Function reference • scglmmr

All functions

AggregateCellMetadata(): AggregateCellMetadata - take cell level metadata and collapse down to sample-level metadata for use in pseudobuk testing. This can be useful if you do not already have metadata for each sample in the experiment, but these data are stored in single cell metadata. For example, metadata for the donor ID, unique sample (e.g. donorid_timepoint), age, sex etc. Note these are all individual sample level data. Single cell intrinsic variables like nUMI cannot be collapsed down, only variables that are unique for each sample which are the columns of the pseudobulk data. Only include metadata that you intend to adjust models for as covariates or random effects because all variables will be referenced during count normalization and feature filtering.

AverageSampleModuleZscore(): AverageSampleModuleZscore apply the function calc_avg_module_zscore to a pseudobulklist.

BulkDesignMatrix(): BulkDesignMatrix - designmatrix for the main experiment factor - used in normalization

CombineResults(): CombineResults - For all cell types merge the gsea leading edge genes with their contrast model coefficieint and p value from limma / dream

EnrichmentJaccard(): EnrichmentJaccard - using gsea list and LeadingEdgeIndexed result, compute pairwise jaccard index of leadingedge genes within celltypes. saves a heatmap of modules for each cell type in savpath if saveplot = TRUE. Returns a gsea result dataframe with all celltypes combined and module annotated with average within celltype jaccard index and leadingedge genes.

ExtractResult(): ExtractResult - convenience function to return statistics for downstream analysis functions such as FgseaList. Returns results from list of dream or lmFit results from those functions natively (use model.fit.list = list(fit)) or from scglmmr::RunVoomLimma and scglmmr::dreamMixedModel

FgseaList(): FgseaList - wrapper around fast gene set enrichment analysis with the fgsea R package https://bioconductor.org/packages/release/bioc/html/fgsea.html to implement on a list of ranks indexec by cell type.

FitDream(): FitDream - run mixed effects model on aggregated (summed) data using the method 'dream' by Hoffman et. al. Bioinformatics (2021) doi.org/10.1093/bioinformatics/btaa687. Fits mixed model using lme4 with REML and voom weights.

FitLmer(): FitLmer - This is a simpler version of FitLmerContrast that makes no assumptions about structure of underlying data except that it can accomodate a mixed effects model formula and that there are multiple cell types to be separately fitted. fit a single cell mixed effects linear model. Designed to fit an aggregated gene module score. Fits a model to each cell cluster to test a grouping, treatment or combination factor; returns model fits for maximum flexibility

FitLmerContrast(): FitLmerContrast - for data with pre post treatment and 2 response groups; within each cell type contrast difference in fold change between groups, baseline difference, and fold change across groups of module scores.

GetContrastResults(): GetContrastResults - return results from a contrast fit on list of celltypes from RunVoomLimma using topTable

GetContrastResultsRaw(): GetContrastResultsRaw - calculate p values and return contrast results from modelfit with dreamMixedModel

GetGeneMatrix(): GetGeneMatrix get a gene matric for plotting genes by celltypes statistic from pseudobulk model results e.g. using heatmap pheatmap or complexheatmap

GetLeadingEdgeFull(): GetLeadingEdgeFull Get a tidy dataframe of ALL Leading Edge Genes from gene set enrichment for all cell types

GetLeadingEdgeGenes(): GetLeadingEdgeGenes get the leading edge genes from a single cell type / module combination

GetRankResults(): GetRankResults get list of gene ranks by t stat for fGSEA

GetRankResultsRaw(): GetRankResultsRaw get list of gene ranks by raw t statistic for fGSEA. for results returned by dreamMixedModel

GetTidySummary(): GetTidySummary - tidy data summary for a single cell type

HeatmapDiag(): HeatmapDiag utility function for `pheatmap` using `slanter` to order the maximum values across the matrix diagonal

LeadEdgeSampleHeatmap(): LeadEdgeSampleHeatmap make a heatmap of average expression for top or leading edge genes returned by LeadEdgeTidySampleExprs

LeadEdgeTidySampleExprs(): LeadEdgeTidySampleExprs - convert a PseudobulkList into a tidy dataframe for each sample across cell types of the leading edge genes from a gsea list

LeadingEdgeIndexed(): LeadingEdgeIndexed - extract leading edge genes from FgseaList results, return a new embedded list of named modules, indexed by celltype

Normalize(): Normalize - normalize summed gene counts across samples. This is a wrapper around edgeR functions `calcNormFactors` and `filterByExpr`.

PlotFgsea(): PlotFgsea identical to GSEABubblePlot, returns plot for manual adjustment or saving and also clusters the map.

PlotHypergeometric(): PlotHypergeometric - plot results returned by RunHypergeometricTest

PseudobulkList(): PseudobulkList - make a pseudobuk summed or average dataset for each samplexcelltype

RbindGseaResultList(): RbindGseaResultList - prepare gsea result list for visualization funcitons GseaBubblePlot or GseaBarPlot; called by PlotFgseaList

RunFgseaOnRankList(): RunFgseaOnRankList - wrapper around fast gene set enrichment analysis with the fgsea R package https://bioconductor.org/packages/release/bioc/html/fgsea.html

RunHypergeometricTest(): RunHypergeometricTest - run a hypergeometric test on results returned by GetContrastResults or GetContrastResultsRaw

RunVoomLimma_covar(): RunVoomLimma_covar RunLimmaVoom with an option to adjust for a covariate within mRNA.

SCMixedPoisson(): SCMixedPoisson - Fit a gene level Poisson count mixed generalized linear model.

SubjectCelltypeTable(): SubjectCelltypeTable - quality control on sample level aggregation across cell types

TidySampleData(): TidySampleData convert data from PseudobulkList into a dataframe for each sample across cell types of the top differentially expressed genes for a contrast

WeightedCellModuleScore(): WeightedCellModuleScore - calculate the average or weighted aveage +/- scaling return dataframe of cells by average module score

calc_avg_module_zscore(): calc_avg_module_zscore - calculate average module z score of list of modules on a PseudobulkList This is equivalent to the average z score method used in in Kotliarov et. al. Nature Med 2020 zscore is calculated across both genes and samples it is adopted below to run on 'pseudobulk lists' (average "averagemetacell.list" or pseudobulk list created by PseudobulkList) this small wrapper is called by the AverageSampleModuleZscore. calculate signature score for each cell type, BTM, Subject function input = named list of modules, dataframe with subject as rows genes as columns