Package: scMaSigPro 0.6.0

scMaSigPro: Differential Expression Analysis Along Single-Cell Trajectories

Identifies genes whose expression changes along single-cell pseudotime and differs between the branching paths of a trajectory. Cells are pseudo-bulked into pseudotime bins that preserve their ordering, and the polynomial generalised linear model of the 'maSigPro' package is fitted to the binned counts with a negative binomial family and size-factor offsets. Methods are described in Srivastava et al. (2024) <doi:10.1093/bioinformatics/btae443>.

Authors:Ana Conesa [aut], María José Nueda [aut], Priyansh Srivastava [aut, cre], Marta Benegas Coll [aut], European Union [fnd]

scMaSigPro_0.6.0.tar.gz
scMaSigPro_0.6.0.zip(r-4.7-any)scMaSigPro_0.6.0.zip(r-4.6-any)
scMaSigPro_0.6.0.tgz(r-4.6-any)
scMaSigPro_0.6.0.tar.gz(r-4.7-any)scMaSigPro_0.6.0.tar.gz(r-4.6-any)
scMaSigPro_0.6.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scMaSigPro/json (API)

# Install 'scMaSigPro' in R:
install.packages('scMaSigPro', repos = c('https://spriyansh.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/biobam/scmasigpro/issues

Pkgdown/docs site:https://spriyansh.github.io

Datasets:

On CRAN:

Conda:

clusteringregressiontimecoursedifferentialexpressiongeneexpressionrnaseqsequencingsoftwaresinglecelltranscriptomicsmultiplecomparisonvisualizationpseudotimesingle-cell-rna-seqtime-seriestrajectory-analysis

4.35 score 37 scripts 41 exports 44 dependencies

Last updated from:f7b543ea60. Checks:7 OK, 2 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK410
source / vignettesOK538
linux-releaseOK487
macos-releaseOK510
macos-oldrelFAIL168
windows-develOK340
windows-releaseOK486
windows-oldrelFAIL68
wasm-releaseOK251

Exports:as_scmpassign_pathscDensecDense<-create_scmpcSparsecSparse<-eDenseeDense<-eSparseeSparse<-pathAssignpathAssign<-paths_from_graphpb_countsplotBinTileplotDiagnosticsplotIntersectplotTrendplotTrendClusterpredictorspredictors<-queryCoeffsc.cluster.trendsc.filtersc.p.vectorsc.restructsc.set.polysc.squeezesc.t.fitscmpResultsscmpToRowDatashowCoeffshowGroupCoeffshowInflushowParamsshowPolyshowSigProfshowSolshowTSupdateObject

Dependencies:abindBHBiobaseBiocGenericsBiocParallelclicodetoolscpp11DelayedArrayfarverformatRfutile.loggerfutile.optionsgenericsGenomicRangesggplot2gluegtableIRangesisobandlabelinglambda.rlatticelifecycleMASSMatrixMatrixGenericsmatrixStatsR6RColorBrewerrlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentsnowSparseArraySummarizedExperimentvctrsviridisLitewithrXVector

Frequently asked questions
Input | What does scMaSigPro need from a trajectory? | How do I analyse a Monocle3 trajectory? | What about cells that belong to several paths? | Raw or normalised counts? | How many cells and bins are enough? | The paths have different lengths. Does that matter? | Settings | Which bin rule should I use? | Which test should I use for the full model, chi-squared or F? | When should I turn the offset off? | How do I fit the genes in parallel? | Are the results reproducible? | Results | No gene passes. What now? | Why do my results differ from those of version 0.0.4 or the 2024 article? | I saved an object with an earlier version. Can I still use it? | How do I get the results into my SingleCellExperiment? | How long does an analysis take? | Citing scMaSigPro | Session information
Rendered fromfaq.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-10-27

maSigPro and the ScMaSigPro class
The ScMaSigPro object | Two resolutions and the results | Cells and bins | The design | The results | The settings | Objects from earlier versions | Equivalence with maSigPro | A Gaussian model on microarray data | A negative binomial model on counts | What scMaSigPro adds | Session information | References
Rendered frommasigpro-and-class.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-10-27

