blockwiseConsensusModules(WGCNA)
blockwiseConsensusModules()所属R语言包：WGCNA
Find consensus modules across several datasets.
跨多个数据集的共识模块。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Perform network construction and consensus module detection across several datasets.
在多个数据集进行网络建设和共识模块检测。
用法----------Usage----------
blockwiseConsensusModules(
multiExpr,
# Data checking options
checkMissingData = TRUE,
# Blocking options
blocks = NULL,
maxBlockSize = 5000,
randomSeed = 12345,
# TOM precalculation arguments, if available
individualTOMInfo = NULL,
useIndivTOMSubset = NULL,
# Network construction arguments: correlation options
corType = "pearson",
maxPOutliers = 1,
quickCor = 0,
pearsonFallback = "individual",
cosineCorrelation = FALSE,
# Adjacency function options
power = 6,
networkType = "unsigned",
checkPower = TRUE,
# Topological overlap options
TOMType = "unsigned",
TOMDenom = "min",
# Save individual TOMs?
saveIndividualTOMs = TRUE,
individualTOMFileNames = "individualTOM-Set%s-Block%b.RData",
# Consensus calculation options
consensusQuantile = 0,
scaleTOMs = TRUE, scaleQuantile = 0.95,
# Sampling for scaling (speeds up calculation)
sampleForScaling = TRUE, sampleForScalingFactor = 1000,
getTOMScalingSamples = FALSE,
# Returning the consensus TOM
saveTOMs = FALSE,
consensusTOMFileNames = "consensusTOM-block.%b.RData",
# Internal handling of TOMs
useDiskCache = TRUE, chunkSize = NULL,
cacheBase = ".blockConsModsCache",
# Basic tree cut options
deepSplit = 2,
detectCutHeight = 0.995, minModuleSize = 20,
checkMinModuleSize = TRUE,
# Advanced tree cut opyions
maxCoreScatter = NULL, minGap = NULL,
maxAbsCoreScatter = NULL, minAbsGap = NULL,
pamStage = TRUE, pamRespectsDendro = TRUE,
# Gene reassignment and trimming from a module, and module "significance" criteria
reassignThresholdPS = 1e-4,
trimmingConsensusQuantile = consensusQuantile,
minCoreKME = 0.5, minCoreKMESize = minModuleSize/3,
minKMEtoStay = 0.2,
# Module eigengene calculation options
impute = TRUE,
trapErrors = FALSE,
#Module merging options
mergeCutHeight = 0.15,
mergeConsensusQuantile = consensusQuantile,
# Output options
numericLabels = FALSE,
# General options
nThreads = 0,
verbose = 2, indent = 0)
参数----------Arguments----------
参数：multiExpr
expression data in the multi-set format (see checkSets). A vector of lists, one per set. Each set must contain a component data that contains the expression data, with rows corresponding to samples and columns to genes or probes.
表达在多集的格式的数据（见checkSets）。一个向量的列表，每一个组。每个集必须包含一个组件data包含的表达数据，与对应的基因或探针的样品和列的行。
参数：checkMissingData
logical: should data be checked for excessive numbers of missing entries in genes and samples, and for genes with zero variance? See details.
逻辑：基因和样品缺少的条目过多的数据进行检查，并与零差异的基因呢？查看详细信息。
参数：blocks
optional specification of blocks in which hierarchical clustering and module detection should be performed. If given, must be a numeric vector with one entry per gene of multiExpr giving the number of the block to which the corresponding gene belongs.
块层次聚类和模块检测应执行的可选规格。如果给定的，必须是一个数值向量与基因multiExpr给该块的数目相应的基因所属的每一个条目。
参数：maxBlockSize
integer giving maximum block size for module detection. Ignored if blocks above is non-NULL. Otherwise, if the number of genes in datExpr exceeds maxBlockSize, genes will be pre-clustered into blocks whose size should not exceed maxBlockSize.
整数，最大块大小为模块检测。如果忽略blocks以上非NULL。否则，如果基因的数目在datExpr超过maxBlockSize，基因将预聚成块的大小应不超过maxBlockSize的。
参数：randomSeed
integer to be used as seed for the random number generator before the function starts. If a current seed exists, it is saved and restored upon exit. If NULL is given, the function will not save and restore the seed.
