計算數據幀中連續行對之間的差值 - R.

Question

我有一個data.frame，其中每個基因名稱都重復，並包含2個條件的值：

df <- data.frame(gene=c("A","A","B","B","C","C"),
condition=c("control","treatment","control","treatment","control","treatment"),
count=c(10, 2, 5, 8, 5, 1), 
sd=c(1, 0.2, 0.1, 2, 0.8, 0.1))

  gene condition count  sd
1    A   control    10 1.0
2    A treatment     2 0.2
3    B   control     5 0.1
4    B treatment     8 2.0
5    C   control     5 0.8
6    C treatment     1 0.1

我想計算治療后“計數”是否增加或減少，並將它們標記為和/或將它們分組。 那是（偽代碼）：

for each unique(gene) do 
   if df[geneRow1,3]-df[geneRow2,3] > 0 then gene is "up"
       else gene is "down"

它最終應該是什么樣子（最后一列是可選的）：

up-regulated
 gene condition count  sd  regulation
 B    control     5    0.1    up
 B    treatment   8    2.0    up

down-regulated
 gene condition count  sd  regulation
 A    control     10   1.0    down
 A    treatment   2    0.2    down
 C    control     5    0.8    down
 C    treatment   1    0.1    down

我一直在用這種方式嘲笑我，包括玩ddply，我找不到解決方案 - 請一個倒霉的生物學家。

干杯。

Answer 1

plyr解決方案看起來像：

library(plyr)
reg.fun <- function(x) {
  reg.diff <- x$count[x$condition=='control'] - x$count[x$condition=='treatment']
  x$regulation <- ifelse(reg.diff > 0, 'up', 'down')

  x
}

ddply(df, .(gene), reg.fun)


  gene condition count  sd regulation
1    A   control    10 1.0         up
2    A treatment     2 0.2         up
3    B   control     5 0.1       down
4    B treatment     8 2.0       down
5    C   control     5 0.8         up
6    C treatment     1 0.1         up
> 

您還可以考慮使用不同的包和/或使用不同形狀的數據執行此操作：

df.w <- reshape(df, direction='wide', idvar='gene', timevar='condition')

library(data.table)
DT <- data.table(df.w, key='gene')

DT[, regulation:=ifelse(count.control-count.treatment > 0, 'up', 'down'), by=gene]

   gene count.control sd.control count.treatment sd.treatment regulation
1:    A            10        1.0               2          0.2         up
2:    B             5        0.1               8          2.0       down
3:    C             5        0.8               1          0.1         up
>     

Answer 2

像這樣的東西：

df$up.down <- with( df, ave(count, gene,
                FUN=function(diffs) c("up", "down")[1+(diff(diffs) < 0) ]) )
spltdf <- split(df, df$up.down)

> df
  gene condition count  sd up.down
1    A   control    10 1.0    down
2    A treatment     2 0.2    down
3    B   control     5 0.1      up
4    B treatment     8 2.0      up
5    C   control     5 0.8    down
6    C treatment     1 0.1    down
> spltdf
$down
  gene condition count  sd up.down
1    A   control    10 1.0    down
2    A treatment     2 0.2    down
5    C   control     5 0.8    down
6    C treatment     1 0.1    down

$up
  gene condition count  sd up.down
3    B   control     5 0.1      up
4    B treatment     8 2.0      up