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Glen_b
Glen_b
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Sander Van der Zeeuw
Sander Van der Zeeuw
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Which kind of test is most suitable for comparing correlations of 3 methods?

I have a R data frame like this:

structure(list(Mash_pear = c(0.328239947270445, 0.752207607551684, 
0.812118104861163, 0.640824971449627, 0.615568052052443, 0.546635339103089, 
0.557460706464288, 0.650480192893698, 0.418044504894929, 0.52962586938499
), tRap_pear = c(0.0350096175177328, 0.234255507711743, 0.23714999195134, 
0.185536020521134, 0.191585098617356, 0.201402054387186, 0.220911538536031, 
0.216072802572045, 0.132247101763063, 0.172753098431029), Beeml_pear = c(0.179209909971615, 
0.79129167285928, 0.856908302056589, 0.729078080521886, 0.709346164378725, 
0.669599784720647, 0.585348196746785, 0.639355942917055, 0.544909349368496, 
0.794652394149651), Mash_pear50 = c(0.192474082559755, 0.679726904159742, 
0.778564545349054, 0.573745352397321, 0.56633658385284, 0.472559997318901, 
0.462635414367878, 0.562128414492567, 0.354624921832056, 0.64532681437697
), labels = c("Aft1", "Alx3", "Alx4", "Arid3a", "Arid3a", "Arid3a", 
"Arid3a", "Arid5a", "Arid5a", "Aro80"), fam = c("AFT", "Homeo", 
"Homeo", "BRIGHT", "BRIGHT", "BRIGHT", "BRIGHT", "BRIGHT", "BRIGHT", 
"Zn2Cys6"), pwmlength = c("21", "17", "17", "17", "17", "17", 
"17", "14", "14", "21")), .Names = c("Mash_pear", "tRap_pear", 
"Beeml_pear", "Mash_pear50", "labels", "fam", "pwmlength"), row.names = c("Aft1", 
"Alx3_3418.2", "Alx4_1744.1", "Arid3a_3875.1_v1_primary", "Arid3a_3875.1_v2_primary", 
"Arid3a_3875.2_v1_primary", "Arid3a_3875.2_v2_primary", "Arid5a_3770.2_v1_primary", 
"Arid5a_3770.2_v2_primary", "Aro80"), class = "data.frame")

The first 4 columns are my correlations which i want to test for significant differences. These 4 columns are methods to estimate transcription factor binding to the DNA. Now i want to know which method performs best? I tried a paired t-test and unpaired t-test which seems the most suitable to me. Now i am wondering on how to interpret the test and are there other ways to test which method is better.

Data.frame for readability:

                         Mash_pear  tRap_pear Beeml_pear Mash_pear50 labels     fam pwmlength
Aft1                     0.3282399 0.03500962  0.1792099   0.1924741   Aft1     AFT        21
Alx3_3418.2              0.7522076 0.23425551  0.7912917   0.6797269   Alx3   Homeo        17
Alx4_1744.1              0.8121181 0.23714999  0.8569083   0.7785645   Alx4   Homeo        17
Arid3a_3875.1_v1_primary 0.6408250 0.18553602  0.7290781   0.5737454 Arid3a  BRIGHT        17
Arid3a_3875.1_v2_primary 0.6155681 0.19158510  0.7093462   0.5663366 Arid3a  BRIGHT        17
Arid3a_3875.2_v1_primary 0.5466353 0.20140205  0.6695998   0.4725600 Arid3a  BRIGHT        17
Arid3a_3875.2_v2_primary 0.5574607 0.22091154  0.5853482   0.4626354 Arid3a  BRIGHT        17
Arid5a_3770.2_v1_primary 0.6504802 0.21607280  0.6393559   0.5621284 Arid5a  BRIGHT        14
Arid5a_3770.2_v2_primary 0.4180445 0.13224710  0.5449093   0.3546249 Arid5a  BRIGHT        14
Aro80                    0.5296259 0.17275310  0.7946524   0.6453268  Aro80 Zn2Cys6        21