R：如何对数据进行分组并在数据框中的不同组内分配因子水平？
R: How to group data and assign factor levels within different groups in a dataframe?
structure(list(drug = c("Chlorambucil", "Fludarabine", "FludarabineMafosfamide",
"NDI031301", "CMPB", "Tofacitinib", "Peficitinib", "FludarabineMafosfamide",
"PDB", "Filgotinib", "Dexamethasone", "CMPA", "Lenalidomide",
"Dexamethasone", "Gandotinib", "NDI031301", "Filgotinib", "PDB",
"CMPB", "Ruxolitinib", "CC122", "Atovaquone", "CC122", "SAR20347",
"Momelotinib", "Momelotinib", "Tofacitinib", "Fludarabine", "Fludarabine",
"Cerdulatinib", "Lenalidomide", "Atovaquone", "Chlorambucil",
"CMPA", "FludarabineMafosfamide", "FludarabineMafosfamide", "Fludarabine",
"Atovaquone", "Momelotinib", "PDB", "Filgotinib", "Chlorambucil",
"Dexamethasone", "Tofacitinib", "SAR20347", "CMPB", "Momelotinib",
"Fludarabine", "Cerdulatinib", "Peficitinib", "Atovaquone", "CC122",
"CMPA", "NDI031301", "PDB", "CMPA", "Lenalidomide", "SAR20347",
"Tofacitinib", "Gandotinib", "Lenalidomide", "Peficitinib", "CMPB",
"CC122", "Dexamethasone", "FludarabineMafosfamide", "Ruxolitinib",
"CMPB", "Peficitinib", "Tofacitinib", "FludarabineMafosfamide",
"Filgotinib", "Dexamethasone", "CMPA", "Dexamethasone", "Gandotinib",
"NDI031301", "Filgotinib", "SAR20347", "CMPB", "Ruxolitinib",
"Peficitinib", "Atovaquone", "CC122", "SAR20347", "Momelotinib",
"Momelotinib", "Tofacitinib", "Fludarabine", "Fludarabine", "Cerdulatinib",
"Atovaquone", "Chlorambucil", "CMPA", "NDI031301"), dose = c(1,
1, 10, 1, 0.1, 1, 1, 1, 100, 1, 10, 1, 10, 100, 1, 10, 10, 10,
1, 1, 0.1, 3, 1, 1, 1, 0.1, 10, 1, 10, 1, 1, 30, 30, 0.1, 0.01,
0.1, 0.01, 0.3, 0.001, 1, 0.01, 0.3, 0.1, 0.01, 0.1, 0.001, 0.01,
0.1, 0.01, 0.1, 0.03, 0.01, 0.01, 0.01, 0.1, 0.001, 0.01, 0.01,
0.1, 0.01, 0.1, 0.01, 0.01, 0.001, 1, 10, 10, 0.1, 1, 1, 1, 1,
10, 1, 100, 1, 10, 10, 10, 1, 1, 10, 3, 1, 1, 1, 0.1, 10, 10,
1, 1, 30, 30, 0.1, 1), drug.dose = c("Chlorambucil_1uM", "Fludarabine_1uM",
"FludarabineMafosfamide_10ug/mlplus1ug/ml", "NDI031301_1uM",
"CMPB_0.1uM", "Tofacitinib_1uM", "Peficitinib_1uM", "FludarabineMafosfamide_1ug/mlplus1ug/ml",
"PDB_100ng/ml", "Filgotinib_1uM", "Dexamethasone_10uM", "CMPA_1uM",
"Lenalidomide_10uM", "Dexamethasone_100uM", "Gandotinib_1uM",
"NDI031301_10uM", "Filgotinib_10uM", "PDB_10ng/ml", "CMPB_1uM",
"Ruxolitinib_1uM", "CC122_0.1uM", "Atovaquone_3uM", "CC122_1uM",
"SAR20347_1uM", "Momelotinib_1uM", "Momelotinib_0.1uM", "Tofacitinib_10uM",
"Fludarabine_1ug/ml", "Fludarabine_10ug/ml", "Cerdulatinib_1uM",
"Lenalidomide_1uM", "Atovaquone_30uM", "Chlorambucil_30uM", "CMPA_0.1uM",
"FludarabineMafosfamide_0.01ug/mlplus1ug/ml", "FludarabineMafosfamide_0.1ug/mlplus1ug/ml",
"Fludarabine_0.01ug/ml", "Atovaquone_0.3uM", "Momelotinib_0.001uM",
