Package index • scMaSigPro

Input helpers

Create a ScMaSigPro object from a SingleCellExperiment or from counts and a cell table, and build the pseudotime and path labels of the cells from the output of a trajectory tool.

as_scmp(): Create a ScMaSigPro object from a SingleCellExperiment
create_scmp(): Create a ScMaSigPro object from counts and cell metadata
assign_paths(): Assign cells to paths from lineage weights
paths_from_graph(): Lineage weights from a trajectory graph

Workflow steps

Bin the cells along pseudotime, set up the polynomial model, test and fit each gene, list the significant genes and cluster their trends.

sc.squeeze(): Bin the cells of each path along pseudotime
sc.restruct(): Restructure the bins of paths that share a root
sc.set.poly(): Set up the polynomial design
sc.p.vector(): Test the full model of each gene
sc.t.fit(): Select the significant terms of each gene
sc.filter(): List the significant genes
sc.cluster.trend(): Cluster the significant genes

Binning helpers

Compute the pseudo-bulk counts of the bins.

pb_counts(): Pseudo-bulk counts of the bins

Plots

Plot the bins, the trends of genes and clusters, and the gene lists.

plotBinTile(): Plot the sizes of the bins
plotTrend(): Plot the trend of a gene
plotTrendCluster(): Plot the trends of the clusters
plotIntersect(): Plot the overlaps of the gene lists
plotDiagnostics(): Plot the diagnostics of the model of a gene

Results

Read the results of the fits and the settings of an analysis.

scmpResults() scmpToRowData(): Per-gene results of an analysis
showCoeff() showInflu() showTS() showSol() showSigProf() showGroupCoeff(): Results of the term selection
showPoly() showParams(): Parameters and model formula of an analysis
queryCoeff(): Query the coefficients of the selected models

Accessors

Read and replace the data of the cells, the bins and the design.

cSparse() `cSparse<-`(): Cell metadata of the Sparse slot
eSparse() `eSparse<-`(): Cell counts of the Sparse slot
cDense() `cDense<-`(): Bin metadata of the Dense slot
eDense() `eDense<-`(): Pseudo-bulk counts of the Dense slot
predictors() `predictors<-`(): Design matrix of the polynomial model
pathAssign() `pathAssign<-`(): Path assignment matrix of the design

Classes

The ScMaSigPro class, the classes of its slots and their update.

ScMaSigPro ScMaSigPro-class: ScMaSigPro
MatrixDesign MatrixDesign-class: MatrixDesign
VariableProfiles VariableProfiles-class: VariableProfiles
Estimates Estimates-class: Estimates
Significant Significant-class: Significant
ParameterConfig ParameterConfig-class: ParameterConfig
updateObject(<ScMaSigPro>): Update an ScMaSigPro object to the current class definition

Datasets

Data sets bundled with the package.

splat.sim: Simulated bifurcating trajectory
scmp.ob: A complete analysis of splat.sim
multi.lin.sce: Simulated multifurcating trajectory