fisherGOProfiles(goProfiles)
fisherGOProfiles()所属R语言包：goProfiles
GO Class-by-class Fisher tests in lists of genes characterized by their functional profiles
特点是其功能的配置文件列出的基因类按类费舍尔测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given two lists of genes, both characterized by their frequencies of annotations (or "hits") in the same set of GO nodes (also designated as GO terms or GO classes), for each node determine if the annotation frequencies depart from what is expected by chance. The annotation frequencies are specified in the "GO profiles" arguments pn, qm and pn. Both samples may share a common subsample of genes, with GO profile pqn0. The analysis is based on the Fisher's exact test, as is implemented by fisher.test R function, followed by p-value adjustment for multitesting based on function p.adjust. Usually, this function will be called after a significant result on compareGOProfiles which performs global (all GO nodes simultaneously) profile comparisons (with better
由于每个节点两个列表的基因，其频率在同一组的好节点（也被指定为GO术语“或”类）（或“点击”）注释特点，确定是否标注频率是从什么出发预计机会。注释中的“GO型材”参数指定频率pn，qm和pn。两个样品可以共享一个共同的基因子样本，带好个人资料pqn0。 Fisher精确检验分析的基础上，实施fisher.testR函数，P-值调整基础上的功能p.adjustmultitesting。“通常情况下，此功能将被称为后compareGOProfiles执行全球个人资料（都去节点同时）比较显着的结果（具有更好的
用法----------Usage----------
fisherGOProfiles(pn, ...)
## S3 method for class 'numeric'
fisherGOProfiles(pn, qm=NULL, pqn0=NULL,
n = ngenes(pn), m = ngenes(qm), n0 = ngenes(pqn0),
method = "BH", simplify=T, expanded=F, ...)
## S3 method for class 'matrix'
fisherGOProfiles(pn, n, m, method = "BH", ...)
## S3 method for class 'BasicGOProfile'
fisherGOProfiles(pn, qm=NULL, pqn0=NULL,
method = "BH", goIds=T, ...)
## S3 method for class 'ExpandedGOProfile'
fisherGOProfiles(pn, qm=NULL, pqn0=NULL,
method = "BH", simplify=T, ...)
参数----------Arguments----------
参数：pn
an object of class BasicGOProfile or ExpandedGOProfile representing a "sample" GO profile for a fixed ontology, or a numeric vector interpretable as a GO profile (expanded or not), or a two-dimensional frequency matrix (see the 'Details' section). This is a required argument
一个对象类BasicGOProfile或ExpandedGOProfile代表的“样本”的文件为一个固定的本体，或解释作为一个好个人资料（或扩大），或一个两维的频率矩阵数字矢量（见“详细资料”一节）。这是一个必需的参数
参数：qm
similarly, an object representing a "sample" GO profiles for a fixed ontology
同样，一个对象，表示“样品”去一个固定的本体型材
参数：pqn0
an object representing a "sample" GO profile for a fixed ontology
对象代表一个“样本”好一个固定的本体概况
参数：n
the number of genes profiled in pn
在pn异形的基因数目
参数：m
the number of genes profiled in qm
异形的基因数量在QM
参数：n0
the number of genes profiled in pqn0
异形的基因数量在pqn0
参数：method
the p-values adjusting method for multiple comparisons; the same possibilities as in standard R function p.adjust
P-值调整为多重比较的方法;相同的可能性，在标准的R函数p.adjust
参数：expanded
boolean; are these numeric vectors representing expanded profiles?
布尔，这些数字代表扩大型材的向量是什么？
参数：simplify
should the result be simplified, if possible? See the 'Details' section
结果应予以简化，如果可能的话吗？见“详细资料”一节
参数：goIds
if TRUE, each node is represented by its GO identifier
如果为TRUE，每个节点代表的GO标识符
参数：...
other arguments (to be passed to p.adjust or fisher.test functions)
其他参数（将被传递给p.adjust或fisher.test功能）
Details
详情----------Details----------
Given a list of n genes, and a set of s GO classes or nodes X, Y, Z, ... in a given ontology (BP, MF or CC), its associated ("contracted" or "basic") "profile" is the absolute frequencies vector of annotations or hits of the n genes in each one of the s GO nodes. For a given node, say X, this frequency includes all annotations for X alone, for X and Y, for X and Z and so on. Thus, as relative frequencies, its sum is not necessarily one, or as absolute frequencies their sum is not necessarily n. On the other hand, an "expanded profile" corresponds to the relative frequencies in ALL NODE COMBINATIONS. That is, if n genes have been profiled, the expanded profile stands for the frequency of all hits EXCLUSIVELY in node X, exclusively in node Y, exclusively in Z, ..., jointly with all hits simultaneously in nodes X and Y (and only in X and Y), simultaneously in X and Z, in Y and Z, ... , in X and Y and Z (and only in X,Y,Z), and so on. Thus, their sum is one.