"PDB_1ng/ml", "Filgotinib_0.01uM", "Chlorambucil_0.3uM", "Dexamethasone_0.1uM",
"Tofacitinib_0.01uM", "SAR20347_0.1uM", "CMPB_0.001uM", "Momelotinib_0.01uM",
"Fludarabine_0.1ug/ml", "Cerdulatinib_0.01uM", "Peficitinib_0.1uM",
"Atovaquone_0.03uM", "CC122_0.01uM", "CMPA_0.01uM", "NDI031301_0.01uM",
"PDB_0.1ng/ml", "CMPA_0.001uM", "Lenalidomide_0.01uM", "SAR20347_0.01uM",
"Tofacitinib_0.1uM", "Gandotinib_0.01uM", "Lenalidomide_0.1uM",
"Peficitinib_0.01uM", "CMPB_0.01uM", "CC122_0.001uM", "Dexamethasone_1uM",
"FludarabineMafosfamide_10ug/mlplus1ug/ml", "Ruxolitinib_10uM",
"CMPB_0.1uM", "Peficitinib_1uM", "Tofacitinib_1uM", "FludarabineMafosfamide_1ug/mlplus1ug/ml",
"Filgotinib_1uM", "Dexamethasone_10uM", "CMPA_1uM", "Dexamethasone_100uM",
"Gandotinib_1uM", "NDI031301_10uM", "Filgotinib_10uM", "SAR20347_10uM",
"CMPB_1uM", "Ruxolitinib_1uM", "Peficitinib_10uM", "Atovaquone_3uM",
"CC122_1uM", "SAR20347_1uM", "Momelotinib_1uM", "Momelotinib_0.1uM",
"Tofacitinib_10uM", "Fludarabine_10ug/ml", "Fludarabine_1ug/ml",
"Cerdulatinib_1uM", "Atovaquone_30uM", "Chlorambucil_30uM", "CMPA_0.1uM",
"NDI031301_1uM"), combo = c("none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none"), cluster = c(3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 5L, 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, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L), dosage = c("1uM", "1uM", "10ug/mlplus1ug/ml",
"1uM", "0.1uM", "1uM", "1uM", "1ug/mlplus1ug/ml", "100ng/ml",
"1uM", "10uM", "1uM", "10uM", "100uM", "1uM", "10uM", "10uM",
"10ng/ml", "1uM", "1uM", "0.1uM", "3uM", "1uM", "1uM", "1uM",
"0.1uM", "10uM", "1ug/ml", "10ug/ml", "1uM", "1uM", "30uM", "30uM",
"0.1uM", "0.01ug/mlplus1ug/ml", "0.1ug/mlplus1ug/ml", "0.01ug/ml",
"0.3uM", "0.001uM", "1ng/ml", "0.01uM", "0.3uM", "0.1uM", "0.01uM",
"0.1uM", "0.001uM", "0.01uM", "0.1ug/ml", "0.01uM", "0.1uM",
"0.03uM", "0.01uM", "0.01uM", "0.01uM", "0.1ng/ml", "0.001uM",
"0.01uM", "0.01uM", "0.1uM", "0.01uM", "0.1uM", "0.01uM", "0.01uM",
"0.001uM", "1uM", "10ug/mlplus1ug/ml", "10uM", "0.1uM", "1uM",
"1uM", "1ug/mlplus1ug/ml", "1uM", "10uM", "1uM", "100uM", "1uM",
"10uM", "10uM", "10uM", "1uM", "1uM", "10uM", "3uM", "1uM", "1uM",
"1uM", "0.1uM", "10uM", "10ug/ml", "1ug/ml", "1uM", "30uM", "30uM",
"0.1uM", "1uM")), row.names = c(NA, -95L), class = "data.frame")
抱歉菜鸟问题，我有这个复杂的药物集群数据，如屏幕截图所示。
我想将它们显示为堆叠式 geom_col 类型的绘图，x 轴为“药物”，Y 轴为出现次数，并按簇排列。
到目前为止还很简单。但我也想通过使用颜色填充来匹配它们的剂量来查看这些药物和剂量在每个集群中的分布。实际剂量有不同的单位等
我将数字剂量提取到它自己的常设列中。我想指定一个因子向量（“最小”、“低”、“高”、“最大”）来反映剂量水平，因为我知道每种药物有 4 种不同的剂量。
问题是不同药物的数字剂量不同，所以我不能简单地使用等级
例如一些药物剂量范围从0.03到30，一些等级从0.3到300，还有一些范围从0.01到10。
那么我如何使用该数字药物剂量列为每种药物分配药物水平？
这是一种使用 rank() 和连接的方法。我们可以利用以下事实，即每种药物在 种药物中具有相同的单位 。
library(dplyr)
df %>%
arrange(drug) %>% #for visualization
group_by(drug) %>% #group by drug
