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RaceID (version 0.4.0)

Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Description

Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) ). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) ).

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Version

Install

install.packages('RaceID')

Monthly Downloads

278

Version

0.4.0

License

GPL-3

Maintainer

Dominic Grc3<bc>n

Last Published

February 23rd, 2026

Functions in RaceID (0.4.0)

Function for computing local gene expression variability

Function for calculating the total variance fit

Extract Cells on Differentiation Trajectory

Cell cycle markers for Mus Muscuus

Function for computing local gene expression averages

Function for pruning k-nearest neighborhoods based on neighborhood overlap

Plotting a Heatmap of the Distance Matrix

Clustering of single-cell transcriptome data

Inference of differentially expressed genes in a cluster

Function for fitting a negative binomial noise model of technical and biological variability across cells in pruned k-nearest neighbourhoods.

Computation of a two dimensional t-SNE representation

Computing a distance matrix for cell type inference

createKnnMatrix

Function to create a knn matrix

Compute transcriptome entropy of each cell

Computation of a two dimensional umap representation

Function for regressing out the mean-variance dependence. This function corrects for the systematic dependence of the variance on the mean by a local regression.

Computes Medoids from a Clustering Partition

Computation of a two dimensional Fruchterman-Rheingold representation

Compute StemID2 score

Computing P-values for Link Significance

Function for filtering count data

diffNoisyGenesTB

Function for extracting genes with differential biological variability in a cluster

Function for extracting genes with elevated variability in a cluster

fitLogVarLogMean

Second order polynomial fit of mean-variance dependence This function corrects for the systematic dependence of the variance on the mean by a local regression.

Fitting a Gamma distribution to global cell-to-cell variability

Extract all genes for a module in a FateID self-orgaizing map

getFilteredCounts

Function for filtering count data

Function for computing a background model of gene expression variability

Inference of outlier cells and final clustering

Function for fitting a negative binomial noise model of technical and biological variability

Function for infering clustering of the pruned k nearest neighbour graph

Function for fitting a negative binomial noise model of technical and biological variability

Compute Expression Differences between Clusters

Function for differential expression analysis

Imputed expression matrix

Function for extracting a filtered expression matrix from a RaceID SCseq object

Dotplot of gene expression across clusters or samples

Inference of a Lineage Graph

Extracting filtered expression data

intestinalDataSmall

Single-cell transcriptome data of intestinal epithelial cells

Function for computing a fit to the baseline of gene expression variability

Extract Projections of all Cells from a Cluster

Function for extracting genes maximal variability

Single-cell transcriptome data of intestinal epithelial cells

Function for plotting the variance of Pearson residuals

Scatter plot of two noise-related quantaties of local pruned k-nearest neighbourhoods

Function for inspecting pruned k-nearest neighbourhoods

Plot of Mean-Variance dependence and various fits

Noise-expression scatter plot

Barplot of differentially expressed genes

Plot Background Model

Function for plotting the baseline model of gene expression variability

Function for plotting differentially variable genes

Function for plotting transition probabilities between clusters

Function for plottinhg the background model of gene expression variability

Function to plot the selected number of principal components

Plotting noise-related quantaties of local pruned k-nearest neighbourhoods

Function for plotting negative binomial regression

Boxplots for features across clusters

Highlighting feature values in a dimensional reduction representation

plotdiffgenesnb

Function for plotting differentially expressed genes

maxNoisyGenesTB

Function for extracting genes maximal variability

Plotting noise dependence on total UMI count

Plotting the Saturation of Explained Variance

Plot labels in a dimensional reduction representation

Plot Jaccard Similarities

Compute Cell Projections for Randomized Background Distribution

Compute transcriptome entropy of each cell

plotsensitivity

Plot Sensitivity

Highlighting gene expression in a dimensional reduction representation

Plot Cluster Silhouette

plotdistanceratio

Histogram of Cell-to-Cell Distances in Real versus Embedded Space

Posterior check of the model

transitionProbs

Function for the computation of transition probabilities between clusters

Extract pseudo-time order of cells along a trajectory

Noise-related quantaties of local pruned k-nearest neighbourhoods

plotoutlierprobs

Plot Outlier Probabilities

violinMarkerPlot

Violin plot of marker gene expression or noise

Plotting pseudo-time in dimensional reduction representation

Plotting function for posterior checks

Prior function for maximum a posterior inference

Plot Saturation of Within-Cluster Dispersion

Posterior probability

Function for updating a RaceID SCseq object with VarID results

Enrichment of cells on inter-cluster links

plotmarkergenes

Plotting a Heatmap of Marker Gene Expression

Function inferring a pruned knn matrix

Plotting a dimensional reduction representation

StemID2 Lineage Graph

Heatmap of Link P-values

Linear Regression of Sources of Variability

Heatmap of Link Scores

Minimum Spanning Tree of RaceID3 clusters

Plotting groups as different symbols in a dimensional reduction representation

rcpp_hello_world

Simple function using Rcpp

Random Forests-based Reclassification

Baseline gene expression variability

The Ltree Class

Gene Expression Barplot

Function for calculating an aggregated dispersion parameter

Differential Gene Expression between Links

Dimensional Reduction by PCA or ICA

The SCseq Class

Function for calculating total variance from VarID fit

Converting a Seurat object to a RaceID/VarID object

Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data