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RaceID (version 0.3.9)

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

1,024

Version

0.3.9

License

GPL-3

Maintainer

Dominic Grc3<bc>n

Last Published

November 24th, 2024

Functions in RaceID (0.3.9)

Converting a Seurat object to a RaceID/VarID object

Gene Expression Barplot

Computing P-values for Link Significance

Function for computing local gene expression variability

Computing a distance matrix for cell type inference

Compute StemID2 score

Compute transcriptome entropy of each cell

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

Function for extracting genes with elevated variability in a cluster

diffNoisyGenesTB

Function for extracting genes with differential biological variability in a cluster

The Ltree Class

Imputed expression matrix

Function for infering clustering of the pruned k nearest neighbour graph

Function for differential expression analysis

Compute Expression Differences between Clusters

Computation of a two dimensional t-SNE representation

Function for computing local gene expression averages

Computation of a two dimensional umap representation

Plotting a Heatmap of the Distance Matrix

Function for plottinhg the background model of gene expression variability

Clustering of single-cell transcriptome data

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.

createKnnMatrix

Function to create a knn matrix

Function for plotting differentially variable genes

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

Extracting filtered expression data

Extract Projections of all Cells from a Cluster

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

Function for plotting transition probabilities between clusters

Plotting noise dependence on total UMI count

Function for plotting the variance of Pearson residuals

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

plotoutlierprobs

Plot Outlier Probabilities

Boxplots for features across clusters

Function for calculating total variance from VarID fit

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

Function for calculating an aggregated dispersion parameter

Function for inspecting pruned k-nearest neighbourhoods

Dotplot of gene expression across clusters or samples

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.

Plot Saturation of Within-Cluster Dispersion

Function for calculating the total variance fit

Function for filtering count data

Extract pseudo-time order of cells along a trajectory

Noise-related quantaties of local pruned k-nearest neighbourhoods

getFilteredCounts

Function for filtering count data

Plotting function for posterior checks

Plotting pseudo-time in dimensional reduction representation

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

Plotting groups as different symbols in a dimensional reduction representation

Plot Jaccard Similarities

Minimum Spanning Tree of RaceID3 clusters

Plot labels in a dimensional reduction representation

violinMarkerPlot

Violin plot of marker gene expression or noise

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

Function for extracting genes maximal variability

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

rcpp_hello_world

Simple function using Rcpp

Random Forests-based Reclassification

Cell cycle markers for Mus Muscuus

Function for plotting the baseline model of gene expression variability

Function to plot the selected number of principal components

Single-cell transcriptome data of intestinal epithelial cells

maxNoisyGenesTB

Function for extracting genes maximal variability

Plotting the Saturation of Explained Variance

Plot of Mean-Variance dependence and various fits

Compute Cell Projections for Randomized Background Distribution

plotdiffgenesnb

Function for plotting differentially expressed genes

Noise-expression scatter plot

Compute transcriptome entropy of each cell

Function for plotting negative binomial regression

Highlighting feature values in a dimensional reduction representation

Computes Medoids from a Clustering Partition

Function for computing a background model of gene expression variability

StemID2 Lineage Graph

Computation of a two dimensional Fruchterman-Rheingold representation

Plotting a dimensional reduction representation

plotdistanceratio

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

Highlighting gene expression in a dimensional reduction representation

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

Inference of outlier cells and final clustering

Heatmap of Link Scores

Heatmap of Link P-values

Inference of a Lineage Graph

plotmarkergenes

Plotting a Heatmap of Marker Gene Expression

Plot Background Model

Barplot of differentially expressed genes

Enrichment of cells on inter-cluster links

Posterior check of the model

intestinalDataSmall

Single-cell transcriptome data of intestinal epithelial cells

plotsensitivity

Plot Sensitivity

Function for updating a RaceID SCseq object with VarID results

Plot Cluster Silhouette

Function inferring a pruned knn matrix

transitionProbs

Function for the computation of transition probabilities between clusters

Posterior probability

Linear Regression of Sources of Variability

Prior function for maximum a posterior inference

The SCseq Class

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

Baseline gene expression variability

Differential Gene Expression between Links

Dimensional Reduction by PCA or ICA

Function for pruning k-nearest neighborhoods based on neighborhood overlap

Extract Cells on Differentiation Trajectory

Inference of differentially expressed genes in a cluster