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DIscBIO (version 1.2.2)

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.packages('DIscBIO')

Monthly Downloads

238

Version

1.2.2

License

MIT + file LICENSE

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Repository

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

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Last Published

November 6th, 2023

Functions in DIscBIO (1.2.2)

Noise Filtering

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

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

foldchange.seq.twoclass.unpaired

Foldchange of twoclass unpaired sequencing data

RPART Decision Tree

Plotting the Model-based clusters in PCA.

customConvertFeats

Automatic Feature Id Conversion.

Evaluating the performance of the RPART Decision Tree.

tSNE map with labels

pseudoTimeOrdering

Pseudo-time ordering

Convert Single Cell Data Objects to DISCBIO.

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

Silhouette Plot for K-means clustering

Plotting clusters in a heatmap representation of the cell distances

Highlighting gene expression in the t-SNE map

Plotting Gap Statistics

reformatSiggenes

Reformat Siggenes Table

prepExampleDataset

Prepare Example Dataset

replaceDecimals

Replace Decimals

Significance analysis of microarrays

plotSymbolstSNE

tSNE map for K-means clustering with symbols

samr.estimate.depth

Estimate sequencing depths

Single-cells data from a myxoid liposarcoma cell line

wilcoxon.unpaired.seq.func

Twoclass Wilcoxon statistics

Clustering of single-cell transcriptome data

Inference of outlier cells

FinalPreprocessing

Final Preprocessing

The DISCBIO Class

DEGanalysis2clust

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

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

Plot PCA symbols

Normalizing and filtering

Plotting the network.

ClassVectoringDT

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

Networking analysis.

Performing Model-based clustering on expression values

Jaccard’s similarity

Evaluating the performance of the J48 decision tree.

Pseudo-time ordering based on k-means clusters

DISCBIO2SingleCellExperiment

Convert a DISCBIO object to a SingleCellExperiment.

HumanMouseGeneIds

Human and Mouse Gene Identifiers.

J48 Decision Tree