好的，我已經檢查了互聯網，我仍在創建復制行的資料框。我有一個創建 welch t 檢驗結果的 for 回圈，我保存了如下值：
gene <- biomarkers$Symbol
pval <- ttest$p.value
tstat <- ttest$statistic
我嘗試使用 for 回圈進行迭代，以將結果添加到在塊開始時創建的資料框中
df2 <- data.frame(pathol=(character()),
genes=character(),
p_value=character(),
t_stat=character(),
stringsAsFactors=FALSE)
for (gene in biomarkers$Symbol) {
print(gene)
ddat <- degs[degs$Symbol== gene & degs$pathol=="mitosis",]
ttest <- t.test(logFC ~ value, data = ddat)
print (ttest)
df2[nrow(df2) 1,] #(this added the 10 genes only to the rows, not to the column)
然后我意識到我需要使用 lapply ......我試過這個：
prova <- lapply(biomarkers$Symbol, function(gene) {
append = (gene)
#ttest <- t.test(logFC ~ value, data = ddat)
})
do.call(rbind, prova)
這創建了十個基因的串列，但是當我取消注釋變數“ttest”時，它只是添加了一個串列：
[1,] "logFC by value"
[2,] "logFC by value"
[3,] "logFC by value"
[4,] "logFC by value"
[5,] "logFC by value"
[6,] "logFC by value"
[7,] "logFC by value"
[8,] "logFC by value"
[9,] "logFC by value"
[10,] "logFC by value"
我想最終得到一個如下所示的資料框：
| 悲愴 | 基因 | p_value | t_stat |
|---|---|---|---|
| 有絲分裂 | PBK | 0.05 | 000.4 |
| 有絲分裂 | PLK4 | 0.02 | 000.9 |
#所有行的基因都是相同的。任何幫助都會很棒！
編輯
輸入輸出：
dput(head(ddat))
structure(list(experiment = c("FP001RO_15_HI", "FP001RO_15_HI",
"FP001RO_15_HI", "FP001RO_15_LOW", "FP001RO_15_LOW", "FP001RO_15_LOW"
), Human.Gene.entrezID = c("57405", "57405", "57405", "57405",
"57405", "57405"), Symbol = c("SPC25", "SPC25", "SPC25", "SPC25",
"SPC25", "SPC25"), description = c("SPC25 component of NDC80 kinetochore complex",
"SPC25 component of NDC80 kinetochore complex", "SPC25 component of NDC80 kinetochore complex",
"SPC25 component of NDC80 kinetochore complex", "SPC25 component of NDC80 kinetochore complex",
"SPC25 component of NDC80 kinetochore complex"), score = c(3.867,
3.867, 3.867, 3.867, 3.867, 3.867), type = c("WGGNC", "WGGNC",
"WGGNC", "WGGNC", "WGGNC", "WGGNC"), pathol = c("mitosis", "mitosis",
"mitosis", "mitosis", "mitosis", "mitosis"), Probeid = c("295661_at",
"295661_at", "295661_at", "295661_at", "295661_at", "295661_at"
), logFC = c(-0.0641349806730976, -0.0641349806730976, -0.0641349806730976,
-0.0324566291924587, -0.0324566291924587, -0.0324566291924587
), AveExpr = c(4.1541958195567, 4.1541958195567, 4.1541958195567,
4.17003499529702, 4.17003499529702, 4.17003499529702), t = c(-0.567682269120301,
-0.567682269120301, -0.567682269120301, -0.214562465957216, -0.214562465957216,
-0.214562465957216), P.Value = c(0.580865708246137, 0.580865708246137,
0.580865708246137, 0.833648126277364, 0.833648126277364, 0.833648126277364
), adj.P.Val = c(0.828914361133465, 0.828914361133465, 0.828914361133465,
0.999594589241814, 0.999594589241814, 0.999594589241814), B = c(-6.4683360535952,
-6.4683360535952, -6.4683360535952, -5.45944975240508, -5.45944975240508,
-5.45944975240508), cpd = c("FP001RO", "FP001RO", "FP001RO",
"FP001RO", "FP001RO", "FP001RO"), time = c(15, 15, 15, 15, 15,
15), dose = c("HI", "HI", "HI", "LOW", "LOW", "LOW"), entrezgene_rat = c(295661,
295661, 295661, 295661, 295661, 295661), external_gene_name_rat = c("Spc25",
"Spc25", "Spc25", "Spc25", "Spc25", "Spc25"), external_gene_name_human = c("SPC25",
"SPC25", "SPC25", "SPC25", "SPC25", "SPC25"), entrezGene_probes_human = c("57405_at",
"57405_at", "57405_at", "57405_at", "57405_at", "57405_at"),
inMap_human_withGrey = c(1, 1, 1, 1, 1, 1), inMap_rat_withGrey = c(1,
1, 1, 1, 1, 1), variable = structure(c(11L, 12L, 10L, 10L,
11L, 12L), .Label = c("Necrosis1", "Necrosis2", "Necrosis3",
"hyperpl1", "hyperpl2", "hyperpl3", "fibrosis", "hypertrophy1",
"hypertrophy2", "mitosis1", "mitosis2", "mitosis3", "vacuolation1",
"vacuolation2", "vacuolation3"), class = "factor"), value = structure(c(1L,
1L, 1L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"),
pathology = c("mitosis", "mitosis", "mitosis", "mitosis",
"mitosis", "mitosis")), row.names = c(13L, 14L, 15L, 55L,
56L, 57L), class = "data.frame")
基因輸入
dput(biomarkers$Symbol)
c("PBK", "PLK4", "CDKN3", "CDCA3", "PRC1", "CDK1", "TCF19", "SHCBP1",
"CENPK", "SPC25")
uj5u.com熱心網友回復：
我們可能會使用
prova <- lapply(biomarkers$Symbol, function(gene) {
ddat <- subset(degs, Symbol == gene & pathol =="mitosis")
fmla <- reformulate('value', response = 'logFC')
if(nlevels(droplevels(ddat$val)) >=2) {
ttest <- t.test(fmla, data = ddat)
data.frame(pathol = 'mitosis', genes = gene,
p_value = ttest$p.value, t_stat = ttest$statistic)
} else NULL
})
names(prova) <- biomarkers$Symbol
out <- do.call(rbind, prova)
uj5u.com熱心網友回復：
您的問題的附錄，請記住在一次進行大量測驗時執行多個假設檢驗校正。
根據 akrun 的回答，這里是 BH 更正：
ord = order(prova$p.value)
prova$fdr = prova$p.value*nrow(prova)/ord
這將減少誤報的數量。
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