R：具有 4 个级别的 1 因子方差分析
R: 1-factor ANOVA with 4-levels
我想用 4 个水平测量 1 因子方差分析：ctl 与 schiz、bp、dep。我期望 aov.run 到 return 是一个包含 3 个变量的数字向量，因为我正在将 ctl 与其他三个级别进行比较。但是，我在 aov.run 中只得到 2 个变量。为什么？
d <- read.table("filtered.mds", header=T)
ann <- read.table("clinical_table.txt", header=T, sep="\t")
# Create new dataframe
dat <- t(cbind(d$C1,d$C2))
colnames(dat) <- paste(ann$Profile, data.table::rowid(ann$Profile), sep="_")
rownames(dat) <- c("C1", "C2")
# Levels
ctl <- grepl("^Unaffected control", ann$Profile)
schiz <- grepl("^Schiz.", ann$Profile)
bp <- grepl("^BP", ann$Profile)
dep <- grepl("^Dep.", ann$Profile)
# 1-factor ANOVA with 4 levels
aov.lvl <- function(x,s1,s2,s3,s4) {
x1 <- as.numeric(x[s1])
x2 <- as.numeric(x[s2])
x3 <- as.numeric(x[s3])
x4 <- as.numeric(x[s4])
fac <- c(rep("A",length(x1)), rep("B",length(x2)), rep("C",length(x3)), rep("D",length(x4)))
a.dat <- data.frame(as.factor(fac),c(x1,x2,x3,x4))
names(a.dat) <- c("factor","express")
p.out <- summary(aov(express~factor, a.dat))[[1]][1,5]
return(p.out)
}
aov.run <- apply(dat, 1, aov.lvl, s1=ctl, s2=schiz, s3=bp, s4=dep)
数据帧d
> dput(d)
structure(list(FID = c("AC10", "AC11", "AC12", "AC13", "AC14",
"AC15", "AC17", "AC18", "AC19", "AC1", "AC20", "AC21", "AC22",
"AC23", "AC24", "AC25", "AC26", "AC27", "AC29", "AC2", "AC30",
"AC31", "AC32", "AC33", "AC34", "AC35", "AC36", "AC37", "AC38",
"AC39", "AC3", "AC40", "AC41", "AC42", "AC43", "AC45", "AC46",
"AC47", "AC48", "AC49", "AC50", "AC51", "AC52", "AC53", "AC54",
"AC55", "AC56", "AC57", "AC58", "AC5", "AC60", "AC61", "AC62",
"AC63", "AC64", "AC65", "AC66", "AC67", "AC69", "AC6", "AC70",
"AC71", "AC72", "AC73", "AC74", "AC75", "AC76", "AC77", "AC78",
"AC79", "AC7", "AC80", "AC81", "AC82", "AC83", "AC84", "AC86",
"AC87", "AC88", "AC89", "AC8", "AC90", "AC91", "AC92", "AC9",
"AC100", "AC101", "AC102", "AC103", "AC104", "AC105", "AC16",
"AC68", "AC93", "AC94", "AC95", "AC96", "AC97", "AC99", "DE10",
"DE12", "DE13", "DE14", "DE15", "DE16", "DE17", "DE18", "DE19",
"DE1", "DE20", "DE21", "DE22", "DE23", "DE25", "DE26", "DE27",
"DE2", "DE33", "DE34", "DE35", "DE36", "DE37", "DE38", "DE39",
"DE3", "DE40", "DE41", "DE42", "DE44", "DE45", "DE46", "DE47",
"DE48", "DE49", "DE4", "DE50", "DE51", "DE52", "DE53", "DE54",
"DE55", "DE56", "DE57", "DE58", "DE59", "DE60", "DE7", "DE9",
"DE29", "DE30", "DE32", "DE43", "DE5"), IID = c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), SOL = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), C1 = c(-0.00385609, 0.0101138, -0.0146168, -0.0218236, -0.0134745,
-0.017089, 0.0152448, 0.0134359, 0.00540102, -0.0125389, 0.00463956,
-0.00416079, -0.000325898, 0.0132781, 0.0130666, 0.00718399,
