Calculating arithmetic mean of technical replicates for subsequent ANOVA analysis
meanTech(x, groups)A raw data frame including technical replicates.
grouping columns based on which the mean technical replicates are calculated.
A data frame with the mean of technical replicates.
The meanTech calculates mean of technical replicates. Arithmetic mean of technical replicates can be calculated in order to simplify the statistical comparison between sample groups.
# See example input data frame:
data_withTechRep
#> factor1 factor2 factor3 biolrep techrep Etarget targetCt Eref refCt
#> 1 Line1 Heat Ctrl 1 1 2 33.346 2 31.520
#> 2 Line1 Heat Ctrl 1 2 2 28.895 2 29.905
#> 3 Line1 Heat Ctrl 1 3 2 28.893 2 29.454
#> 4 Line1 Heat Ctrl 2 1 2 30.411 2 28.798
#> 5 Line1 Heat Ctrl 2 2 2 33.390 2 31.574
#> 6 Line1 Heat Ctrl 2 3 2 33.211 2 31.326
#> 7 Line1 Heat Ctrl 3 1 2 33.845 2 31.759
#> 8 Line1 Heat Ctrl 3 2 2 33.345 2 31.548
#> 9 Line1 Heat Ctrl 3 3 2 32.500 2 31.477
#> 10 Line1 Heat Treat 1 1 2 33.006 2 31.483
#> 11 Line1 Heat Treat 1 2 2 32.588 2 31.902
#> 12 Line1 Heat Treat 1 3 2 33.370 2 31.196
#> 13 Line1 Heat Treat 2 1 2 36.820 2 31.440
#> 14 Line1 Heat Treat 2 2 2 32.750 2 31.300
#> 15 Line1 Heat Treat 2 3 2 32.450 2 32.597
#> 16 Line1 Heat Treat 3 1 2 35.238 2 31.461
#> 17 Line1 Heat Treat 3 2 2 28.532 2 30.651
#> 18 Line1 Heat Treat 3 3 2 28.285 2 30.745
# Calculating mean of technical replicates
meanTech(data_withTechRep, groups = 1:4)
#> factor1 factor2 factor3 biolrep Etarget targetCt Eref refCt
#> 1 Line1 Heat Ctrl 1 2 30.37800 2 30.29300
#> 2 Line1 Heat Ctrl 2 2 32.33733 2 30.56600
#> 3 Line1 Heat Ctrl 3 2 33.23000 2 31.59467
#> 4 Line1 Heat Treat 1 2 32.98800 2 31.52700
#> 5 Line1 Heat Treat 2 2 34.00667 2 31.77900
#> 6 Line1 Heat Treat 3 2 30.68500 2 30.95233
# Calculating mean of technical replicates
meanTech(Lee_etal2020qPCR, groups = 1:3)
#> factor1 DS biolRep APOE_efficiency APOE_Ct GAPDH_efficiency GAPDH_Ct
#> 1 DSWHi D12 1 2.120 24.710 2.190 14.675
#> 2 DSWHi D12 2 1.985 27.785 1.935 16.385
#> 3 DSWHi D12 3 2.030 26.395 2.025 14.290
#> 4 DSWHi D15 1 2.100 27.030 2.045 15.130
#> 5 DSWHi D15 2 2.005 29.780 2.075 15.870
#> 6 DSWHi D15 3 2.005 28.205 2.155 13.810
#> 7 DSWHi D18 1 2.110 28.865 2.060 15.095
#> 8 DSWHi D18 2 1.935 31.380 1.885 16.110
#> 9 DSWHi D18 3 2.105 30.385 2.075 14.005
#> 10 DSWHi D7 1 2.080 24.175 2.100 15.260
#> 11 DSWHi D7 2 1.970 26.480 2.025 16.715
#> 12 DSWHi D7 3 2.195 24.830 2.175 14.725
#> 13 DSWi D12 1 1.945 25.330 2.080 15.840
#> 14 DSWi D12 2 1.920 24.160 2.030 14.725
#> 15 DSWi D12 3 2.105 23.830 2.335 14.745
#> 16 DSWi D15 1 1.905 28.615 1.895 16.180
#> 17 DSWi D15 2 1.935 28.280 1.990 14.755
#> 18 DSWi D15 3 2.125 27.005 2.395 14.735
#> 19 DSWi D18 1 1.930 29.730 2.040 16.585
#> 20 DSWi D18 2 2.085 28.655 1.970 14.490
#> 21 DSWi D18 3 2.090 28.680 2.140 14.060
#> 22 DSWi D7 1 2.035 25.835 2.000 16.625
#> 23 DSWi D7 2 2.085 24.145 2.080 14.895
#> 24 DSWi D7 3 2.305 23.330 2.145 14.470
#> 25 DSi D12 1 1.970 26.460 1.960 16.315
#> 26 DSi D12 2 2.095 25.305 1.945 16.275
#> 27 DSi D12 3 1.965 24.650 2.065 14.835
#> 28 DSi D15 1 1.915 29.240 1.940 16.200
#> 29 DSi D15 2 1.975 27.275 1.975 16.390
#> 30 DSi D15 3 1.935 26.940 2.045 14.855
#> 31 DSi D18 1 2.005 30.980 1.965 16.230
#> 32 DSi D18 2 1.960 30.215 1.970 16.305
#> 33 DSi D18 3 2.110 29.890 2.075 14.735
#> 34 DSi D7 1 2.000 25.090 2.020 17.065
#> 35 DSi D7 2 2.045 24.260 1.915 16.280
#> 36 DSi D7 3 1.910 23.680 2.035 15.205