归一化为数据框中控制组的平均值
Normalizing to average of control group within a data frame
我有一个数据框：
tissue_merge <-structure(list(Experiment = c(170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L), Sample = structure(c(11L,
11L, 11L, 12L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L,
15L, 16L, 16L, 16L, 17L, 17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L,
20L, 20L, 20L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L,
24L, 24L, 25L, 25L, 25L, 26L, 26L, 26L, 27L, 27L, 27L, 28L, 28L,
28L, 29L, 29L, 29L, 30L, 30L, 30L, 31L, 31L, 31L, 32L, 32L, 32L,
33L, 33L, 33L, 34L, 34L, 34L, 35L, 35L, 35L, 36L, 36L, 36L, 37L,
37L, 37L, 38L, 38L, 38L, 39L, 39L, 39L, 40L, 40L, 40L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L), .Label = c("1: FL_643", "10: cKO_657",
"2: FL_645", "3: FL_647", "4: FL_656", "5: FL_658", "6: cKO_644",
"7: cKO_646", "8: cKO_654", "9: cKO_655", "Spl_cKO_19", "Spl_cKO_21",
"Spl_cKO_29", "Spl_cKO_37", "Spl_cKO_39", "Spl_FL_622", "Spl_FL_630",
"Spl_FL_631", "Spl_FL_635", "Spl_FL_638", "iLN_cKO_19", "iLN_cKO_21",
"iLN_cKO_29", "iLN_cKO_37", "iLN_cKO_39", "iLN_FL_622", "iLN_FL_630",
"iLN_FL_631", "iLN_FL_635", "iLN_FL_638", "Thy_cKO_19", "Thy_cKO_21",
"Thy_cKO_29", "Thy_cKO_37", "Thy_cKO_39", "Thy_FL_622", "Thy_FL_630",
"Thy_FL_631", "Thy_FL_635", "Thy_FL_638"), class = "factor"),
Genotype = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("miR-15/16 FL", "miR-15/16 cKO"
), class = "factor"), variable = structure(c(1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L), .Label = c("MFI CD127 Foxp3+ CD4+",
"MFI CD127 Foxp3- CD4+", "MFI CD127 CD8+"), class = "factor"),
value = c(3076, 4718, 4987, 3083, 5317, 5345, 3058, 5007,
4744, 3531, 5308, 5143, 3032, 4804, 4409, 1757, 4173, 3991,
2039, 3501, 3357, 1927, 4434, 3910, 1611, 3325, 3085, 1748,
3509, 3093, 1992, 4502, 4866, 2306, 5047, 5062, 2295, 5084,
4900, 2436, 5266, 5139, 2396, 4804, 4648, 1363, 3974, 3903,
1550, 3829, 3653, 1543, 4356, 4013, 1356, 3587, 3334, 1444,
3715, 3410, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 494, 518, 524, 917, 1786, 848, 912, 1092, 1191,
1343, 543, 882, 914, 1127, 1237, 649, 843, 926, 1084, 3714,
894, 1271, 1382, 1623, 1629, 570, 902, 1363, 1490, 1963,
528, 610, 715, 2079, NA, 857, 1139, 1147, 1278, 1325, 377,
1212, 1280, 1635, NA, 572, 613, 727, 1066, 2199, 976, 1025,
1089, 1304, 1311, 276, 1037, 1165, 1400, 1654, 524, 599,
624, 1059, 2345, 970, 1090, 1140, 1154, 1208, 470, 1139,
1267, 1359, 1583, 603, 614, 631, 939, 2360, 868, 1147, 1180,
1202, 1555, 868, 961, 1102, 1251, 1607, 772, 881, 925, 1269,
2408, 985, 1095, 1165, 1517, 1735, 402, 1019, 1445, 1583,
1720, 743, 779, 880, 1047, 2509, 916, 1179, 1190, 1406, 1441,
489, 904, 1374, 1483, 1817, 719, 722, 932, 974, 3129, 839,
1188, 1344, 1455, 1616, 524, 966, 1088, 1342, 2100, 764,
