estimate_log_fold_change {moanin} | R Documentation |
Estimates log fold change
## S4 method for signature 'Moanin' estimate_log_fold_change( object, contrasts, method = c("timecourse", "sum", "max", "timely", "abs_sum", "abs_squared_sum", "min") )
object |
An object of class |
contrasts |
The contrasts to consider |
method |
method for calculating the log-fold change. See details. |
The following methods exist for calculating the log-fold change between conditions over time (default is "timecourse"):
timely
The log-fold change for each individual timepoint
(lfc(t))
timecourse
The average absolute per-week fold-change,
multiplied by the sign of the average per-week fold-change.
sum
Sum of per-week log fold change, over all timepoints
max
Max of per-week log fold change, over all timepoints
abs_sum
Sum of the absolute value of the per-week log fold
change, over all timepoints
abs_squared_sum
Sum of the square value of the per-week log
fold change, over all timepoint
min
Min of per-week log fold change, over all timepoints
If the user set log_transform=TRUE
in the creation of the
Moanin
object, the data will be log transformed before calculating
the fold-change.
A data.frame giving the estimated log-fold change for each gene
(row). For all methods except for "timely", the data frame will consist of
one column for each value of the argument contrasts
. For "timely"
there will be one column for each timepoint and contrast combination.
data(exampleData) moanin <- create_moanin_model(data=testData,meta=testMeta) estsTimely <- estimate_log_fold_change(moanin, contrasts=c("K-C"), method="timely") head(estsTimely) estsTimecourse <- estimate_log_fold_change(moanin, contrasts=c("K-C"),method="timecourse") head(estsTimecourse)