整数函数开始之前被用作随机数发生器的种子。如果当前的种子存在，它退出时保存和恢复。 NULL如果的，该函数将不能保存和恢复的种子。
参数：individualTOMInfo
Optional data for TOM matrices in individual data sets. This object is returned by the function blockwiseIndividualTOMs. If not given, appropriate topological overlaps will be calculated using the network contruction options below.
TOM矩阵的单个数据集的可选数据。返回这个对象的功能blockwiseIndividualTOMs。如果没有给出，适当的的拓扑重叠，将计算以下使用网络敷设渠道的选择。
参数：useIndivTOMSubset
If individualTOMInfo is given, this argument allows to only select a subset of the individual set networks contained in individualTOMInfo. It should be a numeric vector giving the indices of the individual sets to be used. Note that this argument is NOT applied to multiExpr.
individualTOMInfo如果的，这种说法只允许选择中包含的individualTOMInfo个人网络的一个子集。它应该是一个数值向量给要使用的各个集的索引。请注意，这种说法并不适用于multiExpr。
参数：corType
character string specifying the correlation to be used. Allowed values are (unique abbreviations of) "pearson" and "bicor", corresponding to Pearson and bidweight midcorrelation, respectively. Missing values are handled using the pariwise.complete.obs option.
字符串指定要使用的相关性。允许的值是（唯一的缩写）"pearson"和"bicor"，对应的Pearson和bidweight midcorrelation的，分别。处理缺失值pariwise.complete.obs使用选项。
参数：maxPOutliers
only used for corType=="bicor". Specifies the maximum percentile of data that can be considered outliers on either side of the median separately. For each side of the median, if higher percentile than maxPOutliers is considered an outlier by the weight function based on 9*mad(x), the width of the weight function is increased such that the percentile of outliers on that side of the median equals maxPOutliers. Using maxPOutliers=1 will effectively disable all weight function broadening; using maxPOutliers=0 will give results that are quite similar (but not equal to) Pearson correlation.
仅用于corType=="bicor"。指定的最大百分位数的数据是可以考虑的离群值中位数的任一侧上分开。如果在中位数的每一侧，如果更高的百分比maxPOutliers被认为是一个异常值的权重函数基于9*mad(x)，权重函数的宽度的增加，离群值的那侧上的百分中位数等于maxPOutliers。使用maxPOutliers=1将有效地禁用所有的权重函数扩大; maxPOutliers=0给出的结果是相当类似（但不等于）Pearson相关。
参数：quickCor
real number between 0 and 1 that controls the handling of missing data in the calculation of correlations. See details.
0和1之间，控制处理中丢失的数据的相关性的计算的实数。查看详细信息。
参数：pearsonFallback
Specifies whether the bicor calculation, if used, should revert to Pearson when median absolute deviation (mad) is zero. Recongnized values are (abbreviations of) "none", "individual", "all". If set to "none", zero mad will result in NA for the corresponding correlation. If set to "individual", Pearson calculation will be used only for columns that have zero mad. If set to "all", the presence of a single zero mad will cause the whole variable to be treated in Pearson correlation manner (as if the corresponding robust option was set to FALSE). Has no effect for Pearson correlation. See bicor.
指定是否BICOR计算，如果使用的话，应恢复时，Pearson的平均绝对偏差（MAD）是零。株型识别的值是（的缩写）"none", "individual", "all"。如果设置为"none"，零狂会导致NA相应的相关。如果设置为"individual"，皮尔森计算将仅用于列具有零狂。如果设置为"all"，一个单独的零狂的存在，将导致在Pearson相关性的方式来对待整个变量（如果相应的robust选项被设置为FALSE）。有没有影响Pearson相关。见bicor。
参数：cosineCorrelation
logical: should the cosine version of the correlation calculation be used? The cosine calculation differs from the standard one in that it does not subtract the mean.
余弦版本的相关计算逻辑：应使用？的余弦计算不同于标准的一个，它并没有减去均值。
参数：power
soft-thresholding power for netwoek construction.
软阈值功率为netwoek建设。
参数：networkType
network type. Allowed values are (unique abbreviations of) "unsigned", "signed", "signed hybrid". See adjacency.