779, 876, 1048, 3263, 866, 1336, 1413, 1560, 1571, 570, 1038,
1446, 1499, 2051), Tissue = structure(c(2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 5L, 4L,
4L, 5L, 2L, 3L, 1L, 4L, 5L, 3L, 2L, 1L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 4L, 3L, 2L, 4L, 5L, 1L, 3L, 2L, 1L, 2L, 3L, 4L, 5L,
5L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 2L, 5L, 3L, 2L, 1L, 5L, 4L,
5L, 4L, 3L, 2L, 1L, 4L, 5L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 2L, 3L, 4L, 4L, 5L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 3L, 2L, 1L, 4L, 5L, 4L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
4L, 1L, 5L, 2L, 3L, 4L, 5L, 1L, 3L, 2L, 3L, 1L, 2L, 5L, 4L,
5L, 4L, 1L, 2L, 3L, 4L, 5L, 1L, 3L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 4L, 3L, 2L, 4L, 5L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 4L, 3L, 2L, 4L, 5L, 1L, 3L, 2L), .Label = c("Thymus",
"Spleen", "iLN", "Skin", "Colon"), class = "factor")), .Names = c("Experiment",
"Sample", "Genotype", "variable", "value", "Tissue"), row.names = c(19L,
23L, 30L, 71L, 75L, 82L, 123L, 127L, 134L, 175L, 179L, 186L,
227L, 231L, 238L, 279L, 283L, 290L, 331L, 335L, 342L, 383L, 387L,
394L, 435L, 439L, 446L, 487L, 491L, 498L, 539L, 543L, 550L, 591L,
595L, 602L, 643L, 647L, 654L, 695L, 699L, 706L, 747L, 751L, 758L,
799L, 803L, 810L, 851L, 855L, 862L, 903L, 907L, 914L, 955L, 959L,
966L, 1007L, 1011L, 1018L, 1049L, 1053L, 1055L, 1098L, 1102L,
1104L, 1147L, 1151L, 1153L, 1196L, 1200L, 1202L, 1245L, 1249L,
1251L, 1294L, 1298L, 1300L, 1343L, 1347L, 1349L, 1392L, 1396L,
1398L, 1441L, 1445L, 1447L, 1490L, 1494L, 1496L, 1589L, 1590L,
1591L, 1592L, 1593L, 1609L, 1610L, 1611L, 1612L, 1613L, 1629L,
1630L, 1631L, 1632L, 1633L, 1842L, 1843L, 1844L, 1845L, 1846L,
1862L, 1863L, 1864L, 1865L, 1866L, 1882L, 1883L, 1884L, 1885L,
1886L, 2095L, 2096L, 2097L, 2098L, 2099L, 2115L, 2116L, 2117L,
2118L, 2119L, 2135L, 2136L, 2137L, 2138L, 2139L, 2348L, 2349L,
2350L, 2351L, 2352L, 2368L, 2369L, 2370L, 2371L, 2372L, 2388L,
2389L, 2390L, 2391L, 2392L, 2601L, 2602L, 2603L, 2604L, 2605L,
2621L, 2622L, 2623L, 2624L, 2625L, 2641L, 2642L, 2643L, 2644L,
2645L, 2854L, 2855L, 2856L, 2857L, 2858L, 2874L, 2875L, 2876L,
2877L, 2878L, 2894L, 2895L, 2896L, 2897L, 2898L, 3107L, 3108L,
3109L, 3110L, 3111L, 3127L, 3128L, 3129L, 3130L, 3131L, 3147L,
3148L, 3149L, 3150L, 3151L, 3360L, 3361L, 3362L, 3363L, 3364L,
3380L, 3381L, 3382L, 3383L, 3384L, 3400L, 3401L, 3402L, 3403L,
3404L, 3613L, 3614L, 3615L, 3616L, 3617L, 3633L, 3634L, 3635L,
3636L, 3637L, 3653L, 3654L, 3655L, 3656L, 3657L, 3866L, 3867L,
3868L, 3869L, 3870L, 3886L, 3887L, 3888L, 3889L, 3890L, 3906L,
3907L, 3908L, 3909L, 3910L), class = "data.frame")
我想做的是将每个值标准化为每个组织和每个实验的各自 "miR-15/16 FL" 对照的平均值。
我尝试使用具有平均函数的 dplyr 来做到这一点
tissue_merge <- tissue_merge %>%
