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RaceID (version 0.3.5)

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

0.3.5

License

GPL-3

Maintainer

Dominic Grc3<bc>n

Last Published

April 3rd, 2024

Functions in RaceID (0.3.5)

Computation of a two dimensional t-SNE representation

Function for computing local gene expression averages

createKnnMatrix

Function to create a knn matrix

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.

Computing a distance matrix for cell type inference

Compute transcriptome entropy of each cell

Computes Medoids from a Clustering Partition

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.

Computation of a two dimensional Fruchterman-Rheingold representation

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

Function for extracting genes with elevated variability in a cluster

Computation of a two dimensional umap representation

diffNoisyGenesTB

Function for extracting genes with differential biological variability in a cluster

Computing P-values for Link Significance

Dotplot of gene expression across clusters or samples

Compute StemID2 score

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

Extracting filtered expression data

Function for filtering count data

Extract Projections of all Cells from a Cluster

Function for computing a background model of gene expression variability

Function for plottinhg the background model of gene expression variability

Function for plotting differentially variable genes

Inference of a Lineage Graph

Inference of outlier cells and final clustering

getFilteredCounts

Function for filtering count data

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

Compute Expression Differences between Clusters

Imputed expression matrix

intestinalDataSmall

Single-cell transcriptome data of intestinal epithelial cells

Function for infering clustering of the pruned k nearest neighbour graph

Function for differential expression analysis

plotdistanceratio

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

Plotting pseudo-time in dimensional reduction representation

Function for plotting the variance of Pearson residuals

Plotting function for posterior checks

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

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

Barplot of differentially expressed genes

Plot Jaccard Similarities

Plot Background Model

Plot Saturation of Within-Cluster Dispersion

Function for inspecting pruned k-nearest neighbourhoods

plotoutlierprobs

Plot Outlier Probabilities

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

plotdiffgenesnb

Function for plotting differentially expressed genes

Highlighting gene expression in a dimensional reduction representation

Plotting the Saturation of Explained Variance

Single-cell transcriptome data of intestinal epithelial cells

maxNoisyGenesTB

Function for extracting genes maximal variability

Function for plotting negative binomial regression

plotsensitivity

Plot Sensitivity

Plot of Mean-Variance dependence and various fits

Noise-expression scatter plot

Function for extracting genes maximal variability

Plotting a dimensional reduction representation

Plot Cluster Silhouette

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

Plot labels in a dimensional reduction representation

Extract pseudo-time order of cells along a trajectory

Noise-related quantaties of local pruned k-nearest neighbourhoods

Function inferring a pruned knn matrix

Function for plotting transition probabilities between clusters

Plotting noise dependence on total UMI count

plotmarkergenes

Plotting a Heatmap of Marker Gene Expression

Posterior probability

Enrichment of cells on inter-cluster links

rcpp_hello_world

Simple function using Rcpp

Compute transcriptome entropy of each cell

Compute Cell Projections for Randomized Background Distribution

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

Boxplots for features across clusters

Function to plot the selected number of principal components

Prior function for maximum a posterior inference

Function for updating a RaceID SCseq object with VarID results

StemID2 Lineage Graph

Heatmap of Link Scores

Function for plotting the baseline model of gene expression variability

Linear Regression of Sources of Variability

Highlighting feature values in a dimensional reduction representation

Random Forests-based Reclassification

Heatmap of Link P-values

violinMarkerPlot

Violin plot of marker gene expression or noise

Minimum Spanning Tree of RaceID3 clusters

transitionProbs

Function for the computation of transition probabilities between clusters

Plotting groups as different symbols in a dimensional reduction representation

Posterior check of the model

The SCseq Class

The Ltree Class

Baseline gene expression variability

Converting a Seurat object to a RaceID/VarID object

Dimensional Reduction by PCA or ICA

Function for calculating total variance from VarID fit

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

Gene Expression Barplot

Differential Gene Expression between Links

Function for calculating an aggregated dispersion parameter

Extract Cells on Differentiation Trajectory

Clustering of single-cell transcriptome data

Function for computing local gene expression variability

Inference of differentially expressed genes in a cluster

Function for pruning k-nearest neighborhoods based on neighborhood overlap

Function for calculating the total variance fit

Plotting a Heatmap of the Distance Matrix

Cell cycle markers for Mus Muscuus

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