RunParallelICP | R Documentation |
This functions runs in parallel L
ICP runs, which is the computational
bottleneck of ILoReg. With ~ 3,000 cells this step should be completed
in ~ 2 h and ~ 1 h with 3 and 12 logical processors (threads), respectively.
RunParallelICP.SingleCellExperiment( object, k, d, L, r, C, reg.type, max.iter, threads, icp.batch.size ) ## S4 method for signature 'SingleCellExperiment' RunParallelICP( object, k = 15, d = 0.3, L = 200, r = 5, C = 0.3, reg.type = "L1", max.iter = 200, threads = 0, icp.batch.size = Inf )
object |
An object of |
k |
A positive integer greater or equal to |
d |
A numeric greater than |
L |
A positive integer greater than |
r |
A positive integer that denotes the number of reiterations
performed until the ICP algorithm stops.
Increasing recommended with a significantly larger sample size
(tens of thousands of cells). Default is |
C |
A positive real number denoting the cost of constraints violation in
the L1-regularized logistic regression model from the LIBLINEAR library.
Decreasing leads to more stringent feature selection, i.e. less genes are
selected that are used to build the projection classifier. Decreasing to a
very low value (~ |
reg.type |
"L1" or "L2". L2-regularization was not investigated in the manuscript, but it leads to a more conventional outcome (less subpopulations). Default is "L1". |
max.iter |
A positive integer that denotes
the maximum number of iterations performed until ICP stops. This parameter
is only useful in situations where ICP converges extremely slowly, preventing
the algorithm to run too long. In most cases, reaching
the number of reiterations ( |
threads |
A positive integer that specifies how many logical processors
(threads) to use in parallel computation.
Set |
icp.batch.size |
A positive integer that specifies how many cells
to randomly select for each ICP run from the complete data set.
This is a new feature intended to speed up the process
with larger data sets. Default is |
an object of SingleCellExperiment
class
library(SingleCellExperiment) sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500)) sce <- PrepareILoReg(sce) ## These settings are just to accelerate the example, use the defaults. sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,r=1,k=5)
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