R语言 CellNOptR包 normaliseCNOlist()函数中文帮助文档(中英文对照)

loveR

normaliseCNOlist(CellNOptR)
normaliseCNOlist()所属R语言包：CellNOptR

                                         Normalisation for boolean modelling.
                                         为布尔建模的标准化。

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This function takes in a CNOlist and does the normalisation of the data between 0 and 1, according to two different procedures (see details)
此功能需要在CNOlist和0和1之间的数据标准化，根据两个不同的程序（见详情）


用法----------Usage----------


normaliseCNOlist(CNOlist, EC50Data = 0.5, HillCoef = 2, EC50Noise = 0.1, Detection = 0, Saturation = Inf, ChangeTh = 0, Norm2TorCtrl = "time")



参数----------Arguments----------

参数：CNOlist
a CNOlist  
1 CNOlist


参数：EC50Data
parameter for the scaling of the data between 0 and 1, default=0.5  
为0和1之间的数据缩放参数，默认值= 0.5


参数：HillCoef
Hill coefficient for the scaling of the data, defat to 2  
Hill系数为缩放的数据，脱脂2


参数：EC50Noise
parameter for the computation of a penalty for data comparatively smaller than other time points or conditions  
比其他的时间点或条件参数计算数据相对较小的罚款


参数：Detection
minimum detection level of the instrument, everything smaller will be treated as noise (NA), default to 0  
仪器的最低检测水平，一切都将被视为小噪声（NA），默认为0


参数：Saturation
saturation level of the instrument, everything over this will be treated as NA, default to Inf.  
饱和度的仪器，都在这个水平，将被视为不适用，默认为INF。


参数：ChangeTh
threshold for relative change considered significant, default to 0  
阈值的相对变化视为显着，默认为0


参数：Norm2TorCtrl
"time" or "ctrl": choice of a normalisation method: compute the relative change compared to the control at the same time, or to the same condition and measurement at time 0  
“时间”或“CTRL”：归一化方法的选择：计算相对变化相比，在相同的时间控制，或以相同的条件和测量时间0


Details

详情----------Details----------

The normalisation procedure works as follows: a) every value that is out of the dynamic range of the equipment (as specified by the parameters Detection and Saturation are set to NA, b) values are transformed to fold changes relative to the same condition at t0 (if Norm2TorCtrl="time") or the control condition (i.e. same inhibitors, no stimuli) at the same time (if Norm2TorCtrl="ctrl"), c) the fold changes are transformed with a Hill function (i.e. for each data point x^HillCoef/((EC50Data^HillCoef)+(x^HillCoef))), d) a penalty for "noisiness" is computed for each measurement as the value divided by the maximum value for that readout across all conditions and times (excluding values out of the dynamic range), e) the noise penalty is transformed by a saturation function (for each measurement x/(EC50Noise+x) where x=x/max(x)), f) the noise penalty and Hilled fold changes are multiplied, g) if the fold change is negative and bigger than ChangeTh, the resulting product is multiplied by -1, if the fold change is smaller than ChangeTh (either positive or negative), it is set to 0.  The normalisation procedure applied here is explained in details in Saez-Rodriguez et al. (2009).
标准化的过程如下：a）每值是设备的动态范围（由指定的参数Detection和Saturation设置为NA，B）值转化为倍数相对在T0相同的条件下，（如果Norm2TorCtrl="time"）或控制的条件下，在同一时间（即相同的抑制剂，无刺激）（如果Norm2TorCtrl="ctrl"），C）倍的变化与转化的变化希尔函数，计算每个测量值除以最大值，在所有的条件和时间的读数（d）在“吵闹”的刑罚为（即每个数据点x^HillCoef/((EC50Data^HillCoef)+(x^HillCoef))），不包括值动态范围），E）的噪音被罚款转化饱和功能（每个测量x/(EC50Noise+x)x=x/max(x)），F）相乘的噪音罚款和Hilled倍的变化，G），如果倍变化是负和大于ChangeTh乘以-1，由此产生的产品，如果褶皱的变化小于ChangeTh（正或负），它被设置为0。这里应用的标准化过程，解释在赛斯Rodriguez等人的详细信息。 （2009年）。

As the normalisation procedure works by computing a fold change relative to the same condition at time 0 or the control condition, if the aforementioned conditions have a value of zero (which is not expected with any common biochemical technique), then the fold change calculation will return a "NaN" value.  If this is a problem for your particular case then we would suggest putting a dummy, very low value, instead of the zero, or setting that measurement to "NA" in the MIDAS file.
作为标准化过程工程计算倍的相对变化在时间0或控制条件相同的条件下，如果上述条件有一个零值（这是不符合任何共同的生化技术预计），然后倍计算返回一个“南”的价值。如果这是您的特定情况下的问题，那么我们建议把一个虚拟的，非常低的值，而不是零，或“不适用”在MIDAS文件设置，测量。


值----------Value----------

a normalised CNOlist
归一CNOlist


作者（S）----------Author(s)----------



C. Terfve




参考文献----------References----------

mammalian signal transduction, Molecular Systems Biology, 5:331, 2009.

参见----------See Also----------

makeCNOlist
makeCNOlist


举例----------Examples----------


#Load a CNOlist[装入1 CNOlist]

data(CNOlistToy,package="CellNOptR")

#Replace the values in the list by random values [通过随机值替换列表中的值]
#(for demonstration purposes, when actually using this function you would simply load a non-normalised CNOlist)[（用于演示目的，实际使用此功能时，你会简单地装载一个非规范化CNOlist）]

CNOlistToy$valueSignals$t0<-matrix(
        data=runif(n=(dim(CNOlistToy$valueSignals$t0)[1]*dim(CNOlistToy$valueSignals$t0)[2]),min=0,max=400),
        nrow=dim(CNOlistToy$valueSignals$t0)[1],
        ncol=dim(CNOlistToy$valueSignals$t0)[2])
CNOlistToy$valueSignals[[2]]<-CNOlistToy$valueSignals[[1]]+matrix(
        data=runif(n=(dim(CNOlistToy$valueSignals$t0)[1]*dim(CNOlistToy$valueSignals$t0)[2]),min=0,max=100),
        nrow=dim(CNOlistToy$valueSignals$t0)[1],
        ncol=dim(CNOlistToy$valueSignals$t0)[2])

CNOlistToyN<-normaliseCNOlist(
        CNOlistToy,
        EC50Data = 0.5,
        HillCoef = 2,
        EC50Noise = 0.1,
        Detection = 0,
        Saturation = Inf,
        ChangeTh = 0,
        Norm2TorCtrl = "time")

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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。