group_by(.dots = c("Experiment", "Tissue", "variable"), na.rm = T) %>%
mutate(value_norm = value/mean(value))
然而，这个函数只给我 "NA" 作为每个数字的 value_norm。
我意识到在我当前的代码中，我没有指定我只想使用 "miR-15/16 FL" 个样本的平均值，但我不知道如何将它合并到这个函数中（更不用说该函数甚至根本不提供值。
澄清一下：
对于给定的组织，说 "Spleen"，对于给定的变量，说 "MFI CD127 Foxp3- CD4+"，对于给定的实验，说“170911” 我想取每个值并将其除以"miR-15/16 FL" 个样本的平均值。这应该会导致每个实验的批归一化，这样当我汇集来自多个实验的数据时，一切都与每个实验中的 "miR-15/16 FL" 个样本有关。
对于"Spleen""MFI CD127 Foxp3- CD4+"，合并多个实验post归一化时归一化数据应该是这样的：
您可以一步完成所有操作，但为控制均值添加一列然后再将其取出会更清楚。
tissue_merge %>%
group_by(.dots = c("Experiment", "Tissue", "variable")) %>%
mutate(control_mean = mean(value[Genotype == "miR-15/16 FL"], na.rm = T),
value_norm = value / control_mean) %>%
select(-control_mean)
Demo 下面您可以看到，对于实验 170911 和组织脾脏，对于每个变量，MFI CD127 Foxp3+ CD4+、MFI CD127 Foxp3- CD4+ 和 MFI CD127 CD127 CD8+，control_mean 等于 Genotype == "miR-15/16 FL" 时该变量值的平均值，value_norm 是 value 除以 control_mean。
tissue_merge %>%
group_by(.dots = c("Experiment", "Tissue", "variable")) %>%
mutate(control_mean = mean(value[Genotype == "miR-15/16 FL"], na.rm = T),
value_norm = value / control_mean) %>%
filter(Experiment == 170911 & Tissue == "Spleen") %>%
arrange(variable) %>%
print.data.frame
# Experiment Sample Genotype variable value Tissue control_mean value_norm
# 1 170911 Spl_cKO_19 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3076 Spleen 1816.4 1.6934596
# 2 170911 Spl_cKO_21 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3083 Spleen 1816.4 1.6973134
# 3 170911 Spl_cKO_29 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3058 Spleen 1816.4 1.6835499
# 4 170911 Spl_cKO_37 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3531 Spleen 1816.4 1.9439551
# 5 170911 Spl_cKO_39 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3032 Spleen 1816.4 1.6692359
# 6 170911 Spl_FL_622 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1757 Spleen 1816.4 0.9672980
# 7 170911 Spl_FL_630 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 2039 Spleen 1816.4 1.1225501
# 8 170911 Spl_FL_631 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1927 Spleen 1816.4 1.0608897
# 9 170911 Spl_FL_635 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1611 Spleen 1816.4 0.8869192
# 10 170911 Spl_FL_638 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1748 Spleen 1816.4 0.9623431
# 11 170911 Spl_cKO_19 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 4718 Spleen 3788.4 1.2453806
# 12 170911 Spl_cKO_21 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 5317 Spleen 3788.4 1.4034949
# 13 170911 Spl_cKO_29 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 5007 Spleen 3788.4 1.3216661
# 14 170911 Spl_cKO_37 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 5308 Spleen 3788.4 1.4011192
# 15 170911 Spl_cKO_39 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 4804 Spleen 