鉴于一个n基因的列表，和一套s好类或节点的X，Y，Z，...在一个给定的本体（BP，MF或CC），及其相关的（“合同”或“基本”）“个人资料”是绝对频率在每一个注解或命中的n基因向量s节点。对于一个给定节点，说X，这个频率包括所有注释为X，X和Y单，X和Z等。因此，相对频率，其总和未必，他们为绝对频率的总和不一定n。另一方面，“扩大个人”对应在所有节点组合的相对频率。也就是说，如果n基因已被异形，扩大轮廓代表所有点击的频率只在节点X，专门在节点Y，只在Z，...，共同所有命中同时在节点X和Y（以及仅在X和Y），同时在X和Z，Y和Z，...在X和Y和Z（只有在X，Y，Z轴），等等。因此，他们的总和就是其中之一。
Let n, m and n0 designate the total number of genes profiled in pn, qm and pqn0 respectively. According to these profiles, n[i], m[i] and n0[i] genes are annotated for node 'i', i = 1, ..., s. Note that the sum of all the n[i] not necessarily equals n and so on. If not NULL, pqn0 stands for the profile of the n0 genes common to the gene lists that gave rise to pn and qm. fisherGOProfiles builds a sx2 absolute frequencies matrix
让我们n，m和n0指定pn，qm和pqn0分别异形基因的总数。根据这些配置，N [我]，M [我]和N0 [我]的基因注释为节点的“i”，I = 1，...，s。请注意，所有的n的总和[我]不一定等于n等。如果不为NULL，pqn0代表共同的基因名单，引起了n0和pnqm基因资料。 fisherGOProfiles建sX2矩阵绝对频率
with column totals N1 and N2 (not necessarily equal to the column sums) and performs a Fisher's exact test over each one of the 2x2 tables
列总计N1和N2（不一定等于列金额），并执行过每一个2x2的表的Fisher精确检验
followed by a p-value correction for multiplicity in testing. If pqn0 is NULL, then both gene lists do not have any genes in common, N[i,1] = n[i] and N[i,2] = m[i], and N1 = n, N2 = m, n0 = 0. Otherwhise (if pqn0 is not NULL) N[i,1] = n[i] - n0[i], N1 = n - n0 and N[i,2] = n0[i], N2 = n0 if qm is NULL, or N[i,2] = m[i], N2 = m if qm is not NULL.
其次是为测试中的多重P-值校正。 pqn0如果是NULL，那么这两个基因的名单不常见，氮[I，1] = N [I]和N [I 2] = M [I]和N1 = N，任何基因N2 = M，N0 = 0。 otherwhise（pqn0如果不为NULL）无我，1] = [I] - N0 [我]，N1 = N - N0和N [I 2] = N0 [I]，氮气= N0 qm如果是NULL，或N [I，2] = M [I]，N2 = M qm如果不为NULL。
In other words, this function provides a general setting for diverse, common in practice, situations where a node-by-node analysis is required. When pqn0 = NULL, two lists with no genes in common are compared. Otherwise, when qm = NULL, the genes profiled in pn are compared with a subsample of them, those profiled in pqn0 (a set of genes vs a restricted subset, e.g. those overexpressed under a disease). Finally, if both arguments qm and pqn0 are not NULL (pn is always required) two gene lists with some genes in common are analised.
换句话说，此功能提供了多样化，在实践中常见的，其中一个节点由节点分析需要的情况下一般设置。当pqn0= NULL的，没有共同的基因的两个列表进行比较。否则，当qm= NULL，则在异形的基因pn与他们的子样本，pqn0（禁区子集的基因与异形的比较，如那些过度表达下一种疾病）。最后，如果这两个参数qm和pqn0不为空（pn总是需要）一些共同的基因的两个基因名单analised。
If both qm and pqn0 are NULL, pn should correspond to an absolute frequencies matrix with s rows and 2 columns.
如果双方qm和pqn0是NULL，pn应符合绝对频率s行2列的矩阵。
The arguments n, m or n0 are only required in case of numeric vectors or matrices specifying profiles but lacking the 'ngenes' attribute.
论据n，m或n0只需要在数值向量或矩阵的指定配置文件的情况下，但缺乏“ngenes”属性。
值----------Value----------
A list containing max(ncol(pn),ncol(qm),ncol(pqn0)) p-values numeric vectors, or a single p-values vector if max(ncol(pn),ncol(qm),ncol(pqn0))==1 and simplify == T.
如果一个列表，包含最大（NCOL（PN），NCOL（QM），NCOL（pqn0））p-值的数字向量，或一个单一p值向量最大（NCOL（PN），NCOL（QM），NCOL（pqn0） ）== 1和简化== T。
作者（S）----------Author(s)----------
Jordi Ocana
参考文献----------References----------
参见----------See Also----------
fitGOProfile, compareGOProfiles, equivalentGOProfiles
fitGOProfile，compareGOProfiles，equivalentGOProfiles
举例----------Examples----------
require("org.Hs.eg.db")
data(prostateIds) # "singh01EntrezIDs", "singh05EntrezIDs", "welsh01EntrezIDs", "welsh05EntrezIDs"[的“singh01EntrezIDs”，“singh05EntrezIDs”，“welsh01EntrezIDs”，“welsh05EntrezIDs”]
# To improve speed, use only the first 100 genes:[为了提高速度，只用前100个基因：]
list1 <- welsh01EntrezIDs[1:100]
list2 <- singh01EntrezIDs[1:100]
prof1 <- basicProfile(list1, onto="MF", level=2, orgPackage="org.Hs.eg.db")$MF
prof2 <- basicProfile(list2, onto="MF", level=2, orgPackage="org.Hs.eg.db")$MF
commProf <- basicProfile(intersect(list1, list2), onto="MF", level=2, orgPackage="org.Hs.eg.db")$MF
fisherGOProfiles(prof1, prof2, commProf, method="holm")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-2-25 20:49:33
