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DIscBIO (version 1.2.1)

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

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Install

install.packages('DIscBIO')

Monthly Downloads

238

Version

1.2.1

License

MIT + file LICENSE

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Repository

https://github.com/ocbe-uio/DIscBIO

Maintainer

Waldir Leoncio

Last Published

October 9th, 2023

Functions in DIscBIO (1.2.1)

HumanMouseGeneIds

Human and Mouse Gene Identifiers.

Plot PCA symbols

J48 Decision Tree

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

tSNE map with labels

Convert Single Cell Data Objects to DISCBIO.

prepExampleDataset

Prepare Example Dataset

Evaluating the performance of the RPART Decision Tree.

Plotting the Model-based clusters in PCA.

pseudoTimeOrdering

Pseudo-time ordering

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

RPART Decision Tree

reformatSiggenes

Reformat Siggenes Table

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

foldchange.seq.twoclass.unpaired

Foldchange of twoclass unpaired sequencing data

customConvertFeats

Automatic Feature Id Conversion.

Plotting clusters in a heatmap representation of the cell distances

samr.estimate.depth

Estimate sequencing depths

Significance analysis of microarrays

Plotting Gap Statistics

Single-cells data from a myxoid liposarcoma cell line

Highlighting gene expression in the t-SNE map

replaceDecimals

Replace Decimals

plotSymbolstSNE

tSNE map for K-means clustering with symbols

Silhouette Plot for K-means clustering

wilcoxon.unpaired.seq.func

Twoclass Wilcoxon statistics

The DISCBIO Class

Clustering of single-cell transcriptome data

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

DISCBIO2SingleCellExperiment

Convert a DISCBIO object to a SingleCellExperiment.

DEGanalysis2clust

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

FinalPreprocessing

Final Preprocessing

Performing Model-based clustering on expression values

Inference of outlier cells

ClassVectoringDT

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

Normalizing and filtering

Networking analysis.

Pseudo-time ordering based on k-means clusters

Evaluating the performance of the J48 decision tree.

Jaccard’s similarity

Plotting the network.

Noise Filtering