-0.0051912, -0.0227934, 0.0364974, -0.0180301, -0.0226556, -0.00585266,
0.0258924, -0.00994298, -0.00380612, 0.0187883, 0.0103367, 0.00747272,
0.0191431, -0.00501846, -0.00118336, 0.0361201, 0.00830498, 0.00380194,
0.00667686, -0.000441697, -0.00170991, -0.0281008, -0.00424591,
0.0213412, 0.00261405, 0.016154, 0.0098956, 0.0141544, 0.0367203,
0.0144693, 0.0256731, -0.00218851, 0.0204603, -0.000603019, -0.00504176,
-0.00917368, 0.00237875, 0.0175946, 0.0188388, 0.0368965, -0.00408476,
0.00871812, -0.00851917, 0.0252035, -0.00915532, 0.0223745, 0.016866,
0.026825, 0.0366276, 0.0540474, 0.0386237, 0.0029996, 0.0207176,
0.0177353, -0.0066377, 0.0343811, 0.0282509, 0.00526683, 0.0459516,
0.00976286, 0.0259005, -0.00104822, -0.012696, 0.0134071, 0.0231658,
0.00359455, 0.0194968, -0.000936478, -0.0029218, -0.0058512,
-0.000837274, -0.0129465, -0.0102079, -0.00559039, 0.0118966,
0.00147658, 0.0120396, -0.0104779, -0.0315149, -0.0115454, -0.0122457,
-6.72242e-05, 0.00370599, -0.0164126, -0.0107853, -0.0271741,
-0.0212005, -0.0445118, -0.0387773, -0.025109, -0.0321735, -0.0398603,
-0.0266408, -0.0260984, -0.0296337, -0.0185381, -0.0403944, 0.0197937,
-0.0176322, -0.013238, -0.0071666, -7.27277e-05, 0.00397489,
0.0335056, -0.00604706, -0.00926438, 0.00706601, -0.0156982,
-0.0275085, -0.00864179, -0.0247967, -0.030564, -0.00767327,
-0.0235161, 0.00649758, -0.0329062, -0.0016138, -0.00701695,
0.00819454, 0.0100377, 0.0250199, -0.0493141, -0.0216641, -0.0244709,
-0.00466616, 0.016751, -0.0191688, -0.00492488, -0.0162364, -0.0167085,
-0.0113427, 0.000422333, 0.030274, 0.0317995, 0.00237194, -0.00693838,
-0.0100835), C2 = c(0.000865365, -0.001752, 0.0189917, -0.023343,
-0.0340531, -0.0258976, -0.00794043, 0.0173163, 0.00639341, -0.0343077,
0.01083, -0.0402179, 0.0158751, -0.00262893, -0.0216757, -0.00261259,
-0.00542089, -0.00515714, 0.0105216, -0.0193606, 0.00692795,
-0.0117295, -0.0235627, -0.00850041, -0.0156109, -0.00871875,
-0.0163218, 0.0227143, -0.0161961, -0.0176719, -0.0070994, 0.0262932,
0.00164033, -0.00969917, -0.0197631, -0.0154387, -0.0194608,
0.00442207, -0.0234804, 0.00822342, -0.00657274, -0.0092332,
0.0130892, -0.0345162, -0.0114187, -0.0129497, -0.00306092, 0.0417858,
0.0262002, -0.0188849, -0.0184154, -0.0109956, -0.0151195, -0.00414531,
0.010064, 0.0308816, -0.0153337, 0.0157867, -0.0289866, -0.0106713,
0.000112714, -0.00152177, 0.0184509, 0.0112357, 0.00097954, 0.032083,
0.0190258, -0.0371498, -0.0307498, -0.00947645, -0.00198995,
0.015845, -0.0240248, -0.0122369, -0.00107049, -0.0144661, 0.0207883,
-0.0418619, -0.0123712, -0.0212721, -0.00667244, -0.028512, -0.00522357,
-0.018842, -0.0123026, -0.00511655, 0.0188473, 0.00739189, 0.0321578,