Model and tuning
The data | Bins | The model | A polynomial in pseudotime per path | The reference path | The negative binomial distribution and theta | The offset | The link | The steps and their settings | Testing each gene | Selecting the terms | Gene lists | Influential observations | Reading the coefficients | Parallel fits | The settings against the truth | Session information | References
Rendered frommodel-and-tuning.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-10-27

Quick start
Introduction | Installation | The data | The workflow | Create the object | Bin the cells along pseudotime | Set up the polynomial model | Find the genes that change | Select the significant terms | List the significant genes | Plot gene trends | Cluster the trends | Get the results | Compare with the simulated truth | Session information | References
Rendered fromscMaSigPro.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-10-27

Shared root
Root, link and end paths | The object | Each segment as a path | The root and the link in every path | By hand | Choosing the reference path | Session information | References
Rendered fromshared-root.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-10-27

Trajectory input
The input contract | From lineage weights | From slingshot | From a trajectory graph | From Monocle3 | Other tools | Session information | References
Rendered fromtrajectory-input.Rmdusingknitr::rmarkdown

Last update: 2026-10-27
Started: 2026-10-27

Readme and manuals

Help Manual

Help pageTopics
Create a ScMaSigPro object from a SingleCellExperimentas_scmp
Assign cells to paths from lineage weightsassign_paths
Bin metadata of the Dense slotcDense cDense,ScMaSigPro-method cDense<- cDense<-,ScMaSigPro-method
Create a ScMaSigPro object from counts and cell metadatacreate_scmp
Cell metadata of the Sparse slotcSparse cSparse,ScMaSigPro-method cSparse<- cSparse<-,ScMaSigPro-method
Pseudo-bulk counts of the Dense sloteDense eDense,ScMaSigPro-method eDense<- eDense<-,ScMaSigPro,character-method eDense<-,ScMaSigPro,missing-method
Cell counts of the Sparse sloteSparse eSparse,ScMaSigPro-method eSparse<- eSparse<-,ScMaSigPro,character-method eSparse<-,ScMaSigPro,missing-method
EstimatesEstimates Estimates-class
MatrixDesignMatrixDesign MatrixDesign-class
Simulated multifurcating trajectorymulti.lin.sce
ParameterConfigParameterConfig ParameterConfig-class
Path assignment matrix of the designpathAssign pathAssign,ScMaSigPro-method pathAssign<- pathAssign<-,ScMaSigPro-method
Lineage weights from a trajectory graphpaths_from_graph
Pseudo-bulk counts of the binspb_counts
Plot the sizes of the binsplotBinTile
Plot the diagnostics of the model of a geneplotDiagnostics
Plot the overlaps of the gene listsplotIntersect
Plot the trend of a geneplotTrend
Plot the trends of the clustersplotTrendCluster
Design matrix of the polynomial modelpredictors predictors,ScMaSigPro-method predictors<- predictors<-,ScMaSigPro-method
Query the coefficients of the selected modelsqueryCoeff
Cluster the significant genessc.cluster.trend
List the significant genessc.filter
Test the full model of each genesc.p.vector
Restructure the bins of paths that share a rootsc.restruct
Set up the polynomial designsc.set.poly
Bin the cells of each path along pseudotimesc.squeeze
Select the significant terms of each genesc.t.fit
ScMaSigProScMaSigPro ScMaSigPro-class
A complete analysis of splat.simscmp.ob
Per-gene results of an analysisscmpResults scmpToRowData
Results of the term selectionshowCoeff showGroupCoeff showInflu showSigProf showSol showTS
Parameters and model formula of an analysisshowParams showPoly
SignificantSignificant Significant-class
Simulated bifurcating trajectorysplat.sim
Update an ScMaSigPro object to the current class definitionupdateObject,ScMaSigPro-method
VariableProfilesVariableProfiles VariableProfiles-class