# 分位数标准化

quantile normalization 原理：

A quick illustration of such normalizing on a very small dataset:

Arrays 1 to 3, genes A to D

A    5    4    3
B    2    1    4
C    3    4    6
D    4    2    8


For each column determine a rank from lowest to highest and assign number i-iv

A    iv    iii   i
B    i     i     ii
C    ii    iii   iii
D    iii   ii    iv


These rank values are set aside to use later. Go back to the first set of data. Rearrange that first set of column values so each column is in order going lowest to highest value. (First column consists of 5,2,3,4. This is rearranged to 2,3,4,5. Second Column 4,1,4,2 is rearranged to 1,2,4,4, and column 3 consisting of 3,4,6,8 stays the same because it is already in order from lowest to highest value.) The result is:

A    5    4    3    becomes A 2 1 3
B    2    1    4    becomes B 3 2 4
C    3    4    6    becomes C 4 4 6
D    4    2    8    becomes D 5 4 8


Now find the mean for each row to determine the ranks

A (2 1 3)/3 = 2.00 = rank i
B (3 2 4)/3 = 3.00 = rank ii
C (4 4 6)/3 = 4.67 = rank iii
D (5 4 8)/3 = 5.67 = rank iv


Now take the ranking order and substitute in new values

A    iv    iii   i
B    i     i     ii
C    ii    iii   iii
D    iii   ii    iv


becomes:

A    5.67    4.67    2.00
B    2.00    2.00    3.00
C    3.00    4.67    4.67
D    4.67    3.00    5.67R实现方法：

1：affy
2: preprocessCore

> a<-matrix(1:6,3,2)
> a
[,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6

> library(preprocessCore)
> b=normalize.quantiles(a)
> b
[,1] [,2]
[1,]  2.5  2.5
[2,]  3.5  3.5
[3,]  4.5  4.5

posted @ 2016-11-10 17:49  史迪仔_lmj  Views(3802)  Comments(3Edit  收藏  举报