RunscMC: Seurat wrapper for scMC
In amsszlh/scMC: Integrating and comparing multiple single cell genomic datasets

Run scMC algorithm with Seurat pipelines

RunscMC(
  object.list,
  resolution = NULL,
  method = c("matrix", "igraph"),
  algorithm = 4,
  resRange = NULL,
  nDims.consensus = 30,
  clustering.method = c("hierarchical", "community"),
  graph.name = NULL,
  quantile.cutoff = 0.75,
  similarity.cutoff = 0.6,
  new.assay.name = NULL,
  nDims.scMC = 40,
  lambda = 1,
  integrationFeatures.method = c("joint", "individual"),
  selection.method = c("vst", "mean.var.plot"),
  nfeatures = 2000,
  mean.cutoff = c(0.01, 5),
  dispersion.cutoff = c(0.25, Inf),
  nDims.pca = 40,
  force.pca = TRUE,
  nDims.knn = 40,
  k.param = 20,
  prune.SNN = 1/15,
  features = NULL,
  test.use = "wilcox",
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25,
  add.cell.ids = NULL,
  assay = "RNA",
  ...
)

`object.list`	a list of Seurat objects, one per dataset Parameters in identifyClusters
`resolution`	the resolution in Leiden algorithm; if it is NULL, the optimal resoultion will be inferred based on eigen spectrum
`method`	Method for running leiden (defaults to matrix which is fast for small datasets). Enable method = "igraph" to avoid casting large data to a dense matrix.
`algorithm`	Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python.
`resRange`	the range of resolution values in Leiden algorithm; if it is NULL, the default range of resoultion will be from 0.1 to 0.5
`nDims.consensus`	the number of singular values to estimate from the consensus matrix.
`clustering.method`	method for performing clustering on the consensus matrix from a range of resolutions
`graph.name`	Name of graph to use for the clustering algorithm Parameters in identifyConfidentCells
`quantile.cutoff`	quantile cutoff (default = 0.75) Parameters in learnTechnicalVariation
`similarity.cutoff`	a thresholding parameter determining whether cell clusters are shared across different datasets based on their similarity. If T is too small, the biological variation may be removed. If T is too large, the technical variation could not be removed. Parameters in integrateData
`new.assay.name`	Name for the new assay containing the integrated data
`nDims.scMC`	number of dimensions to compute in the scMC integrated space
`lambda`	the tuning parameter, non-negative. Parameters in identifyIntegrationFeatures
`integrationFeatures.method`	"joint" or "individual"; "joint": Identify integration features from the concatenated data matrix; "individual": ranks features by the number of datasets they are deemed variable in, breaking ties by the median variable feature rank across datasets. It returns the top scoring features by this ranking.
`selection.method`	The method to choose top variable features: vst: First, fits a line to the relationship of log(variance) and log(mean) using local polynomial regression (loess). Then standardizes the feature values using the observed mean and expected variance (given by the fitted line). Feature variance is then calculated on the standardized values after clipping to a maximum (see clip.max parameter). mean.var.plot (mvp): First, uses a function to calculate average expression (mean.function) and dispersion (dispersion.function) for each feature. Next, divides features into num.bin (deafult 20) bins based on their average expression, and calculates z-scores for dispersion within each bin. The purpose of this is to identify variable features while controlling for the strong relationship between variability and average expression.
`nfeatures`	Number of features to select as top variable features; only used when selection.method is set to 'vst'
`mean.cutoff`	A two-length numeric vector with low- and high-cutoffs for feature means
`dispersion.cutoff`	A two-length numeric vector with low- and high-cutoffs for feature dispersions Parameters in identifyNeighbors
`nDims.pca`	the number of dimensions to use for running PCA
`force.pca`	Set force.pca = FALSE to skip the PCA calculation. Default = TRUE will calculate PCA.
`nDims.knn`	the number of dimensions to use for building SNN
`k.param`	Defines k for the k-nearest neighbor algorithm
`prune.SNN`	Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the strigency of pruning (0 — no pruning, 1 — prune everything). Parameters in identifyMarkers
`features`	features used to perform statistical test
`test.use`	which test to use
`only.pos`	Only return positive markers
`min.pct`	Threshold of the percent of cells enriched in one cluster
`logfc.threshold`	Threshold of Log Fold Change
`add.cell.ids`	A character vector of length(object.list) when merging multiple objects. Appends the corresponding values to the start of each objects' cell names.
`assay`	Assay to use
`...`	other parameter passing to Seurat functions