-0.015449, 0.0214631, -0.00995001, -0.00144645, 0.00934907, 0.0344757,
-0.0220224, 0.0121403, -0.00615057, -0.0208969, 0.0313899, -0.0251011,
0.011635, 0.00536455, 0.0233033, -0.0019204, 0.0273593, 0.00844028,
0.00181444, 0.02824, 0.0255231, 0.00266055, -0.00850383, -0.0129938,
0.0268634, 0.0195986, 0.0320615, -0.0026514, 0.0127147, 0.014279,
0.0553434, -0.020963, 0.00629119, -0.0244099, -0.0080923, 0.0173508,
0.0485753, -0.00666049, 0.0501603, 0.0029162, 0.0267363, 0.0066606,
0.00857736, 0.0172693, -0.00827586, -0.0117478, -0.00336638,
0.00954265, -0.00889617, 0.00290055, 0.0229832, 0.0504569, 0.025979,
-0.00795356, -0.0135421, -0.00359528, 0.0150037, -0.0105817,
0.0167827, 0.0110882, 0.00200862, -0.00597284, -0.0188371, -0.00827599
)), class = "data.frame", row.names = c(NA, -153L))
列ann$Profile
> dput(ann$Profile)
c("Schiz.", "Schiz.", "Schiz.", "BP", "BP", "Unaffected control",
"Schiz.", "BP", "Unaffected control", "BP", "BP", "BP", "Schiz.",
"BP", "Unaffected control", "Schiz.", "Schiz.", "Unaffected control",
"Unaffected control", "BP", "Unaffected control", "Schiz.", "BP",
"Unaffected control", "BP", "Unaffected control", "BP", "Schiz.",
"Unaffected control", "Schiz.", "Schiz.", "Schiz.", "Schiz.",
"BP", "Unaffected control", "Schiz.", "BP", "Schiz.", "BP", "Unaffected control",
"BP", "Unaffected control", "Unaffected control", "Unaffected control",
"Unaffected control", "Schiz.", "Unaffected control", "BP", "BP",
"BP", "Unaffected control", "BP", "BP", "BP", "BP", "Unaffected control",
"Schiz.", "Unaffected control", "BP", "BP", "Unaffected control",
"Unaffected control", "BP", "Schiz.", "BP", "Schiz.", "BP", "Unaffected control",
"Schiz.", "Unaffected control", "Schiz.", "Unaffected control",
"Schiz.", "Schiz.", "Unaffected control", "Unaffected control",
"Unaffected control", "Schiz.", "Schiz.", "BP", "BP", "Unaffected control",
"Unaffected control", "Schiz.", "Schiz.", "Schiz.", "Schiz.",
"BP", "Unaffected control", "BP", "Unaffected control", "BP",
"Schiz.", "Schiz.", "Schiz.", "Unaffected control", "Unaffected control",
"Schiz.", "Unaffected control", "Unaffected control", "Unaffected control",
"BP", "BP", "Dep.", "Unaffected control", "Unaffected control",
"Dep.", "Dep.", "Dep.", "Dep.", "Dep.", "Unaffected control",
"Unaffected control", "Schiz.", "Dep.", "BP", "Dep.", "Schiz.",
"Schiz.", "Schiz.", "BP", "Unaffected control", "Unaffected control",
"Unaffected control", "BP", "BP", "Dep.", "Schiz.", "Dep.", "BP",
"Unaffected control", "Unaffected control", "Schiz.", "Schiz.",
"Unaffected control", "Unaffected control", "BP", "BP", "Schiz.",
"Dep.", "BP", "Dep.", "BP", "Schiz.", "Unaffected control", "Dep.",
"BP", "Schiz.", "Dep.", "Dep.", "BP", "BP", "Schiz.")
