findDAR | R Documentation |
This function takes a snap object and finds differentially accessible regions (DARs) that define clusters.
findDAR(obj, input.mat, cluster.pos, cluster.neg, cluster.neg.method, bcv, fdr, pvalue, test.method, seed.use)
obj |
A snap object. |
input.mat |
Matrix to use for finding differential features c("bmat", "pmat", "gmat"). |
cluster.pos |
Cluster to identify DAR markers. |
cluster.neg |
Cluster used as negative control compare with cluster.pos [NULL]. If cluster.neg is NULL, runFindDARs will automatically identifies background cells by finding those that are closest to cluster.pos cells as a local background. |
cluster.neg.method |
Method to find negative control cells if cluster.neg==NULL. ["knn", "random"] |
bcv |
Biological coefficient of variation. Typical values for the common BCV (square-rootdispersion) for datasets arising from well-controlled experiments are 0.4 for human data, 0.1 for data on genetically identical model organisms or 0.01 for technical replicates. |
test.method |
Test method for differential analysis c("exactTest", "LRT", "QLF"). |
seed.use |
Random seeds. |
data(demo.sp); idy = findDAR( obj=demo.sp, input.mat="pmat", cluster.pos=1, bcv=0.1, test.method="exactTest", seed.use=10 );
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