select(dose) %>% #get rid of extra columns
filter(!duplicated(dose)) %>% #remove duplicates
mutate(rank = rank(dose), #rank doses, mostly for visualization of results
category = c("min","low","high","max")[rank]) #assign category
# A tibble: 67 x 4
# Groups: drug [19]
drug dose rank category
<chr> <dbl> <dbl> <chr>
1 Atovaquone 3 3 high
2 Atovaquone 30 4 max
3 Atovaquone 0.3 2 low
4 Atovaquone 0.03 1 min
5 CC122 0.1 3 high
6 CC122 1 4 max
7 CC122 0.01 2 low
8 CC122 0.001 1 min
9 Cerdulatinib 1 2 low
10 Cerdulatinib 0.01 1 min
# … with 57 more rows
现在我们可以加入回原来的data.frame:
df %>%
arrange(drug) %>%
group_by(drug) %>%
select(dose) %>%
filter(!duplicated(dose)) %>%
mutate(rank = rank(dose), #rank doses
category = c("min","low","high","max")[rank]) %>%
right_join(df)
# A tibble: 95 x 8
# Groups: drug [19]
drug dose dosage rank category drug.dose combo cluster
<chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <int>
1 Atovaquone 3 3uM 3 high Atovaquone_3uM none 4
2 Atovaquone 3 3uM 3 high Atovaquone_3uM none 6
3 Atovaquone 30 30uM 4 max Atovaquone_30uM none 4
4 Atovaquone 30 30uM 4 max Atovaquone_30uM none 6
5 Atovaquone 0.3 0.3uM 2 low Atovaquone_0.3uM none 5
6 Atovaquone 0.03 0.03uM 1 min Atovaquone_0.03uM none 5
7 CC122 0.1 0.1uM 3 high CC122_0.1uM none 4
8 CC122 1 1uM 4 max CC122_1uM none 4
9 CC122 1 1uM 4 max CC122_1uM none 6
10 CC122 0.01 0.01uM 2 low CC122_0.01uM none 5
# … with 85 more rows
structure(list(drug = c("Chlorambucil", "Fludarabine", "FludarabineMafosfamide",
"NDI031301", "CMPB", "Tofacitinib", "Peficitinib", "FludarabineMafosfamide",
"PDB", "Filgotinib", "Dexamethasone", "CMPA", "Lenalidomide",
"Dexamethasone", "Gandotinib", "NDI031301", "Filgotinib", "PDB",
"CMPB", "Ruxolitinib", "CC122", "Atovaquone", "CC122", "SAR20347",
"Momelotinib", "Momelotinib", "Tofacitinib", "Fludarabine", "Fludarabine",
"Cerdulatinib", "Lenalidomide", "Atovaquone", "Chlorambucil",
"CMPA", "FludarabineMafosfamide", "FludarabineMafosfamide", "Fludarabine",
"Atovaquone", "Momelotinib", "PDB", "Filgotinib", "Chlorambucil",
"Dexamethasone", "Tofacitinib", "SAR20347", "CMPB", "Momelotinib",
"Fludarabine", "Cerdulatinib", "Peficitinib", "Atovaquone", "CC122",
"CMPA", "NDI031301", "PDB", "CMPA", "Lenalidomide", "SAR20347",
"Tofacitinib", "Gandotinib", "Lenalidomide", "Peficitinib", "CMPB",
"CC122", "Dexamethasone", "FludarabineMafosfamide", "Ruxolitinib",
"CMPB", "Peficitinib", "Tofacitinib", "FludarabineMafosfamide",
"Filgotinib", "Dexamethasone", "CMPA", "Dexamethasone", "Gandotinib",
"NDI031301", "Filgotinib", "SAR20347", "CMPB", "Ruxolitinib",
"Peficitinib", "Atovaquone", "CC122", "SAR20347", "Momelotinib",
"Momelotinib", "Tofacitinib", "Fludarabine", "Fludarabine", "Cerdulatinib",
"Atovaquone", "Chlorambucil", "CMPA", "NDI031301"), dose = c(1,