aov.run 分别返回与每个列上的方差分析 运行 关联的 p-value。每个 ANOVA 只有一个 p-value，所以你得到一个 C1 和一个 C2，总共两个。一切正常！
听起来您更感兴趣的是方差分析的 post-hoc 结果，该结果是通过 Tukey 检验或类似方法获得的。 Tukey 的 HSD 将为您分别提供每个比较的 p-value，每个方差分析进行 6 次比较（“Dep.-BP”、“Schiz.-BP”、“未受影响 control-BP”、“Schiz.-Dep. ”、“未受影响 control-Dep。”和“未受影响 control-Schiz。”）听起来您只对“未受影响 control-BP”、“未受影响 control-Dep 。”和“未受影响 control-Schiz。”比较。您可以通过将对 summary() 的调用替换为 tukeyHSD():
来获取这些
v <- cbind(d, Profile=ann$Profile)
# p-value for each ANOVA
summary(aov(C1~Profile, data = v))[[1]][1,5]
summary(aov(C2~Profile, data = v))[[1]][1,5]
# p-value for each comparison within the ANOVA
TukeyHSD(aov(C1~Profile, data = v))
TukeyHSD(aov(C2~Profile, data = v))
我想用 4 个水平测量 1 因子方差分析：ctl 与 schiz、bp、dep。我期望 aov.run 到 return 是一个包含 3 个变量的数字向量，因为我正在将 ctl 与其他三个级别进行比较。但是，我在 aov.run 中只得到 2 个变量。为什么？
d <- read.table("filtered.mds", header=T)
ann <- read.table("clinical_table.txt", header=T, sep="\t")
# Create new dataframe
dat <- t(cbind(d$C1,d$C2))
colnames(dat) <- paste(ann$Profile, data.table::rowid(ann$Profile), sep="_")
rownames(dat) <- c("C1", "C2")
# Levels
ctl <- grepl("^Unaffected control", ann$Profile)
schiz <- grepl("^Schiz.", ann$Profile)
bp <- grepl("^BP", ann$Profile)
dep <- grepl("^Dep.", ann$Profile)
# 1-factor ANOVA with 4 levels
aov.lvl <- function(x,s1,s2,s3,s4) {
x1 <- as.numeric(x[s1])
x2 <- as.numeric(x[s2])
x3 <- as.numeric(x[s3])
x4 <- as.numeric(x[s4])
fac <- c(rep("A",length(x1)), rep("B",length(x2)), rep("C",length(x3)), rep("D",length(x4)))
a.dat <- data.frame(as.factor(fac),c(x1,x2,x3,x4))
names(a.dat) <- c("factor","express")
p.out <- summary(aov(express~factor, a.dat))[[1]][1,5]
return(p.out)
}
aov.run <- apply(dat, 1, aov.lvl, s1=ctl, s2=schiz, s3=bp, s4=dep)
数据帧d
> dput(d)
structure(list(FID = c("AC10", "AC11", "AC12", "AC13", "AC14",
"AC15", "AC17", "AC18", "AC19", "AC1", "AC20", "AC21", "AC22",
"AC23", "AC24", "AC25", "AC26", "AC27", "AC29", "AC2", "AC30",
"AC31", "AC32", "AC33", "AC34", "AC35", "AC36", "AC37", "AC38",
"AC39", "AC3", "AC40", "AC41", "AC42", "AC43", "AC45", "AC46",
"AC47", "AC48", "AC49", "AC50", "AC51", "AC52", "AC53", "AC54",
"AC55", "AC56", "AC57", "AC58", "AC5", "AC60", "AC61", "AC62",
"AC63", "AC64", "AC65", "AC66", "AC67", "AC69", "AC6", "AC70",
"AC71", "AC72", "AC73", "AC74", "AC75", "AC76", "AC77", "AC78",
"AC79", "AC7", "AC80", "AC81", "AC82", "AC83", "AC84", "AC86",
"AC87", "AC88", "AC89", "AC8", "AC90", "AC91", "AC92", "AC9",
"AC100", "AC101", "AC102", "AC103", "AC104", "AC105", "AC16",
"AC68", "AC93", "AC94", "AC95", "AC96", "AC97", "AC99", "DE10",
"DE12", "DE13", "DE14", "DE15", "DE16", "DE17", "DE18", "DE19",
"DE1", "DE20", "DE21", "DE22", "DE23", "DE25", "DE26", "DE27",
"DE2", "DE33", "DE34", "DE35", "DE36", "DE37", "DE38", "DE39",
"DE3", "DE40", "DE41", "DE42", "DE44", "DE45", "DE46", "DE47",
"DE48", "DE49", "DE4", "DE50", "DE51", "DE52", "DE53", "DE54",
"DE55", "DE56", "DE57", "DE58", "DE59", "DE60", "DE7", "DE9",
"DE29", "DE30", "DE32", "DE43", "DE5"), IID = c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), SOL = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), C1 = c(-0.00385609, 0.0101138, -0.0146168, -0.0218236, -0.0134745,