3788.4 1.2680815
# 16 170911 Spl_FL_622 miR-15/16 FL MFI CD127 Foxp3- CD4+ 4173 Spleen 3788.4 1.1015204
# 17 170911 Spl_FL_630 miR-15/16 FL MFI CD127 Foxp3- CD4+ 3501 Spleen 3788.4 0.9241368
# 18 170911 Spl_FL_631 miR-15/16 FL MFI CD127 Foxp3- CD4+ 4434 Spleen 3788.4 1.1704150
# 19 170911 Spl_FL_635 miR-15/16 FL MFI CD127 Foxp3- CD4+ 3325 Spleen 3788.4 0.8776792
# 20 170911 Spl_FL_638 miR-15/16 FL MFI CD127 Foxp3- CD4+ 3509 Spleen 3788.4 0.9262485
# 21 170911 Spl_cKO_19 miR-15/16 cKO MFI CD127 CD8+ 4987 Spleen 3487.2 1.4300872
# 22 170911 Spl_cKO_21 miR-15/16 cKO MFI CD127 CD8+ 5345 Spleen 3487.2 1.5327483
# 23 170911 Spl_cKO_29 miR-15/16 cKO MFI CD127 CD8+ 4744 Spleen 3487.2 1.3604038
# 24 170911 Spl_cKO_37 miR-15/16 cKO MFI CD127 CD8+ 5143 Spleen 3487.2 1.4748222
# 25 170911 Spl_cKO_39 miR-15/16 cKO MFI CD127 CD8+ 4409 Spleen 3487.2 1.2643382
# 26 170911 Spl_FL_622 miR-15/16 FL MFI CD127 CD8+ 3991 Spleen 3487.2 1.1444712
# 27 170911 Spl_FL_630 miR-15/16 FL MFI CD127 CD8+ 3357 Spleen 3487.2 0.9626635
# 28 170911 Spl_FL_631 miR-15/16 FL MFI CD127 CD8+ 3910 Spleen 3487.2 1.1212434
# 29 170911 Spl_FL_635 miR-15/16 FL MFI CD127 CD8+ 3085 Spleen 3487.2 0.8846639
# 30 170911 Spl_FL_638 miR-15/16 FL MFI CD127 CD8+ 3093 Spleen 3487.2 0.8869580
例如，对于MFI CD127 Foxp3- CD4+基因型，miR-15/16 FL值为4173、3501、4434、3325、3509，平均值是正确的。
mean(c(4173, 3501, 4434, 3325, 3509))
# [1] 3788.4
我有一个数据框：
tissue_merge <-structure(list(Experiment = c(170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170911L, 170911L, 170911L, 170911L, 170911L,
170911L, 170911L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L, 170918L, 170918L,
170918L, 170918L, 170918L, 170918L, 170918L), Sample = structure(c(11L,
11L, 11L, 12L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L,
15L, 16L, 16L, 16L, 17L, 17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L,
20L, 20L, 20L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L,
24L, 24L, 25L, 25L, 25L, 26L, 26L, 26L, 27L, 27L, 27L, 28L, 28L,
28L, 29L, 29L, 29L, 30L, 30L, 30L, 31L, 31L, 31L, 32L, 32L, 32L,
33L, 33L, 33L, 34L, 34L, 34L, 35L, 35L, 35L, 36L, 36L, 36L, 37L,
37L, 37L, 38L, 38L, 38L, 39L, 39L, 39L, 40L, 40L, 40L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L), .Label = c("1: FL_643", "10: cKO_657",
"2: FL_645", "3: FL_647", "4: FL_656", "5: FL_658", "6: cKO_644",
"7: cKO_646", "8: cKO_654", "9: cKO_655", "Spl_cKO_19", "Spl_cKO_21",
"Spl_cKO_29", "Spl_cKO_37", "Spl_cKO_39", "Spl_FL_622", "Spl_FL_630",
"Spl_FL_631", "Spl_FL_635", "Spl_FL_638", "iLN_cKO_19", "iLN_cKO_21",
"iLN_cKO_29", "iLN_cKO_37", "iLN_cKO_39", "iLN_FL_622", "iLN_FL_630",
"iLN_FL_631", "iLN_FL_635", "iLN_FL_638", "Thy_cKO_19", "Thy_cKO_21",
"Thy_cKO_29", "Thy_cKO_37", "Thy_cKO_39", "Thy_FL_622", "Thy_FL_630",
"Thy_FL_631", "Thy_FL_635", "Thy_FL_638"), class = "factor"),