1, 10, 1, 0.1, 1, 1, 1, 100, 1, 10, 1, 10, 100, 1, 10, 10, 10,
1, 1, 0.1, 3, 1, 1, 1, 0.1, 10, 1, 10, 1, 1, 30, 30, 0.1, 0.01,
0.1, 0.01, 0.3, 0.001, 1, 0.01, 0.3, 0.1, 0.01, 0.1, 0.001, 0.01,
0.1, 0.01, 0.1, 0.03, 0.01, 0.01, 0.01, 0.1, 0.001, 0.01, 0.01,
0.1, 0.01, 0.1, 0.01, 0.01, 0.001, 1, 10, 10, 0.1, 1, 1, 1, 1,
10, 1, 100, 1, 10, 10, 10, 1, 1, 10, 3, 1, 1, 1, 0.1, 10, 10,
1, 1, 30, 30, 0.1, 1), drug.dose = c("Chlorambucil_1uM", "Fludarabine_1uM",
"FludarabineMafosfamide_10ug/mlplus1ug/ml", "NDI031301_1uM",
"CMPB_0.1uM", "Tofacitinib_1uM", "Peficitinib_1uM", "FludarabineMafosfamide_1ug/mlplus1ug/ml",
"PDB_100ng/ml", "Filgotinib_1uM", "Dexamethasone_10uM", "CMPA_1uM",
"Lenalidomide_10uM", "Dexamethasone_100uM", "Gandotinib_1uM",
"NDI031301_10uM", "Filgotinib_10uM", "PDB_10ng/ml", "CMPB_1uM",
"Ruxolitinib_1uM", "CC122_0.1uM", "Atovaquone_3uM", "CC122_1uM",
"SAR20347_1uM", "Momelotinib_1uM", "Momelotinib_0.1uM", "Tofacitinib_10uM",
"Fludarabine_1ug/ml", "Fludarabine_10ug/ml", "Cerdulatinib_1uM",
"Lenalidomide_1uM", "Atovaquone_30uM", "Chlorambucil_30uM", "CMPA_0.1uM",
"FludarabineMafosfamide_0.01ug/mlplus1ug/ml", "FludarabineMafosfamide_0.1ug/mlplus1ug/ml",
"Fludarabine_0.01ug/ml", "Atovaquone_0.3uM", "Momelotinib_0.001uM",
"PDB_1ng/ml", "Filgotinib_0.01uM", "Chlorambucil_0.3uM", "Dexamethasone_0.1uM",
"Tofacitinib_0.01uM", "SAR20347_0.1uM", "CMPB_0.001uM", "Momelotinib_0.01uM",
"Fludarabine_0.1ug/ml", "Cerdulatinib_0.01uM", "Peficitinib_0.1uM",
"Atovaquone_0.03uM", "CC122_0.01uM", "CMPA_0.01uM", "NDI031301_0.01uM",
"PDB_0.1ng/ml", "CMPA_0.001uM", "Lenalidomide_0.01uM", "SAR20347_0.01uM",
"Tofacitinib_0.1uM", "Gandotinib_0.01uM", "Lenalidomide_0.1uM",
"Peficitinib_0.01uM", "CMPB_0.01uM", "CC122_0.001uM", "Dexamethasone_1uM",
"FludarabineMafosfamide_10ug/mlplus1ug/ml", "Ruxolitinib_10uM",
"CMPB_0.1uM", "Peficitinib_1uM", "Tofacitinib_1uM", "FludarabineMafosfamide_1ug/mlplus1ug/ml",
"Filgotinib_1uM", "Dexamethasone_10uM", "CMPA_1uM", "Dexamethasone_100uM",
"Gandotinib_1uM", "NDI031301_10uM", "Filgotinib_10uM", "SAR20347_10uM",
"CMPB_1uM", "Ruxolitinib_1uM", "Peficitinib_10uM", "Atovaquone_3uM",
"CC122_1uM", "SAR20347_1uM", "Momelotinib_1uM", "Momelotinib_0.1uM",
"Tofacitinib_10uM", "Fludarabine_10ug/ml", "Fludarabine_1ug/ml",
"Cerdulatinib_1uM", "Atovaquone_30uM", "Chlorambucil_30uM", "CMPA_0.1uM",
"NDI031301_1uM"), combo = c("none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none", "none", "none", "none", "none", "none", "none",
"none", "none"), cluster = c(3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 5L, 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, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L), dosage = c("1uM", "1uM", "10ug/mlplus1ug/ml",