网络类型。允许的值是（）"unsigned"，"signed"，"signed hybrid"唯一的缩写。见adjacency。
参数：checkPower
logical: should basic sanity check be performed on the supplied power? If you would like to experiment with unusual powers, set the argument to FALSE and proceed with caution.
逻辑基础上进行完整性检查所提供的power？如果您想尝试不寻常的权力，设置的参数FALSE和谨慎行事。
参数：TOMType
one of "none", "unsigned", "signed". If "none", adjacency will be used for clustering. If "unsigned", the standard TOM will be used (more generally, TOM function will receive the adjacency as input). If "signed", TOM will keep track of the sign of correlations between neighbors.
"none"，"unsigned"，"signed"之一。如果"none"，邻接将用于聚类。如果"unsigned"，标准的TOM将使用（更一般地，TOM函数将接收到作为输入的邻接）。如果"signed"，TOM将跟踪邻居之间的相关性的符号。
参数：TOMDenom
a character string specifying the TOM variant to be used. Recognized values are "min" giving the standard TOM described in Zhang and Horvath (2005), and "mean" in which the min function in the denominator is replaced by mean. The "mean" may produce better results but at this time should be considered experimental.
要使用的字符的字符串指定的TOM变种。公认的价值观是"min"的给标准TOM张和霍瓦特（2005年）中描述的，"mean"在其中min函数分母中的被替换的mean。 "mean"可能会产生更好的结果，但在这个时候，应考虑实验。
参数：saveIndividualTOMs
logical: should individual TOMs be saved to disk for later use?
逻辑：个人TOMS被保存到磁盘，以备后用呢？
参数：individualTOMFileNames
character string giving the file names to save individual TOMs into. The following tags should be used to make the file names unique for each set and block: %s will be replaced by the set number; %N will be replaced by the set name (taken from names(multiExpr)) if it exists, otherwise by set number; %b will be replaced by the block number. If the file names turn out to be non-unique, an error will be generated.
字符的字符串，给出文件名进行保存个人TOMS。下面的标签应该被用来制造独特的文件名，为每个集合和数据块：%s将被取代的定数; %N将被替换集的名称（取自names(multiExpr) ），如果它存在，否则定数，“%b将被替换的块号。如果该文件名变成非唯一的，会产生错误。
参数：consensusQuantile
qunatile at which consensus is to be defined. See details.
qunatile的共识是被定义。查看详细信息。
参数：scaleTOMs
should set-specific TOM matrices be scaled to the same scale?
应设置具体的TOM矩阵扩展到同等规模的？
参数：scaleQuantile
if scaleTOMs is TRUE, topological overlaps (or adjacencies if TOMs are not computed) will be scaled such that their scaleQuantile quantiles will agree.
如果scaleTOMsTRUE，的的拓扑重叠（或邻接如果大卫 - 汤姆斯不计算）将进行调整，这样他们的scaleQuantile位数会同意。
参数：sampleForScaling
if TRUE, scale quantiles will be determined from a sample of network similarities. Note that using all data can double the memory footprint of the function and the function may fail.
如果TRUE，规模位数将确定的样本网络相似之处。请注意，使用的功能，所有数据都可以增加一倍的内存占用和功能可能会失败。
参数：sampleForScalingFactor
determines the number of samples for scaling: the number is 1/scaleQuantile * sampleForScalingFactor. Should be set well above 1 to ensure accuracy of the sampled quantile.
确定的样本数的换算：数字是1/scaleQuantile * sampleForScalingFactor。应设置远高于1的采样位数，以确保准确性。
参数：getTOMScalingSamples
logical: should samples used for TOM scaling be saved for future analysis? This option is only available when sampleForScaling is TRUE.
逻辑：样品用于TOM缩放的保存，供日后分析？此选项仅当sampleForScaling是TRUE。
参数：saveTOMs
logical: should the consensus topological overlap matrices for each block be saved and returned?
逻辑的共识的拓扑重叠的矩阵每块被保存并返回吗？
参数：consensusTOMFileNames
character string containing the file namefiles containing the consensus topological overlaps. The tag %b will be replaced by the block number. If the resulting file names are non-unique (for example, because the user gives a file name without a %b tag), an error will be generated. These files are standard R data files and can be loaded using the load function.