Genotype = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("miR-15/16 FL", "miR-15/16 cKO"
), class = "factor"), variable = structure(c(1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L), .Label = c("MFI CD127 Foxp3+ CD4+",
"MFI CD127 Foxp3- CD4+", "MFI CD127 CD8+"), class = "factor"),
value = c(3076, 4718, 4987, 3083, 5317, 5345, 3058, 5007,
4744, 3531, 5308, 5143, 3032, 4804, 4409, 1757, 4173, 3991,
2039, 3501, 3357, 1927, 4434, 3910, 1611, 3325, 3085, 1748,
3509, 3093, 1992, 4502, 4866, 2306, 5047, 5062, 2295, 5084,
4900, 2436, 5266, 5139, 2396, 4804, 4648, 1363, 3974, 3903,
1550, 3829, 3653, 1543, 4356, 4013, 1356, 3587, 3334, 1444,
3715, 3410, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 494, 518, 524, 917, 1786, 848, 912, 1092, 1191,
1343, 543, 882, 914, 1127, 1237, 649, 843, 926, 1084, 3714,
894, 1271, 1382, 1623, 1629, 570, 902, 1363, 1490, 1963,
528, 610, 715, 2079, NA, 857, 1139, 1147, 1278, 1325, 377,
1212, 1280, 1635, NA, 572, 613, 727, 1066, 2199, 976, 1025,
1089, 1304, 1311, 276, 1037, 1165, 1400, 1654, 524, 599,
624, 1059, 2345, 970, 1090, 1140, 1154, 1208, 470, 1139,
1267, 1359, 1583, 603, 614, 631, 939, 2360, 868, 1147, 1180,
1202, 1555, 868, 961, 1102, 1251, 1607, 772, 881, 925, 1269,
2408, 985, 1095, 1165, 1517, 1735, 402, 1019, 1445, 1583,
1720, 743, 779, 880, 1047, 2509, 916, 1179, 1190, 1406, 1441,
489, 904, 1374, 1483, 1817, 719, 722, 932, 974, 3129, 839,
1188, 1344, 1455, 1616, 524, 966, 1088, 1342, 2100, 764,
779, 876, 1048, 3263, 866, 1336, 1413, 1560, 1571, 570, 1038,
1446, 1499, 2051), Tissue = structure(c(2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 5L, 4L,
4L, 5L, 2L, 3L, 1L, 4L, 5L, 3L, 2L, 1L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 4L, 3L, 2L, 4L, 5L, 1L, 3L, 2L, 1L, 2L, 3L, 4L, 5L,
5L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 2L, 5L, 3L, 2L, 1L, 5L, 4L,
5L, 4L, 3L, 2L, 1L, 4L, 5L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 2L, 3L, 4L, 4L, 5L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 3L, 2L, 1L, 4L, 5L, 4L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
4L, 1L, 5L, 2L, 3L, 4L, 5L, 1L, 3L, 2L, 3L, 1L, 2L, 5L, 4L,
5L, 4L, 1L, 2L, 3L, 4L, 5L, 1L, 3L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 4L, 3L, 2L, 4L, 5L, 3L, 1L, 2L, 1L, 3L, 2L, 5L, 4L,
5L, 1L, 4L, 3L, 2L, 4L, 5L, 1L, 3L, 2L), .Label = c("Thymus",
"Spleen", "iLN", "Skin", "Colon"), class = "factor")), .Names = c("Experiment",
"Sample", "Genotype", "variable", "value", "Tissue"), row.names = c(19L,
23L, 30L, 71L, 75L, 82L, 123L, 127L, 134L, 175L, 179L, 186L,
227L, 231L, 238L, 279L, 283L, 290L, 331L, 335L, 342L, 383L, 387L,
394L, 435L, 439L, 446L, 487L, 491L, 498L, 539L, 543L, 550L, 591L,
595L, 602L, 643L, 647L, 654L, 695L, 699L, 706L, 747L, 751L, 758L,
