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RaceID (version 0.3.4)

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

427

Version

0.3.4

License

GPL-3

Maintainer

Dominic Grc3<bc>n

Last Published

January 8th, 2024

Functions in RaceID (0.3.4)

Computation of a two dimensional t-SNE representation

Function for computing local gene expression averages

Compute transcriptome entropy of each cell

Function for filtering count data

Function for pruning k-nearest neighborhoods based on neighborhood overlap

Extract Cells on Differentiation Trajectory

Computing a distance matrix for cell type inference

Computation of a two dimensional umap representation

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.

Function for differential expression analysis

Compute Expression Differences between Clusters

Cell cycle markers for Mus Muscuus

Dotplot of gene expression across clusters or samples

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

Inference of outlier cells and final clustering

maxNoisyGenesTB

Function for extracting genes maximal variability

Function for extracting genes maximal variability

Extracting filtered expression data

Function for inspecting pruned k-nearest neighbourhoods

Function for computing a background model of gene expression variability

Single-cell transcriptome data of intestinal epithelial cells

Extract Projections of all Cells from a Cluster

Function for infering clustering of the pruned k nearest neighbour graph

Function for plotting the variance of Pearson residuals

diffNoisyGenesTB

Function for extracting genes with differential biological variability in a cluster

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

Function for extracting genes with elevated variability in a cluster

Imputed expression matrix

Computing P-values for Link Significance

Compute StemID2 score

Computation of a two dimensional Fruchterman-Rheingold representation

Function for plottinhg the background model of gene expression variability

getFilteredCounts

Function for filtering count data

Computes Medoids from a Clustering Partition

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

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

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

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.

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

intestinalDataSmall

Single-cell transcriptome data of intestinal epithelial cells

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

plotsensitivity

Plot Sensitivity

Inference of a Lineage Graph

Highlighting gene expression in a dimensional reduction representation

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

Plotting pseudo-time in dimensional reduction representation

Plotting function for posterior checks

plotdistanceratio

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

Function for plotting negative binomial regression

plotdiffgenesnb

Function for plotting differentially expressed genes

Noise-related quantaties of local pruned k-nearest neighbourhoods

Heatmap of Link Scores

Barplot of differentially expressed genes

Plot Background Model

rcpp_hello_world

Simple function using Rcpp

Heatmap of Link P-values

Plot labels in a dimensional reduction representation

Extract pseudo-time order of cells along a trajectory

Plot Jaccard Similarities

Plotting a dimensional reduction representation

Plotting the Saturation of Explained Variance

plotmarkergenes

Plotting a Heatmap of Marker Gene Expression

Plotting groups as different symbols in a dimensional reduction representation

Boxplots for features across clusters

Minimum Spanning Tree of RaceID3 clusters

violinMarkerPlot

Violin plot of marker gene expression or noise

Random Forests-based Reclassification

Function for plotting transition probabilities between clusters

Posterior probability

Plotting noise dependence on total UMI count

Function for plotting the baseline model of gene expression variability

plotoutlierprobs

Plot Outlier Probabilities

Plot Saturation of Within-Cluster Dispersion

Function to plot the selected number of principal components

Noise-expression scatter plot

Function for plotting differentially variable genes

Prior function for maximum a posterior inference

Plot of Mean-Variance dependence and various fits

StemID2 Lineage Graph

Compute transcriptome entropy of each cell

Highlighting feature values in a dimensional reduction representation

Compute Cell Projections for Randomized Background Distribution

Plot Cluster Silhouette

Linear Regression of Sources of Variability

Function for updating a RaceID SCseq object with VarID results

Enrichment of cells on inter-cluster links

transitionProbs

Function for the computation of transition probabilities between clusters

Posterior check of the model

Function inferring a pruned knn matrix

Baseline gene expression variability

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

Converting a Seurat object to a RaceID/VarID object

Differential Gene Expression between Links

Function for calculating total variance from VarID fit

The SCseq Class

Gene Expression Barplot

Function for calculating an aggregated dispersion parameter

Dimensional Reduction by PCA or ICA

The Ltree Class

Function for calculating the total variance fit

Plotting a Heatmap of the Distance Matrix

Inference of differentially expressed genes in a cluster

Function for computing local gene expression variability

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

Clustering of single-cell transcriptome data