-0.017089, 0.0152448, 0.0134359, 0.00540102, -0.0125389, 0.00463956,
-0.00416079, -0.000325898, 0.0132781, 0.0130666, 0.00718399,
-0.0051912, -0.0227934, 0.0364974, -0.0180301, -0.0226556, -0.00585266,
0.0258924, -0.00994298, -0.00380612, 0.0187883, 0.0103367, 0.00747272,
0.0191431, -0.00501846, -0.00118336, 0.0361201, 0.00830498, 0.00380194,
0.00667686, -0.000441697, -0.00170991, -0.0281008, -0.00424591,
0.0213412, 0.00261405, 0.016154, 0.0098956, 0.0141544, 0.0367203,
0.0144693, 0.0256731, -0.00218851, 0.0204603, -0.000603019, -0.00504176,
-0.00917368, 0.00237875, 0.0175946, 0.0188388, 0.0368965, -0.00408476,
0.00871812, -0.00851917, 0.0252035, -0.00915532, 0.0223745, 0.016866,
0.026825, 0.0366276, 0.0540474, 0.0386237, 0.0029996, 0.0207176,
0.0177353, -0.0066377, 0.0343811, 0.0282509, 0.00526683, 0.0459516,
0.00976286, 0.0259005, -0.00104822, -0.012696, 0.0134071, 0.0231658,
0.00359455, 0.0194968, -0.000936478, -0.0029218, -0.0058512,
-0.000837274, -0.0129465, -0.0102079, -0.00559039, 0.0118966,
0.00147658, 0.0120396, -0.0104779, -0.0315149, -0.0115454, -0.0122457,
-6.72242e-05, 0.00370599, -0.0164126, -0.0107853, -0.0271741,
-0.0212005, -0.0445118, -0.0387773, -0.025109, -0.0321735, -0.0398603,
-0.0266408, -0.0260984, -0.0296337, -0.0185381, -0.0403944, 0.0197937,
-0.0176322, -0.013238, -0.0071666, -7.27277e-05, 0.00397489,
0.0335056, -0.00604706, -0.00926438, 0.00706601, -0.0156982,
-0.0275085, -0.00864179, -0.0247967, -0.030564, -0.00767327,
-0.0235161, 0.00649758, -0.0329062, -0.0016138, -0.00701695,
0.00819454, 0.0100377, 0.0250199, -0.0493141, -0.0216641, -0.0244709,
-0.00466616, 0.016751, -0.0191688, -0.00492488, -0.0162364, -0.0167085,
-0.0113427, 0.000422333, 0.030274, 0.0317995, 0.00237194, -0.00693838,
-0.0100835), C2 = c(0.000865365, -0.001752, 0.0189917, -0.023343,
-0.0340531, -0.0258976, -0.00794043, 0.0173163, 0.00639341, -0.0343077,
0.01083, -0.0402179, 0.0158751, -0.00262893, -0.0216757, -0.00261259,
-0.00542089, -0.00515714, 0.0105216, -0.0193606, 0.00692795,
-0.0117295, -0.0235627, -0.00850041, -0.0156109, -0.00871875,
-0.0163218, 0.0227143, -0.0161961, -0.0176719, -0.0070994, 0.0262932,
0.00164033, -0.00969917, -0.0197631, -0.0154387, -0.0194608,
0.00442207, -0.0234804, 0.00822342, -0.00657274, -0.0092332,
0.0130892, -0.0345162, -0.0114187, -0.0129497, -0.00306092, 0.0417858,
0.0262002, -0.0188849, -0.0184154, -0.0109956, -0.0151195, -0.00414531,
0.010064, 0.0308816, -0.0153337, 0.0157867, -0.0289866, -0.0106713,
0.000112714, -0.00152177, 0.0184509, 0.0112357, 0.00097954, 0.032083,
0.0190258, -0.0371498, -0.0307498, -0.00947645, -0.00198995,
0.015845, -0.0240248, -0.0122369, -0.00107049, -0.0144661, 0.0207883,
-0.0418619, -0.0123712, -0.0212721, -0.00667244, -0.028512, -0.00522357,
-0.018842, -0.0123026, -0.00511655, 0.0188473, 0.00739189, 0.0321578,
-0.015449, 0.0214631, -0.00995001, -0.00144645, 0.00934907, 0.0344757,
-0.0220224, 0.0121403, -0.00615057, -0.0208969, 0.0313899, -0.0251011,
0.011635, 0.00536455, 0.0233033, -0.0019204, 0.0273593, 0.00844028,
0.00181444, 0.02824, 0.0255231, 0.00266055, -0.00850383, -0.0129938,