799L, 803L, 810L, 851L, 855L, 862L, 903L, 907L, 914L, 955L, 959L,
966L, 1007L, 1011L, 1018L, 1049L, 1053L, 1055L, 1098L, 1102L,
1104L, 1147L, 1151L, 1153L, 1196L, 1200L, 1202L, 1245L, 1249L,
1251L, 1294L, 1298L, 1300L, 1343L, 1347L, 1349L, 1392L, 1396L,
1398L, 1441L, 1445L, 1447L, 1490L, 1494L, 1496L, 1589L, 1590L,
1591L, 1592L, 1593L, 1609L, 1610L, 1611L, 1612L, 1613L, 1629L,
1630L, 1631L, 1632L, 1633L, 1842L, 1843L, 1844L, 1845L, 1846L,
1862L, 1863L, 1864L, 1865L, 1866L, 1882L, 1883L, 1884L, 1885L,
1886L, 2095L, 2096L, 2097L, 2098L, 2099L, 2115L, 2116L, 2117L,
2118L, 2119L, 2135L, 2136L, 2137L, 2138L, 2139L, 2348L, 2349L,
2350L, 2351L, 2352L, 2368L, 2369L, 2370L, 2371L, 2372L, 2388L,
2389L, 2390L, 2391L, 2392L, 2601L, 2602L, 2603L, 2604L, 2605L,
2621L, 2622L, 2623L, 2624L, 2625L, 2641L, 2642L, 2643L, 2644L,
2645L, 2854L, 2855L, 2856L, 2857L, 2858L, 2874L, 2875L, 2876L,
2877L, 2878L, 2894L, 2895L, 2896L, 2897L, 2898L, 3107L, 3108L,
3109L, 3110L, 3111L, 3127L, 3128L, 3129L, 3130L, 3131L, 3147L,
3148L, 3149L, 3150L, 3151L, 3360L, 3361L, 3362L, 3363L, 3364L,
3380L, 3381L, 3382L, 3383L, 3384L, 3400L, 3401L, 3402L, 3403L,
3404L, 3613L, 3614L, 3615L, 3616L, 3617L, 3633L, 3634L, 3635L,
3636L, 3637L, 3653L, 3654L, 3655L, 3656L, 3657L, 3866L, 3867L,
3868L, 3869L, 3870L, 3886L, 3887L, 3888L, 3889L, 3890L, 3906L,
3907L, 3908L, 3909L, 3910L), class = "data.frame")
我想做的是将每个值标准化为每个组织和每个实验的各自 "miR-15/16 FL" 对照的平均值。
我尝试使用具有平均函数的 dplyr 来做到这一点
tissue_merge <- tissue_merge %>%
group_by(.dots = c("Experiment", "Tissue", "variable"), na.rm = T) %>%
mutate(value_norm = value/mean(value))
然而，这个函数只给我 "NA" 作为每个数字的 value_norm。
我意识到在我当前的代码中，我没有指定我只想使用 "miR-15/16 FL" 个样本的平均值，但我不知道如何将它合并到这个函数中（更不用说该函数甚至根本不提供值。
澄清一下：
对于给定的组织，说 "Spleen"，对于给定的变量，说 "MFI CD127 Foxp3- CD4+"，对于给定的实验，说“170911” 我想取每个值并将其除以"miR-15/16 FL" 个样本的平均值。这应该会导致每个实验的批归一化，这样当我汇集来自多个实验的数据时，一切都与每个实验中的 "miR-15/16 FL" 个样本有关。
对于"Spleen""MFI CD127 Foxp3- CD4+"，合并多个实验post归一化时归一化数据应该是这样的：
您可以一步完成所有操作，但为控制均值添加一列然后再将其取出会更清楚。
tissue_merge %>%
group_by(.dots = c("Experiment", "Tissue", "variable")) %>%
mutate(control_mean = mean(value[Genotype == "miR-15/16 FL"], na.rm = T),
value_norm = value / control_mean) %>%
select(-control_mean)
Demo 下面您可以看到，对于实验 170911 和组织脾脏，对于每个变量，MFI CD127 Foxp3+ CD4+、MFI CD127 Foxp3- CD4+ 和 MFI CD127 CD127 CD8+，control_mean 等于 Genotype == "miR-15/16 FL" 时该变量值的平均值，value_norm 是 value 除以 control_mean。
tissue_merge %>%
group_by(.dots = c("Experiment", "Tissue", "variable")) %>%
mutate(control_mean = mean(value[Genotype == "miR-15/16 FL"], na.rm = T),
value_norm = value / control_mean) %>%
filter(Experiment == 170911 & Tissue == "Spleen") %>%
arrange(variable) %>%
print.data.frame
