Function reference • scMiko

Filters and QC
`filterSeurat()`	Apply QC filters to Seurat Object
`clusterFilter()`	Filter seurat object by specified cluster ids
`cleanCluster()`	Clean clusters
`cleanFilterGenes()`	Clean and filter gene list
`findArtifactGenes()`	Identify artifact genes
`getExpressedGenes()`	Identify expressed genes
`getExpressingCells()`	Get cells that express query gene
`getMitoContent()`	Calculate mitochondrial content
`QC.scatterPlot()`	QC scatter plots
`QC.violinPlot()`	QC violin plots
Dimensional Reduction
`pcaElbow()`	Quickly estimate the 'elbow' of a scree plot (PCA)
`projectReduction()`	Project dimensionally-reduced features onto UMAP
`propVarPCA()`	Variance explained by each principal component.
`getReductionGenes()`	Get top loaded features for PCA or ICA dimensional reduction.
`runRPCA()`	Run Robust Prinicipal Component Analysis
Clustering
`setResolution()`	Set cluster resolution
`multiCluster()`	Cluster seurat object at several resolutions
`multiSpecificity()`	Evaluate specificity of single-cell markers across several cluster resolutions.
`multiSilhouette()`	Evaluate silhouette indices of clustered single cell data across several cluster resolutions.
Cell-Type Annotation Module detection
`mikoScore()`	Calculate Miko module scores for feature expression programs
`nullScore()`	Null distribution for standardized module scores.
`sigScore()`	Calculate Miko score significance
Gene Set Functions
`AddSModuleScore()`	Calculate standardized module scores for feature expression programs in single cells.
`aggGroupExpression()`	Get summary of group expression in Seurat object
`avgGroupExpression()`	Get summary of group expression in Seurat object
`coherentFraction()`	Calculate coherent fraction for feature expression program.
`consolidateNMF()`	Consolidate several NMF reduction objects into single NMF reduction object within Seurat object.
`getJaccard()`	Jaccard Similarity
`id2geneset()`	Get Reactome/GO geneset
`id2term()`	Map Reactome/GO ID to term
`jaccardSimilarityMatrix()`	Jaccard Similarity Matrix
`optimalBinSize()`	Identify optimal bin size for AddModuleScore() function
`runAUC()`	Run AUCell classification
`runMS()`	Run Modular Scoring
`summarizeHG()`	Summarize hypergeometric enrichment results
`{`()	Pathway annotations from Bader Lab
`getAnnotationPathways()`	Returns list of annotations for given Entrez gene IDs
`runGSEA()`	Run gene-set enrichment analysis (GSEA)
`runHG()`	Run hypergeometric gene enrichment analysis.
`searchAnnotations()`	Search Reactome/GO databases for terms that match query
`signatureCoherence()`	Evaluate signature coherence.
`term2id()`	Map Reactome/GO term to ID
`upset.Plot()`	Upset plot
Expression Functions
`findCDIMarkers()`	Calculate feature co-dependency index
`findCorMarkers()`	Calculate spearman correlations between features.
`findGiniMarkers()`	Calculate Gini marker specificity
`getDEG()`	Get differentially expressed genes
`getExpressionMatrix()`	Get expression matrix from Seurat Object
`miko_volcano()`	Draw volcano plot to visualize differential expression.
`parCor()`	Parallelized correlation
`pseudotimeRF()`	Identify pseudotime-dependent genes using Random Forest (RF) Model
Variance Decomposition Variance decomposition analyses
`vd_Formula()`	Specify model formula for variance decomposition.
`vd_Inputs()`	Specify inputs for variance decomposition analysis
`vd_Run()`	Perform Variance Decomposition Analysis
`vd_UMAP()`	Generate UMAPs with each variance decomposition covariate overlaid.
Scale-Free Shared Nearest Neighbor Network Analysis (SSN) SSN Module detection
`SSNConnectivity()`	SSN connectivity plot
`SSNResolution()`	Identify optimal cluster resolution of scale-free shared nearest neighbor network (SNN)
`pruneSSN()`	Identify and (optionally) prune gene program features in scale-free shared nearest neighbor network (SSN)
`runSSN()`	Perform gene program discovery using SNN analysis
`SSNExpression()`	SSN connectivity plot
`findNetworkFeatures()`	Identify features for gene program discovery.
`getModuleGenes()`	Get list of module genes
`scaleFreeNet()`	Apply scale-free topology transform to shared-nearest neighbor graph
`summarizeModules()`	Summarize SSN, ICA or NMF gene program/module analyses
Independent Component Analysis ICA Module detection
`getICAGenes()`	Get significant ICA genes
`runICA()`	Run Independent Component Analysis on gene expression
Non-Negative Matrix Factorization NMF Module detection
`getNMFGenes()`	Returns top module genes from NMF feature loading matrix
`runNMF()`	Perform non-negative matrix factorization (NMF)
Differential Abundance Analysis
`da_DEG()`	Identify correlated genes/pathways associated with differential abundance.
`da_Run()`	Run differential abundance analysis.
Visualization
`cluster.UMAP()`	UMAP stratified by cluster ID
`expression.Plot()`	Violin plot of single cell gene expression
`exprUMAP()`	Visualize gene expression on UMAP
`featureGradient()`	Visualize feature activity/expression gradient overlaid on UMAP
`geneRepCurve()`	Plot relationship showing percentage of cells expressing atleast percentage of genes
`geom_split_violin()`	Split violin plot using ggplot2
`getUMAP()`	Get UMAP data and plot from Seurat object.
`miko_heatmap()`	Function to draw ggplot heatmaps
`scale_color_miko()`	Gradient color scale