0.0268634, 0.0195986, 0.0320615, -0.0026514, 0.0127147, 0.014279,
0.0553434, -0.020963, 0.00629119, -0.0244099, -0.0080923, 0.0173508,
0.0485753, -0.00666049, 0.0501603, 0.0029162, 0.0267363, 0.0066606,
0.00857736, 0.0172693, -0.00827586, -0.0117478, -0.00336638,
0.00954265, -0.00889617, 0.00290055, 0.0229832, 0.0504569, 0.025979,
-0.00795356, -0.0135421, -0.00359528, 0.0150037, -0.0105817,
0.0167827, 0.0110882, 0.00200862, -0.00597284, -0.0188371, -0.00827599
)), class = "data.frame", row.names = c(NA, -153L))
列ann$Profile
> dput(ann$Profile)
c("Schiz.", "Schiz.", "Schiz.", "BP", "BP", "Unaffected control",
"Schiz.", "BP", "Unaffected control", "BP", "BP", "BP", "Schiz.",
"BP", "Unaffected control", "Schiz.", "Schiz.", "Unaffected control",
"Unaffected control", "BP", "Unaffected control", "Schiz.", "BP",
"Unaffected control", "BP", "Unaffected control", "BP", "Schiz.",
"Unaffected control", "Schiz.", "Schiz.", "Schiz.", "Schiz.",
"BP", "Unaffected control", "Schiz.", "BP", "Schiz.", "BP", "Unaffected control",
"BP", "Unaffected control", "Unaffected control", "Unaffected control",
"Unaffected control", "Schiz.", "Unaffected control", "BP", "BP",
"BP", "Unaffected control", "BP", "BP", "BP", "BP", "Unaffected control",
"Schiz.", "Unaffected control", "BP", "BP", "Unaffected control",
"Unaffected control", "BP", "Schiz.", "BP", "Schiz.", "BP", "Unaffected control",
"Schiz.", "Unaffected control", "Schiz.", "Unaffected control",
"Schiz.", "Schiz.", "Unaffected control", "Unaffected control",
"Unaffected control", "Schiz.", "Schiz.", "BP", "BP", "Unaffected control",
"Unaffected control", "Schiz.", "Schiz.", "Schiz.", "Schiz.",
"BP", "Unaffected control", "BP", "Unaffected control", "BP",
"Schiz.", "Schiz.", "Schiz.", "Unaffected control", "Unaffected control",
"Schiz.", "Unaffected control", "Unaffected control", "Unaffected control",
"BP", "BP", "Dep.", "Unaffected control", "Unaffected control",
"Dep.", "Dep.", "Dep.", "Dep.", "Dep.", "Unaffected control",
"Unaffected control", "Schiz.", "Dep.", "BP", "Dep.", "Schiz.",
"Schiz.", "Schiz.", "BP", "Unaffected control", "Unaffected control",
"Unaffected control", "BP", "BP", "Dep.", "Schiz.", "Dep.", "BP",
"Unaffected control", "Unaffected control", "Schiz.", "Schiz.",
"Unaffected control", "Unaffected control", "BP", "BP", "Schiz.",
"Dep.", "BP", "Dep.", "BP", "Schiz.", "Unaffected control", "Dep.",
"BP", "Schiz.", "Dep.", "Dep.", "BP", "BP", "Schiz.")
aov.run 分别返回与每个列上的方差分析 运行 关联的 p-value。每个 ANOVA 只有一个 p-value，所以你得到一个 C1 和一个 C2，总共两个。一切正常！
听起来您更感兴趣的是方差分析的 post-hoc 结果，该结果是通过 Tukey 检验或类似方法获得的。 Tukey 的 HSD 将为您分别提供每个比较的 p-value，每个方差分析进行 6 次比较（“Dep.-BP”、“Schiz.-BP”、“未受影响 control-BP”、“Schiz.-Dep. ”、“未受影响 control-Dep。”和“未受影响 control-Schiz。”）听起来您只对“未受影响 control-BP”、“未受影响 control-Dep 。”和“未受影响 control-Schiz。”比较。您可以通过将对 summary() 的调用替换为 tukeyHSD():
v <- cbind(d, Profile=ann$Profile)
# p-value for each ANOVA
summary(aov(C1~Profile, data = v))[[1]][1,5]
summary(aov(C2~Profile, data = v))[[1]][1,5]
# p-value for each comparison within the ANOVA
TukeyHSD(aov(C1~Profile, data = v))
TukeyHSD(aov(C2~Profile, data = v))