Estimates log fold change

Estimates log fold change

# S4 method for Moanin
estimate_log_fold_change(
  object,
  contrasts,
  method = c("timecourse", "sum", "max", "timely", "abs_sum", "abs_squared_sum", "min")
)

Arguments

object

An object of class Moanin, an object containing all related information for time course data and the splines model that will be used (if applicable). See create_moanin_model for more details.
contrasts

The contrasts to consider
method

method for calculating the log-fold change. See details.

Value

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.

Details

The following methods exist for calculating the log-fold change between conditions over time (default is "timecourse"):

  • timelyThe log-fold change for each individual timepoint (\(lfc(t)\))

  • timecourseThe average absolute per-week fold-change, multiplied by the sign of the average per-week fold-change.

  • sumSum of per-week log fold change, over all timepoints

  • maxMax of per-week log fold change, over all timepoints

  • abs_sumSum of the absolute value of the per-week log fold change, over all timepoints

  • abs_squared_sumSum of the square value of the per-week log fold change, over all timepoint

  • minMin 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.

Examples

data(exampleData)
moanin <- create_moanin_model(data=testData,meta=testMeta)
estsTimely <- estimate_log_fold_change(moanin,
   contrasts=c("K-C"), method="timely")
head(estsTimely)
#>                           K-C:0       K-C:3       K-C:6       K-C:9      K-C:12
#> NM_009912           0.006142387 -0.02249478  0.05159468  0.11439732 -0.06146186
#> NM_008725          -2.654785490 -0.27343865  0.68829245 -0.47188066  1.74633186
#> NM_007473           0.160362295 -0.10176553 -0.50795729 -0.05957733 -0.42501853
#> ENSMUST00000094955 -0.350591967 -0.64630114 -0.25647962 -0.35991022 -0.41771162
#> NM_001042489       -0.088974987 -0.23213806  0.18915007 -0.17861133 -0.33206436
#> NM_008159           0.386377015 -0.02983951  0.08627550  0.32663505  0.18930621
#>                         K-C:18      K-C:24     K-C:30      K-C:36       K-C:48
#> NM_009912          -0.02073242 -0.09083955 -0.3541720 -0.53331742 -1.277439619
#> NM_008725           0.08891687 -2.04306556  0.5988342  0.90478316 -1.985072414
#> NM_007473          -0.37838655 -0.10451264 -0.4244740  1.03998928 -0.210841392
#> ENSMUST00000094955 -0.43184782 -0.56349779 -0.4123224 -0.28054680 -0.005760532
#> NM_001042489        0.06765746 -0.10443410 -0.4429174  0.07642017  0.683764131
#> NM_008159           0.30017465  0.15285657 -0.5034323 -0.81198828 -1.029925872
#>                        K-C:60     K-C:72    K-C:120     K-C:168
#> NM_009912          -0.9508426 -1.3977873 -0.2928437 -0.86763035
#> NM_008725           2.4051843 -0.2778756  2.7361353  0.71780185
#> NM_007473          -0.9481227 -0.3388556  0.4967829  0.19739522
#> ENSMUST00000094955  0.1210526  0.3766247  0.2479095  0.50586716
#> NM_001042489        0.5895878  0.3821725  0.3287660  0.02832949
#> NM_008159          -1.1093377 -0.1279111 -0.3025480 -0.72920750
estsTimecourse <- estimate_log_fold_change(moanin,
   contrasts=c("K-C"),method="timecourse")
head(estsTimecourse)
#>                           K-C
#> NM_009912          -0.4315497
#> NM_008725           1.2565999
#> NM_007473          -0.3852887
#> ENSMUST00000094955 -0.3554588
#> NM_001042489        0.2660706
#> NM_008159          -0.4347011