`scale_fill_miko()`	Gradient fill scale
`scExpression.UMAP()`	Cell-level gene expression projected on UMAP
`theme_miko()`	scMiko Theme
`variableGenes.Plot()`	Plot variable genes in Seurat Object
`highlightUMAP()`	Highlight cells on UMAP plot
Data
`geneSets`	List of scMiko gene sets
`LR.db`	Ligand-Receptor Database
Miscellaneous Utilities
`autoPointSize()`	Automatically determine optimal point size for geom_point()
`balanceMatrixSize()`	Balance matrix dimensions
`categoricalColPal()`	Generate categorical ColorBrewer palette.
`citationCheck()`	Check pubmed citations for genes
`clearGlobalEnv()`	Remove variables from global environment
`col2rowname()`	Assign column entries in data.frame to row names.
`dist2hclust()`	Hierarchially-cluster distance matrix
`fixBarcodeLabel()`	Fix barcode labels
`getClusterCenters()`	Get cluster centers
`getConnectivity()`	Gene connectivity within network.
`getDensity()`	Get local density (z) of bivariate relationship (x,y)
`getNodesEdges()`	Get nodes and edges from igraph data.frame for visNetwork
`getOrderedGroups()`	Get vector of unique ordered group names from Seurat Object
`group2list()`	Named list of cells grouped by meta feature from Seurat object
`lintersect()`	Returns intersection of all list entries.
`longDF2namedList()`	Convert long data frame to named list
`miko_message()`	Print message
`namedList2longDF()`	Convert named list to long data.frame
`namedList2wideDF()`	Convert named list to wide data.frame
`orderedFactor()`	Sort factor levels in numerical order
`pseudoReplicates()`	Create pseudo-replicates, stratified by grouping variable.
`qNorm()`	Quantile Normalization of 2 Vectors
`rescaleValues()`	Rescale values to specified range.
`rmvCSVprefix()`	Remove "ï.." prefix that is appended to csv header
`scoreGBM()`	Annotate glioblastoma (GBM) subtype based on Neftel 2019 scoring pipeline.
`snip()`	Winsorize values at lower and upper quantiles.
`sim2adj()`	Compute adjaceny matrix from similary (correlation) matrix
`sparse2dense()`	Convert sparse matrix to dense matrix
`sparse2df()`	Convert sparse matrix to data.frame
`ulength()`	Number of unique values
`value2col()`	Convert values to color gradient
`wideDF2namedList()`	Convert wide data.frame to named list
Integration Functions
`miko_integrate()`	scRNAseq integration wrapper
`runScanorama()`	Integrate scRNA-seq data using Scanorama
`runBBKNN()`	Integrate scRNA-seq data using batch-balanced KNN (BBKNN)
Seurat Functions
`balanceSamples()`	Subsample cells in seurat object to be balanced (sample-size-wise) across conditions.
`downsampleSeurat()`	Downsample single cell data
`mergeSeuratList()`	Merge list of seurat objects
`neighborPurity()`	Compute purity of each cell's neighborhood, as defined by KNN graph.
`prepSeurat()`	prep Seurat
`prepSeurat2()`	prep Seurat (Extended adaptation of prepSeurat)
`recodeBarcode()`	Recode (i.e., relabel) metadata in Seurat object
`rmDuplicateGenes()`	Remove duplicate genes from Seurat Object
`scNormScale()`	Normalize and Scale scRNAseq Data
`subsetDimRed()`	Get dimensional reduction from Seurat Object for subset of data
`uniqueFeatures()`	Get unique features from metadata column in seurat object.
`updateDimNames()`	Ensure that all dimNames are correctly specified in Seurat Object
`wnn_Components()`	Compute network component UMAPs and visualize component weights.
`wnn_Run()`	Run WNN Multi-Modal Integration.
Gene Representation
`checkGeneRep()`	Check gene representation
`detectSpecies()`	Determine species based on gene representation
`ens2sym.so()`	Convert gene ensemble to symbol in seurat object
`sym2ens()` `ensembl2sym()`	Convert gene symbol to ensembl
`entrez2sym()`	Convert entrez id to gene symbol
`firstup()`	Uppercase first letter and lowercase rest
`inferSpecies()`	Infer species from list of Ensemble ids
`prepGeneList()`	Prepare gene to ensemble conversion vector
`speciesConvert()`	Convert gene symbol representation to Hs or Mm
`sym2entrez()`	Convert gene symbol to entrez
Dashboard Utilities
`addLogEntry()`	Add entry to analysis log
`barcodeLabels()`	Subset and assign labels to seurat
`flex.asDT()`	Outputs datatable with print button options
`flex.multiTabLogs()`	Generate multi-tab analysis log list for flexdashboard
`flex.multiTabPlot()`	Generate multi-tab ggplot handle list for flexdashboard
`flex.multiTabPlotly()`	Generate multi-tab list of plotly figures for flexdashboard
`flex.multiTabTables()`	Generate multi-tab datatable list for flexdashboard
`getLoadPath()`	Return load path
`initiateLog()`	Initiate analysis log
`loadCellRanger()`	Load CellRanger preprocessed data
`loadMat()`	Load gene x cell count matrix into seurat object
`loadMoffat()`	Load preprocessed data from Moffat lab sciRNA-seq3 pipeline
`updateCentralLog()`	Update central log
Save Functions
`saveHTML()`	Save figure as html
`savePDF()`	Save figure as pdf

Reference

Filters and QC

Dimensional Reduction

Clustering

Cell-Type Annotation

Gene Set Functions

Expression Functions

Variance Decomposition

Scale-Free Shared Nearest Neighbor Network Analysis (SSN)

Independent Component Analysis

Non-Negative Matrix Factorization

Differential Abundance Analysis

Visualization

Data

Miscellaneous Utilities

Integration Functions

Seurat Functions

Gene Representation

Dashboard Utilities

Save Functions