字符串包含的的文件namefiles包含拓扑重叠共识。的标签%b将被替换的块数。如果生成的文件名是唯一的（例如，因为用户提供的是文件名没有%b标签），会产生错误。这些文件是标准的R数据文件，可以加载使用load功能。
参数：useDiskCache
should calculated network similarities in individual sets be temporarilly saved to disk? Saving to disk is somewhat slower than keeping all data in memory, but for large blocks and/or many sets the memory footprint may be too big.
计算的网络相似的各套temporarilly保存到磁盘上？比保持在内存中的所有数据保存到磁盘上是有点慢，但，大块和/或多套的内存占用太大。
参数：chunkSize
network similarities are saved in smaller chunks of size chunkSize.
网络的相似之处都保存在较小的数据块的大小chunkSize。
参数：cacheBase
character string containing the desired name for the cache files. The actual file names will consists of cacheBase and a suffix to make the file names unique.
字符串，包含所需的名称的缓存文件。实际文件名由cacheBase和后缀的文件名是独一无二的。
参数：deepSplit
integer value between 0 and 4. Provides a simplified control over how sensitive module detection should be to module splitting, with 0 least and 4 most sensitive. See cutreeDynamic for more details.
在0和4之间的整数值。应该是敏感的模块检测到模块的分裂提供了一个简化的控制，与0至少4个最敏感的。见cutreeDynamic更多详情。
参数：detectCutHeight
dendrogram cut height for module detection. See cutreeDynamic for more details.
模块检测系统树砍高度。见cutreeDynamic更多详情。
参数：minModuleSize
minimum module size for module detection. See cutreeDynamic for more details.
模块检测最小的模块尺寸。见cutreeDynamic更多详情。
参数：checkMinModuleSize
logical: should sanity checks be performed on minModuleSize?
逻辑上进行完整性检查minModuleSize？
参数：maxCoreScatter
maximum scatter of the core for a branch to be a cluster, given as the fraction of cutHeight relative to the 5th percentile of joining heights. See cutreeDynamic for more details.
最大散度为核心的一个分支，是一个聚类，cutHeight相对于第5百分位加盟高度的比例。见cutreeDynamic更多详情。
参数：minGap
minimum cluster gap given as the fraction of the difference between cutHeight and the 5th percentile of joining heights. See cutreeDynamic for more details.
给出最小聚类隙作为cutHeight和高度的接合的第5百分位之间的差异的馏分。见cutreeDynamic更多详情。
参数：maxAbsCoreScatter
maximum scatter of the core for a branch to be a cluster given as absolute heights. If given, overrides maxCoreScatter. See cutreeDynamic for more details.
最大散度为核心的一个分支，是一个绝对高度聚类。如果给定的，覆盖maxCoreScatter。见cutreeDynamic更多详情。
参数：minAbsGap
minimum cluster gap given as absolute height difference. If given, overrides minGap. See cutreeDynamic for more details.
最小群的差距给予作为绝对高度差。如果给定的，覆盖minGap。见cutreeDynamic更多详情。
参数：pamStage
logical. If TRUE, the second (PAM-like) stage of module detection will be performed. See cutreeDynamic for more details.
逻辑。如果是TRUE，第二阶段（PAM等）的模块检测将被执行。见cutreeDynamic更多详情。
参数：pamRespectsDendro
Logical, only used when pamStage is TRUE. If TRUE, the PAM stage will respect the dendrogram in the sense an object can be PAM-assigned only to clusters that lie below it on the branch that the object is merged into. See cutreeDynamic for more details.
逻辑，只用时pamStage是TRUE。如果TRUE，PAM阶段会尊重树状图在这个意义上，一个对象可以是PAM-分配到聚类位于它下面的对象被合并到分支。见cutreeDynamic更多详情。
参数：reassignThresholdPS
per-set p-value ratio threshold for reassigning genes between modules. See Details.
每个组的p值比阈值重新分配模块之间的基因。查看详细信息。
参数：trimmingConsensusQuantile
a number between 0 and 1 specifying the consensus quantile used for kME calculation that determines module trimming according to the arguments below.
0和1之间的一个数，指定的共识用于KME计算的位数，确定模块，修整根据下面的论据。
参数：minCoreKME
a number between 0 and 1. If a detected module does not have at least minModuleKMESize genes with eigengene connectivity at least minCoreKME, the module is disbanded (its genes are unlabeled and returned to the pool of genes waiting for mofule detection).
