A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics
Convert Single Cell Data Objects to DISCBIO.
Check format
Generating a class vector to be used for the decision tree analysis.
ClustDiffGenes
Clustering of single-cell transcriptome data
Plotting clusters in a heatmap representation of the cell distances
Computing tSNE
Automatic Feature Id Conversion.
Determining differentially expressed genes (DEGs) between all individu...
Determining differentially expressed genes (DEGs) between two particul...
The DISCBIO Class
Convert a DISCBIO object to a SingleCellExperiment.
Performing Model-based clustering on expression values
Final Preprocessing
Inference of outlier cells
Foldchange of twoclass unpaired sequencing data
J48 Decision Tree
Evaluating the performance of the J48 decision tree.
Jaccard’s similarity
Pseudo-time ordering based on k-means clusters
Networking analysis.
Plotting the network.
Noise Filtering
Normalizing and filtering
Plot PCA symbols
Highlighting gene expression in the t-SNE map
Plotting Gap Statistics
tSNE map with labels
Plotting pseudo-time ordering or gene expression in Model-based cluste...
Plotting the Model-based clusters in PCA.
Plotting the pseudo-time ordering in the t-SNE map
Silhouette Plot for K-means clustering
tSNE map for K-means clustering with symbols
tSNE map
Defining protein-protein interactions (PPI) over a list of genes,
Prepare Example Dataset
Pseudo-time ordering
Rank columns
Reformat Siggenes Table
Replace Decimals
Resampling
Retries a URL
RPART Decision Tree
Evaluating the performance of the RPART Decision Tree.
Significance analysis of microarrays
Estimate sequencing depths
Volcano Plot
Twoclass Wilcoxon statistics
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>.