使用tapply生成数据子集的方差

Question

我列出了每个基因的1-3个探针，以及每个探针的强度值。一个例子如下：

GENE_ID             Probes                  Intensity 
GENE:JGI_V11_100009 GENE:JGI_V11_1000090102 253.479375
GENE:JGI_V11_100009 GENE:JGI_V11_1000090202 712.235625
GENE:JGI_V11_100036 GENE:JGI_V11_1000360103 449.065625
GENE:JGI_V11_100036 GENE:JGI_V11_1000360203 641.341875
GENE:JGI_V11_100036 GENE:JGI_V11_1000360303 1237.07125
GENE:JGI_V11_100044 GENE:JGI_V11_1000440101 456.133125
GENE:JGI_V11_100045 GENE:JGI_V11_1000450101 369.790625
GENE:JGI_V11_100062 GENE:JGI_V11_1000620102 2839.97375
GENE:JGI_V11_100062 GENE:JGI_V11_1000620202 6384.55125

我想确定每个基因的探针之间的差异(因此，对于每个基因，我有一个方差值)。

我知道我应该使用tapply()函数，但除了以下之外，我不知道如何实现这一点：

tapply( , , var)

Answer 1

您可以使用data.table或dplyr来完成这一任务。这是一个典型的group_by案例：

library(dplyr)
df %>% 
    group_by(GENE_ID) %>% 
    mutate(new_var = var(Intensity))


library(data.table)
setDT(df)
df[, new_var := var(Intensity), .(GENE_ID)]

这两种情况下的输出都是：

               GENE_ID                  Probes Intensity   new_var
1: GENE:JGI_V11_100009 GENE:JGI_V11_1000090102  253.4794  105228.6
2: GENE:JGI_V11_100009 GENE:JGI_V11_1000090202  712.2356  105228.6
3: GENE:JGI_V11_100036 GENE:JGI_V11_1000360103  449.0656  168802.8
4: GENE:JGI_V11_100036 GENE:JGI_V11_1000360203  641.3419  168802.8
5: GENE:JGI_V11_100036 GENE:JGI_V11_1000360303 1237.0712  168802.8
6: GENE:JGI_V11_100044 GENE:JGI_V11_1000440101  456.1331        NA
7: GENE:JGI_V11_100045 GENE:JGI_V11_1000450101  369.7906        NA
8: GENE:JGI_V11_100062 GENE:JGI_V11_1000620102 2839.9738 6282014.8
9: GENE:JGI_V11_100062 GENE:JGI_V11_1000620202 6384.5513 6282014.8

Answer 2

这是基R中典型的ave情况，而tapply返回的向量长度与分组因子的唯一值相同，ave返回具有相同数据帧/矩阵列向量长度的分组平均值(或其他聚合)(必要时由组重复值)：

gene_df$Probes_var <- ave(gene_df$Intensity, gene_df$GENE_ID, FUN=var)
gene_df

#               GENE_ID                  Probes Intensity Probes_var
# 1 GENE:JGI_V11_100009 GENE:JGI_V11_1000090102  253.4794   105228.6
# 2 GENE:JGI_V11_100009 GENE:JGI_V11_1000090202  712.2356   105228.6
# 3 GENE:JGI_V11_100036 GENE:JGI_V11_1000360103  449.0656   168802.8
# 4 GENE:JGI_V11_100036 GENE:JGI_V11_1000360203  641.3419   168802.8
# 5 GENE:JGI_V11_100036 GENE:JGI_V11_1000360303 1237.0712   168802.8
# 6 GENE:JGI_V11_100044 GENE:JGI_V11_1000440101  456.1331         NA
# 7 GENE:JGI_V11_100045 GENE:JGI_V11_1000450101  369.7906         NA
# 8 GENE:JGI_V11_100062 GENE:JGI_V11_1000620102 2839.9738  6282014.8
# 9 GENE:JGI_V11_100062 GENE:JGI_V11_1000620202 6384.5513  6282014.8