0和1之间的一个数。如果检测到的模块不与eigengene连接至少有minModuleKMESize基因至少minCoreKME，该模块被解散（未标记的基因，并返回到池中等待mofule检测的基因）。
参数：minCoreKMESize
see minCoreKME above.
看到minCoreKME以上。
参数：minKMEtoStay
genes whose eigengene connectivity to their module eigengene is lower than minKMEtoStay are removed from the module.
基因的eigengene连接到模块eigengene的是比minKMEtoStay是从模块中删除。
参数：impute
logical: should imputation be used for module eigengene calculation? See moduleEigengenes for more details.
逻辑：应归集使用计算模块eigengene的？见moduleEigengenes更多详情。
参数：trapErrors
logical: should errors in calculations be trapped?
逻辑：应计算错误而陷入吗？
参数：mergeCutHeight
dendrogram cut height for module merging.
树图切模块合并的高度。
参数：mergeConsensusQuantile
consensus quantile for module merging. See mergeCloseModules for details.
模块合并的共识位数。见mergeCloseModules的详细信息。
参数：numericLabels
logical: should the returned modules be labeled by colors (FALSE), or by numbers (TRUE)?
逻辑：应返回的模块进行标记的颜色（FALSE），或由数字（TRUE）的？
参数：nThreads
non-negative integer specifying the number of parallel threads to be used by certain parts of correlation calculations. This option only has an effect on systems on which a POSIX thread library is available (which currently includes Linux and Mac OSX, but excludes Windows). If zero, the number of online processors will be used if it can be determined dynamically, otherwise correlation calculations will use 2 threads.
非负的整数，用于指定要使用的某些部分的相关性计算的并行线程的数目。此选项仅影响的系统上POSIX线程库（目前包括Linux和Mac OSX，但不包括视窗）。如果为零，则在线的处理器的数目将被使用，如果是可以动态地确定，否则将使用相关计算2个线程。
参数：verbose
integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
整数的详细程度。零表示沉默，较高的值使输出越来越多，更详细。
参数：indent
indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.
缩进诊断消息。零表示无压痕，每个单元增加两个空格。
Details
详细信息----------Details----------
The function starts by optionally filtering out samples that have too many missing entries and genes that have either too many missing entries or zero variance in at least one set. Genes that are filtered out are left unassigned by the module detection. Returned eigengenes will contain NA in entries corresponding to filtered-out samples.
此功能启动，通过随意过滤的样品也有许多遗漏的项目和基因，要么太多的遗漏的项目或至少在一组的方差为零。空缺的模块检测基因被过滤掉。将包含返回的特征基因NA相应的滤出样品中的条目。
If blocks is not given and the number of genes exceeds maxBlockSize, genes are pre-clustered into blocks using the function consensusProjectiveKMeans; otherwise all genes are treated in a single block.
blocks如果不和的基因数目超过maxBlockSize，，基因的预聚成块的功能consensusProjectiveKMeans;否则，所有基因都在一个单一的块处理。
For each block of genes, the network is constructed and (if requested) topological overlap is calculated in each set. To minimize memory usage, calculated topological overlaps are optionally saved to disk in chunks until they are needed again for the calculation of the consensus network topological overlap. If requested, the consensus topological overlaps are saved to disk for later use. Genes are then clustered using average linkage hierarchical clustering and modules are identified in the resulting dendrogram by the Dynamic Hybrid tree cut. Found modules are trimmed of genes whose consensus module membership kME (that is, correlation with module eigengene) is less than minKMEtoStay. Modules in which fewer than minCoreKMESize genes have consensus KME higher than minCoreKME are disbanded, i.e., their constituent genes are pronounced unassigned.
对于每个块的基因，该网络被构造和（如果被请求）拓扑重叠的每个集合中的计算。为了减少内存的使用情况，计算的拓扑重叠选择性地保存到磁盘的块，直到他们都需要重新计算网络拓扑重叠的共识。如果有要求，拓扑重叠共识保存到磁盘，以备后用。基因平均联动的层次聚类和模块标识在聚类分析的动态混合型树切，然后集中使用。发现模块被修剪的共识模块成员KME（即，相关模块eigengene）的是小于minKMEtoStay的基因。模块不到minCoreKMESize基因是有共识的KME高于minCoreKME解散，即其组成基因的发音未分配的。
After all blocks have been processed, the function checks whether there are genes whose KME in the module they assigned is lower than KME to another module. If p-values of the higher correlations are smaller than those of the native module by the factor reassignThresholdPS (in every set), the gene is re-assigned to the closer module.