# Experiment Sample Genotype variable value Tissue control_mean value_norm
# 1 170911 Spl_cKO_19 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3076 Spleen 1816.4 1.6934596
# 2 170911 Spl_cKO_21 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3083 Spleen 1816.4 1.6973134
# 3 170911 Spl_cKO_29 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3058 Spleen 1816.4 1.6835499
# 4 170911 Spl_cKO_37 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3531 Spleen 1816.4 1.9439551
# 5 170911 Spl_cKO_39 miR-15/16 cKO MFI CD127 Foxp3+ CD4+ 3032 Spleen 1816.4 1.6692359
# 6 170911 Spl_FL_622 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1757 Spleen 1816.4 0.9672980
# 7 170911 Spl_FL_630 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 2039 Spleen 1816.4 1.1225501
# 8 170911 Spl_FL_631 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1927 Spleen 1816.4 1.0608897
# 9 170911 Spl_FL_635 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1611 Spleen 1816.4 0.8869192
# 10 170911 Spl_FL_638 miR-15/16 FL MFI CD127 Foxp3+ CD4+ 1748 Spleen 1816.4 0.9623431
# 11 170911 Spl_cKO_19 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 4718 Spleen 3788.4 1.2453806
# 12 170911 Spl_cKO_21 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 5317 Spleen 3788.4 1.4034949
# 13 170911 Spl_cKO_29 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 5007 Spleen 3788.4 1.3216661
# 14 170911 Spl_cKO_37 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 5308 Spleen 3788.4 1.4011192
# 15 170911 Spl_cKO_39 miR-15/16 cKO MFI CD127 Foxp3- CD4+ 4804 Spleen 3788.4 1.2680815
# 16 170911 Spl_FL_622 miR-15/16 FL MFI CD127 Foxp3- CD4+ 4173 Spleen 3788.4 1.1015204
# 17 170911 Spl_FL_630 miR-15/16 FL MFI CD127 Foxp3- CD4+ 3501 Spleen 3788.4 0.9241368
# 18 170911 Spl_FL_631 miR-15/16 FL MFI CD127 Foxp3- CD4+ 4434 Spleen 3788.4 1.1704150
# 19 170911 Spl_FL_635 miR-15/16 FL MFI CD127 Foxp3- CD4+ 3325 Spleen 3788.4 0.8776792
# 20 170911 Spl_FL_638 miR-15/16 FL MFI CD127 Foxp3- CD4+ 3509 Spleen 3788.4 0.9262485
# 21 170911 Spl_cKO_19 miR-15/16 cKO MFI CD127 CD8+ 4987 Spleen 3487.2 1.4300872
# 22 170911 Spl_cKO_21 miR-15/16 cKO MFI CD127 CD8+ 5345 Spleen 3487.2 1.5327483
# 23 170911 Spl_cKO_29 miR-15/16 cKO MFI CD127 CD8+ 4744 Spleen 3487.2 1.3604038
# 24 170911 Spl_cKO_37 miR-15/16 cKO MFI CD127 CD8+ 5143 Spleen 3487.2 1.4748222
# 25 170911 Spl_cKO_39 miR-15/16 cKO MFI CD127 CD8+ 4409 Spleen 3487.2 1.2643382
# 26 170911 Spl_FL_622 miR-15/16 FL MFI CD127 CD8+ 3991 Spleen 3487.2 1.1444712
# 27 170911 Spl_FL_630 miR-15/16 FL MFI CD127 CD8+ 3357 Spleen 3487.2 0.9626635
# 28 170911 Spl_FL_631 miR-15/16 FL MFI CD127 CD8+ 3910 Spleen 3487.2 1.1212434
# 29 170911 Spl_FL_635 miR-15/16 FL MFI CD127 CD8+ 3085 Spleen 3487.2 0.8846639
# 30 170911 Spl_FL_638 miR-15/16 FL MFI CD127 CD8+ 3093 Spleen 3487.2 0.8869580
例如，对于MFI CD127 Foxp3- CD4+基因型，miR-15/16 FL值为4173、3501、4434、3325、3509，平均值是正确的。
mean(c(4173, 3501, 4434, 3325, 3509))
# [1] 3788.4