aldex.clr {ALDEx2} | R Documentation |
aldex.clr
Object
Generate Monte Carlo samples of the Dirichlet distribution for each sample. Convert each instance using a log-ratio transform. This is the input for all further analyses.
aldex.clr(reads, conds, mc.samples = 128, denom="all", verbose=FALSE, useMC=FALSE)
reads |
A |
conds |
A |
mc.samples |
The number of Monte Carlo instances to use to estimate the underlying distributions; since we are estimating central tendencies, 128 is usually sufficient, but larger numbers may be . |
denom |
An |
verbose |
Print diagnostic information while running. Useful only for debugging if fails on large datasets. |
useMC |
Use multicore by default (FALSE). Multi core processing will be attempted with the BiocParallel package. Serial processing will be used if this is not possible. In practice serial and multicore are nearly the same speed because of overhead in setting up the parallel processes. |
An explicit description of the input format for the reads
object is
shown under ‘Examples’, below.
The object produced by the clr
function contains the log-ratio transformed
values for each Monte-Carlo Dirichlet instance, which can be accessed through
getMonteCarloInstances(x)
, where x
is the clr
function output.
Each list element is named by the sample ID. getFeatures(x)
returns the
features, getSampleIDs(x)
returns sample IDs, and getFeatureNames(x)
returns the feature names.
Greg Gloor, Thom Quinn, Ruth Grace Wong, Andrew Fernandes, Matt Links and Jia Rong Wu contributed to this code.
Please use the citation given by citation(package="ALDEx")
.
aldex.ttest
,
aldex.glm
,
aldex.effect
,
selex
# The 'reads' data.frame or # RangedSummarizedExperiment object should # have row and column names that are unique, # and looks like the following: # # T1a T1b T2 T3 N1 N2 Nx # Gene_00001 0 0 2 0 0 1 0 # Gene_00002 20 8 12 5 19 26 14 # Gene_00003 3 0 2 0 0 0 1 # Gene_00004 75 84 241 149 271 257 188 # Gene_00005 10 16 4 0 4 10 10 # Gene_00006 129 126 451 223 243 149 209 # ... many more rows ... data(selex) #subset for efficiency selex <- selex[1201:1600,] conds <- c(rep("NS", 7), rep("S", 7)) x <- aldex.clr(selex, conds, mc.samples=2, denom="all", verbose=FALSE)