ssc.run | R Documentation |
Wrapper for running all the pipeline
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,
...
)
obj |
object of |
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 |
run the pipeline of variable gene identification, dimension reduction, clustering.
an object of SingleCellExperiment
class with cluster labels added.
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.
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