所有块都被处理之后，该函数将检查是否有基因的KME在他们分配模块是低于KME到另一个模块。如果较高的相关性的p-值小于是由因子reassignThresholdPS（在每一个集合），该基因被重新分配到的密切模块的本机模块。
In the last step, modules whose eigengenes are highly correlated are merged. This is achieved by clustering module eigengenes using the dissimilarity given by one minus their correlation, cutting the dendrogram at the height mergeCutHeight and merging all modules on each branch. The process is iterated until no modules are merged. See mergeCloseModules for more details on module merging.
在最后一步中，模块，其特征基因是高度相关的合并。这是通过使用给定的由减去及其相关性之一的相异的聚类模块特征基因，切割mergeCutHeight和合并每个分支上的所有模块的高度在树状图。该过程被重复，直到没有合并模块。见mergeCloseModules模块合并的详细信息。
The argument quick specifies the precision of handling of missing data in the correlation calculations. Zero will cause all calculations to be executed precisely, which may be significantly slower than calculations without missing data. Progressively higher values will speed up the calculations but introduce progressively larger errors. Without missing data, all column means and variances can be pre-calculated before the covariances are calculated. When missing data are present, exact calculations require the column means and variances to be calculated for each covariance. The approximate calculation uses the pre-calculated mean and variance and simply ignores missing data in the covariance calculation. If the number of missing data is high, the pre-calculated means and variances may be very different from the actual ones, thus potentially introducing large errors. The quick value times the number of rows specifies the maximum difference in the number of missing entries for mean and variance calculations on the one hand and covariance on the other hand that will be tolerated before a recalculation is triggered. The hope is that if only a few missing data are treated approximately, the error introduced will be small but the potential speedup can be significant.
参数quick指定的相关计算丢失的数据处理的精度。为零，将导致所有的计算精确被执行，这可能是明显慢于没有丢失的数据的计算。值会逐步提高计算的速度，但介绍逐步误差较大。没有丢失的数据，所有的列均值和方差，可以预先计算出的协方差计算之前。当丢失的数据，精确的计算需要为每个协方差列的均值和方差来计算。的近似计算使用预先计算的均值和方差，并简单地忽略丢失数据的协方差计算。如果丢失的数据的数目是高的，预先计算的均值和方差可能从实际的是非常不同的，从而有可能引入较大的误差。 quick值乘以的行数指定缺少的条目，用于一方面和协方差，在另一方面，将重新计算被触发之前被容忍的均值和方差计算的数目的最大差值。希望是，如果只有很少的缺失数据处理约，将引入的误差小，但潜在的加速可能是显着的。
值----------Value----------
A list with the following components:
以下组件列表：
参数：colors
module assignment of all input genes. A vector containing either character strings with module colors (if input numericLabels was unset) or numeric module labels (if numericLabels was set to TRUE). The color "grey" and the numeric label 0 are reserved for unassigned genes.
所有的输入基因的模块分配。如果输入一个向量，包含模块的颜色是字符的字符串（numericLabels未设置）或数字模块标签（如果numericLabels设置为TRUE的“）。颜色为“灰色”和数字标签0被保留为未分配的基因。
参数：unmergedColors
module colors or numeric labels before the module merging step.
模块的颜色或模块合并步骤之前的数字标签。
参数：multiMEs
module eigengenes corresponding to the modules returned in colors, in multi-set format. A vector of lists, one per set, containing eigengenes, proportion of variance explained and other information. See multiSetMEs for a detailed description.
模块的特征基因对应于返回的模块在colors，在多设置的格式。向量的列表，每一个组，包含特征基因，解释方差比例和其他信息。见multiSetMEs进行了详细的描述。
参数：goodSamples
a list, with one component per input set. Each component is a logical vector with one entry per sample from the corresponding set. The entry indicates whether the sample in the set passed basic quality control criteria.
