ssc.run: Wrapper for running all the pipeline

View source: R/sscClust.R

ssc.runR Documentation

Wrapper for running all the pipeline

Description

Wrapper for running all the pipeline

Usage

ssc.run(
  obj,
  assay.name = "exprs",
  method.vgene = "HVG.sd",
  sd.n = 1500,
  mean.thre = 0.1,
  fdr.thre = 0.001,
  var.block = NULL,
  method.reduction = "iCor",
  method.clust = "kmeans",
  method.classify = "knn",
  method.tsne = "Rtsne",
  pca.npc = NULL,
  iCor.niter = 1,
  iCor.method = "spearman",
  tSNE.perplexity = 30,
  subsampling = F,
  sub.frac = 0.4,
  sub.use.proj = T,
  sub.vis.proj = F,
  k.batch = 2:6,
  refineGene = F,
  de.n = 1500,
  HSD.FC.THRESHOLD = 1,
  nIter = 1,
  out.prefix = NULL,
  parfile = NULL,
  reuse = F,
  ncore = NULL,
  seed = NULL,
  do.DE = F,
  ...
)

Arguments

obj

object of singleCellExperiment class

assay.name

character; which assay (default: "exprs")

method.vgene

character; variable gene identification method used. (default: "sd")

sd.n

integer; top number of genes as variable genes (default 1500)

mean.thre

numeric; threshold for mean, used in trendVar method (default 0.1)

fdr.thre

numeric; threshold for fdr, used in trendVar method (default 0.001)

var.block

character; specify the uninteresting factors by formula. E.g. "~patient". used in trendVar method (default NULL)

method.reduction

character; which dimention reduction method to be used, should be one of "iCor", "pca", and "none". (default: "iCor")

method.clust

character; clustering method to be used, should be one of "kmeans", "hclust", "SNN", "adpclust" and "SC3. (default: "kmeans")

method.classify

character; method used for classification, one of "knn" and "RF". (default: "knn")

method.tsne

character; method to run tsne, one of "Rtsne", "FIt-SNE". (default: "Rtsne")

pca.npc

integer; number of pc be used. Only for reduction method "pca". (default: NULL)

iCor.niter

integer; number of iteration of calculating the correlation. Used in reduction method "iCor". (default: 1)

iCor.method

character; correlation method, one of "spearman", "pearson" (default: "spearman")

tSNE.perplexity

double, perplexity parameter of tSNE. (default: 30)

subsampling

logical; whether cluster using the subsampling->cluster->classification method. (default: F)

sub.frac

numeric; subsample to frac of original samples. (default: 0.4)

sub.use.proj

logical; whether use the projected data for classification. (default: T)

sub.vis.proj

logical; whether get low dimensional representation for visualization, only used in downsample mode. (default: F)

k.batch

integer; number of clusters to be evaluated. (default: 2:6)

refineGene

logical; whether perform second round demension reduction and clustering pipeline using the differential genes found by the first round cluster result. (default: F)

de.n

integer; number of differential genes used for refined geneset for another run of clustering (default 1500)

HSD.FC.THRESHOLD

numeric; threshold for log2FoldChange, used in findDEGenesByAOV (default 1)

nIter

integer; number of iterative clustering in sub-cluster. (default: 1)

out.prefix

character; output prefix, if not NULL, some plots of intermediate result will be produced. (default: NULL)

parfile

character; parameter files, if not NULL, will use the settings. must contain a list named 'parlist'. (default: NULL)

reuse

logical; don't calculate if the query is already available. (default: F)

ncore

integer; nuber of CPU cores to use. if NULL, automatically detect the number. (default: NULL)

seed

integer; seed of random number generation. (default: NULL)

do.DE

logical; perform DE analysis when clustering finished. (default: F)

...

parameters pass to clustering methods

Details

run the pipeline of variable gene identification, dimension reduction, clustering.

Value

an object of SingleCellExperiment class with cluster labels added.

See Also

ssc.variableGene for variable genes' identification, ssc.reduceDim for dimension reduction, ssc.clust for clustering using all data and ssc.clustSubsamplingClassification for clustering with subsampling.


Japrin/sscClust documentation built on May 23, 2024, 7:12 a.m.