"1uM", "0.1uM", "1uM", "1uM", "1ug/mlplus1ug/ml", "100ng/ml",
"1uM", "10uM", "1uM", "10uM", "100uM", "1uM", "10uM", "10uM",
"10ng/ml", "1uM", "1uM", "0.1uM", "3uM", "1uM", "1uM", "1uM",
"0.1uM", "10uM", "1ug/ml", "10ug/ml", "1uM", "1uM", "30uM", "30uM",
"0.1uM", "0.01ug/mlplus1ug/ml", "0.1ug/mlplus1ug/ml", "0.01ug/ml",
"0.3uM", "0.001uM", "1ng/ml", "0.01uM", "0.3uM", "0.1uM", "0.01uM",
"0.1uM", "0.001uM", "0.01uM", "0.1ug/ml", "0.01uM", "0.1uM",
"0.03uM", "0.01uM", "0.01uM", "0.01uM", "0.1ng/ml", "0.001uM",
"0.01uM", "0.01uM", "0.1uM", "0.01uM", "0.1uM", "0.01uM", "0.01uM",
"0.001uM", "1uM", "10ug/mlplus1ug/ml", "10uM", "0.1uM", "1uM",
"1uM", "1ug/mlplus1ug/ml", "1uM", "10uM", "1uM", "100uM", "1uM",
"10uM", "10uM", "10uM", "1uM", "1uM", "10uM", "3uM", "1uM", "1uM",
"1uM", "0.1uM", "10uM", "10ug/ml", "1ug/ml", "1uM", "30uM", "30uM",
"0.1uM", "1uM")), row.names = c(NA, -95L), class = "data.frame")
抱歉菜鸟问题，我有这个复杂的药物集群数据，如屏幕截图所示。
我想将它们显示为堆叠式 geom_col 类型的绘图，x 轴为“药物”，Y 轴为出现次数，并按簇排列。
到目前为止还很简单。但我也想通过使用颜色填充来匹配它们的剂量来查看这些药物和剂量在每个集群中的分布。实际剂量有不同的单位等
我将数字剂量提取到它自己的常设列中。我想指定一个因子向量（“最小”、“低”、“高”、“最大”）来反映剂量水平，因为我知道每种药物有 4 种不同的剂量。
问题是不同药物的数字剂量不同，所以我不能简单地使用等级
例如一些药物剂量范围从0.03到30，一些等级从0.3到300，还有一些范围从0.01到10。
那么我如何使用该数字药物剂量列为每种药物分配药物水平？
这是一种使用 rank() 和连接的方法。我们可以利用以下事实，即每种药物在 种药物中具有相同的单位 。
library(dplyr)
df %>%
arrange(drug) %>% #for visualization
group_by(drug) %>% #group by drug
select(dose) %>% #get rid of extra columns
filter(!duplicated(dose)) %>% #remove duplicates
mutate(rank = rank(dose), #rank doses, mostly for visualization of results
category = c("min","low","high","max")[rank]) #assign category
# A tibble: 67 x 4
# Groups: drug [19]
drug dose rank category
<chr> <dbl> <dbl> <chr>
1 Atovaquone 3 3 high
2 Atovaquone 30 4 max
3 Atovaquone 0.3 2 low
4 Atovaquone 0.03 1 min
5 CC122 0.1 3 high
6 CC122 1 4 max
7 CC122 0.01 2 low
8 CC122 0.001 1 min
9 Cerdulatinib 1 2 low
10 Cerdulatinib 0.01 1 min
# … with 57 more rows
现在我们可以加入回原来的data.frame:
df %>%
arrange(drug) %>%
group_by(drug) %>%
select(dose) %>%
filter(!duplicated(dose)) %>%
mutate(rank = rank(dose), #rank doses
category = c("min","low","high","max")[rank]) %>%
right_join(df)
# A tibble: 95 x 8
# Groups: drug [19]
drug dose dosage rank category drug.dose combo cluster
<chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <int>
1 Atovaquone 3 3uM 3 high Atovaquone_3uM none 4
2 Atovaquone 3 3uM 3 high Atovaquone_3uM none 6
3 Atovaquone 30 30uM 4 max Atovaquone_30uM none 4
4 Atovaquone 30 30uM 4 max Atovaquone_30uM none 6
5 Atovaquone 0.3 0.3uM 2 low Atovaquone_0.3uM none 5
6 Atovaquone 0.03 0.03uM 1 min Atovaquone_0.03uM none 5
7 CC122 0.1 0.1uM 3 high CC122_0.1uM none 4
8 CC122 1 1uM 4 max CC122_1uM none 4
9 CC122 1 1uM 4 max CC122_1uM none 6
10 CC122 0.01 0.01uM 2 low CC122_0.01uM none 5
# … with 85 more rows