Package: scLANE 1.3.0

Jack R. Leary

scLANE: Model Gene Expression Dynamics with Spline-Based NB GLMs, GEEs, & GLMMs

Our scLANE model uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. The modeling architectures currently supported are Negative-binomial GLMs, GEEs, & GLMMs. Downstream analysis functionalities include model comparison, dynamic gene clustering, smoothed counts generation, gene set enrichment testing, & visualization.

Authors:Jack R. Leary [aut, cre], Rhonda Bacher [ctb, fnd]

scLANE_1.3.0.tar.gz
scLANE_1.3.0.zip(r-4.7)scLANE_1.3.0.zip(r-4.6)scLANE_1.3.0.zip(r-4.5)
scLANE_1.3.0.tgz(r-4.6-x86_64)scLANE_1.3.0.tgz(r-4.6-arm64)scLANE_1.3.0.tgz(r-4.5-x86_64)scLANE_1.3.0.tgz(r-4.5-arm64)
scLANE_1.3.0.tar.gz(r-4.7-arm64)scLANE_1.3.0.tar.gz(r-4.7-x86_64)scLANE_1.3.0.tar.gz(r-4.6-arm64)scLANE_1.3.0.tar.gz(r-4.6-x86_64)
scLANE_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
scLANE/json (API)
NEWS

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

Bug tracker:https://github.com/jr-leary7/sclane/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On BioConductor:scLANE-1.3.0(bioc 3.24)scLANE-1.2.0(bioc 3.23)

rnaseqsoftwareclusteringtimecoursesequencingregressionsinglecellvisualizationgeneexpressiontranscriptomicsgenesetenrichmentdifferentialexpressiondifferential-expressionestimating-equationsgenomicsmixed-modelspseudotimerna-velocityscrna-seqsingle-celltrajectory-inferencecpp

6.83 score 16 stars 38 scripts 224 downloads 27 exports 103 dependencies

Last updated from:cebd511287. Checks:1 WARNING, 13 OK. Indexed: yes.

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source / vignettesOK382
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linux-release-x86_64OK655
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Exports:bootstrapRandomEffectschooseCandidateGenesclusterGenescreateCellOffsetembedGenesenrichDynamicGenesextractBreakpointsfitGLMMgeneProgramDriversgeneProgramScoringgeneProgramSignificancegetFittedValuesgetKnotDistgetResultsDEmarge2nbGAMnpConvolveplotClusteredGenesplotModelCoefsplotModelssmoothedCountsMatrixsortGenesHeatmapsortObservationssummarizeModeltestDynamictestSlopetheme_scLANE

Dependencies:backportsbigassertrbigparallelrbigstatsrbitbootbroombroom.mixedbstclicodacodetoolscolorspacecowplotcpp11DerivdigestdoBydoParalleldoSNOWdplyrfarverffflockforcatsforeachforecastfracdifffurrrfuturegamlssgamlss.datagamlss.distgbmgeeMgenericsggplot2glm2glmmTMBglmnetglobalsgluegtableisobanditeratorslabelinglatticelifecyclelistenvlme4lmtestmagrittrMASSMatrixmgcvmicrobenchmarkminqamodelrmpathnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigpspsclpurrrR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasRhpcBLASctlrlangrmiorpartRSpectraS7sandwichscalesshapesnowstringistringrsurvivaltibbletidyrtidyselecttimeDateTMBurcautf8vctrsviridisLiteWeightSVMwithrzoo

Interpretable Trajectory DE Testing

Jack R. Leary

Rendered fromscLANE.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-10-04
Started: 2023-10-19

Readme and manuals

Help Manual

Help pageTopics
Backward selection function for MARGE - uses the Wald information criterion (WIC).backward_sel_WIC
Bias-correct the GEE sandwich variance-covariance matrix.biasCorrectGEE
Generate bootstrapped confidence intervals for random effects.bootstrapRandomEffects
Choose candidate genes for trajectory DE analysis.chooseCandidateGenes
Cluster the fitted values from a set of 'scLANE' models.clusterGenes
Create an offset vector before modeling.createCellOffset
A helper function to create a dataframe of breakpoints and associated _p_-values from a 'marge' model.createSlopeTestData
Generate PCA & UMAP embeddings of fitted gene dynamics.embedGenes
Perform GSEA on dynamic genes identified by 'scLANE'.enrichDynamicGenes
Identify breakpoints in a 'marge' model.extractBreakpoints
Build an NB GLMM using truncated power basis functions.fitGLMM
Identify driver genes for a given gene program.geneProgramDrivers
Add per-cell module scores for gene programs.geneProgramScoring
Test significance of gene program enrichment across a trajectory.geneProgramSignificance
Generate a table of fitted values and celltype metadata for genes of interest.getFittedValues
Pull the set of knots for dynamic genes across each lineage.getKnotDist
Tidy the results of 'testDynamic'.getResultsDE
Fit 'MARGE' models of single cell counts.marge2
Truncates the predictor variable value to exclude extreme values in knots selection.max_span
A truncation function applied on the predictor variable for knot selection.min_span
Perform a likelihood ratio test for one model against another.modelLRT
Fit a negative-binomial GAM.nbGAM
Convolution that matches 'np.convolve'.npConvolve
Generate tidy results from 'clusterGenes' to use in plotting.plotClusteredGenes
Plot gene dynamics with estimated coefficients.plotModelCoefs
Plot results of 'marge' and other models using 'ggplot2'.plotModels
Print method for summary.scLANE objects.print.summary.scLANE
Generate a summary of the MARGE model.pullMARGESummary
Generate a summary of the null model.pullNullSummary
An object of class 'scLANE'.scLANE_models
Given estimates from the null model fit and the design matrix for alternative model, find the score statistic (this is used for GEEs only).score_fun_gee
Given estimates from the null model fit and the design matrix for alternative model, find the score statistic (this is used for GLMs only).score_fun_glm
Use a Lagrange multiplier (score) test to compare nested GEE models.scoreTestGEE
A 'SingleCellExperiment' object containing simulated counts.sim_counts
A data.frame containing ground-truth pseudotime.sim_pseudotime
Generate a smoothed matrix of gene expression using 'scLANE' models.smoothedCountsMatrix
Sort genes by where their peak expression occurs across pseudotime.sortGenesHeatmap
Sort observations by sample ID and pseudotime.sortObservations
Fits a linear regression model and calculates RSS/GCV measures (used for MARS linear models).stat_out
Calculate part of the score statistic for a GEE.stat_out_score_gee_null
Calculate part of the score statistic for a GLM.stat_out_score_glm_null
Make GLM objects much smaller.stripGLM
Represent a 'marge' model as a series of piecewise equations.summarizeModel
Summary method for scLANE objects.summary.scLANE
Test whether a gene is dynamic over pseudotime.testDynamic
Test whether a gene is dynamic over a pseudotime interval.testSlope
A 'ggplot2' theme for 'scLANE'.theme_scLANE
Truncated p-th power function (positive part).tp1
Truncated p-th power function (negative part).tp2
Use a Wald test to compare nested GEE models.waldTestGEE