unpaired timecourse" (only for array data), "One class time course" (only for array data), "Two class.paired timecourse" (only for array data), or "Pattern discovery" (only for array data)
assay.type

Assay type: "array" for microarray data, "seq" for counts from sequencing
s0

Exchangeability factor for denominator of test statistic; Default is automatic choice. Only used for array data.
s0.perc

Percentile of standard deviation values to use for s0; default is automatic choice; -1 means s0=0 (different from s0.perc=0, meaning s0=zeroeth percentile of standard deviation values= min of sd values. Only used for array data.
nperms

Number of permutations used to estimate false discovery rates
center.arrays

Should the data for each sample (array) be median centered at the outset? Default =FALSE. Only used for array data.
testStatistic

Test statistic to use in two class unpaired case.Either "standard" (t-statistic) or ,"wilcoxon" (Two-sample wilcoxon or Mann-Whitney test). Only used for array data.
time.summary.type

Summary measure for each time course: "slope", or "signed.area"). Only used for array data.
regression.method

Regression method for quantitative case: "standard", (linear least squares) or "ranks" (linear least squares on ranked data). Only used for array data.
return.x

Should the matrix of feature values be returned? Only useful for time course data, where x contains summaries of the features over time. Otherwise x is the same as the input data
knn.neighbors

Number of nearest neighbors to use for imputation of missing features values. Only used for array data.
random.seed

Optional initial seed for random number generator (integer)
nresamp

For assay.type="seq", number of resamples used to construct test statistic. Default 20. Only used for sequencing data.
nresamp.perm

For assay.type="seq", number of resamples used to construct test statistic for permutations. Default is equal to nresamp and it must be at most nresamp. Only used for sequencing data.
xl.mode

Used by Excel interface
xl.time

Used by Excel interface
xl.prevfit

Used by Excel interface

Estimate sequencing depths

Description

Estimate sequencing depths

Usage

samr.estimate.depth(x)

Arguments

x

data matrix. nrow=#gene, ncol=#sample

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

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

object

A data frame showing the differentially expressed genes (DEGs) in a particular cluster
value

A numeric value of the false discovery rate. Default is 0.05.. Default is 0.05
name

A string vector showing the name to be used on the plot title
fc

A numeric value of the fold change. Default is 0.5.
FS

A numeric value of the font size. Default is 0.4.

Value

A volcano plot

Twoclass Wilcoxon statistics

Description

Twoclass Wilcoxon statistics

Usage

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

Arguments

xresamp

an rank array with dim #gene*#sample*nresamp
y

outcome vector of values 1 and 2

Value

the statistic.