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DIscBIO (version 1.1.0)

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. This package implements extensions of the work published by Ghannoum et. al. (2019) .

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Install

install.packages('DIscBIO')

Monthly Downloads

264

Version

1.1.0

License

MIT + file LICENSE

Issues

3

Pull Requests

0

Stars

12

Forks

5

Maintainer

Waldir Leoncio

Last Published

November 13th, 2020

Functions in DIscBIO (1.1.0)

DISCBIO

The DISCBIO Class
DEGanalysis

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

ClustDiffGenes
FindOutliers

Inference of outlier cells
FinalPreprocessing

Final Preprocessing
ClassVectoringDT

Generating a class vector to be used for the decision tree analysis.
DISCBIO2SingleCellExperiment

Convert a DISCBIO object to a SingleCellExperiment.
DEGanalysis2clust

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

Clustering of single-cell transcriptome data
Exprmclust

Performing Model-based clustering on expression values
KmeanOrder

Pseudo-time ordering based on k-means clusters
Jaccard

Jaccard<U+2019>s similarity
J48DTeval

Evaluating the performance of the J48 decision tree.
HumanMouseGeneIds

Human and Mouse Gene Identifiers.
check.format

Check format
as.DISCBIO

Convert Single Cell Data Objects to DISCBIO.
plotSymbolstSNE

tSNE map for K-means clustering with symbols
plotSilhouette

Silhouette Plot for K-means clustering
samr.estimate.depth

Estimate sequencing depths
valuesG1msTest

Single-cells data from a myxoid liposarcoma cell line
Networking

Plotting the network.
J48DT

J48 Decision Tree
PlotmclustMB

Plotting the Model-based clusters in PCA.
NetAnalysis

Networking analysis.
RpartDT

RPART Decision Tree
reformatSiggenes

Reformat Siggenes Table
replaceDecimals

Replace Decimals
Normalizedata

Normalizing and filtering
customConvertFeats

Automatic Feature Id Conversion.
RpartEVAL

Evaluating the performance of the RPART Decision Tree.
PCAplotSymbols

Plot PCA symbols
foldchange.seq.twoclass.unpaired

Foldchange of twoclass unpaired sequencing data
pseudoTimeOrdering

Pseudo-time ordering
rankcols

Rank columns
plottSNE

tSNE map
VolcanoPlot

Volcano Plot
resa

Resampling
prepExampleDataset

Prepare Example Dataset
sammy

Significance analysis of microarrays
clustheatmap

Plotting clusters in a heatmap representation of the cell distances
NoiseFiltering

Noise Filtering
plotLabelstSNE

tSNE map with labels
comptSNE

Computing tSNE
PPI

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

Plotting pseudo-time ordering or gene expression in Model-based clustering in PCA
wilcoxon.unpaired.seq.func

Twoclass Wilcoxon statistics
plotOrderTsne

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

Plotting Gap Statistics
plotExptSNE

Highlighting gene expression in the t-SNE map