一个列表，与每个输入组的一个组成部分。每个组件是一个逻辑的矢量与相应的一组样品从每一个条目。项指示是否通过基本的质量控制标准的集合中的样本。
参数：goodGenes
a logical vector with one entry per input gene indicating whether the gene passed basic quality control criteria in all sets.
逻辑向量输入基因基因是否通过基本质量控制标准，在所有集合的每一个条目。
参数：dendrograms
a list with one component for each block of genes. Each component is the hierarchical clustering dendrogram obtained by clustering the consensus gene dissimilarity in the corresponding block.
基因的一个组成部分的每个块的列表。每个组件都是在相应的块由聚类的共识基因不同的层次聚类的聚类分析获得。
参数：TOMFiles
if saveTOMs==TRUE, a vector of character strings, one string per block, giving the file names of files (relative to current directory) in which blockwise topological overlaps were saved.
如果saveTOMs==TRUE，矢量字符的字符串，每块的一个字符串，文件（相对于当前目录），其中列块的拓扑重叠保存的文件名。
参数：blockGenes
a list with one component for each block of genes. Each component is a vector giving the indices (relative to the input multiExpr) of genes in the corresponding block.
基因的一个组成部分的每个块的列表。每个组件是一个给指数（相对于输入multiExpr）在对应的块中的基因的向量。
参数：blocks
if input blocks was given, its copy; otherwise a vector of length equal number of genes giving the block label for each gene. Note that block labels are not necessarilly sorted in the order in which the blocks were processed (since we do not require this for the input blocks). See blockOrder below.
如果输入blocks是给定的，它的拷贝，否则将向量的长度相等数量的基因给每个基因的块标签。需要注意的是，块标签不necessarilly排序块的顺序进行处理（因为我们不需要输入blocks）。见blockOrder下面。
参数：blockOrder
a vector giving the order in which blocks were processed and in which blockGenes above is returned. For example, blockOrder[1] contains the label of the first-processed block.
一个向量块的顺序进行的处理，并在其中blockGenes返回上述。例如，blockOrder[1]的第一处理块中包含的标签。
参数：originCount
if the input consensusQuantile==0, this vector will contain counts of how many times each set contributed the consensus gene similarity value. If the counts are highly unbalanced, the consensus may be biased.
如果输入consensusQuantile==0，此向量将包含每个组提供的多少次的共识基因相似度值的计数。如果罪名极不平衡，可能失之偏颇的共识。
参数：TOMScalingSamples
if the input getTOMScalingSamples is TRUE, this component is a list with one component per block. Each component is again a list with two components: sampleIndex contains indices of the distance structure in which TOM is stored that were sampled, and TOMSamples is a matrix whose rows correspond to TOM samples and columns to individual set. Hence, TOMScalingSamples[[blockNo]]$TOMSamples[index, setNo] contains the TOM entry that corresponds to element TOMScalingSamples[[blockNo]]$sampleIndex[index] of the TOM distance structure in block blockNo and set setNo. (For details on the distance structure, see dist.)
，如果输入getTOMScalingSamplesTRUE，该组件是一个的一个组成部分，每块列表。每个组件又是两部分组成：一个列表，sampleIndex包含指数的距离结构中，TOM存储的采样和TOMSamples是一个矩阵的行对应TOM和列个人组。因此，TOMScalingSamples[[blockNo]]$TOMSamples[index, setNo]包含TOM项相对应的元素TOMScalingSamples[[blockNo]]$sampleIndex[index]块blockNo设置setNo的的TOM距离结构。 （距离结构的详细信息，请参阅dist。）
注意----------Note----------
If the input datasets have large numbers of genes, consider carefully the maxBlockSize as it significantly affects the memory footprint (and whether the function will fail with a memory allocation error). From a theoretical point of view it is advantageous to use blocks as large as possible; on the other hand, using smaller blocks is substantially faster and often the only way to work with large numbers of genes. As a rough guide, it is unlikely a standard desktop computer with 4GB memory or less will be able to work with blocks larger than 7000 genes.
如果输入数据集有大量的基因，仔细考虑maxBlockSize，因为它显着影响的内存占用（而不论该函数将失败，内存分配错误）。从理论的角度来看，它有利的是使用尽可能大的块，另一方面，使用更小的块基本上是更快，往往是唯一的方式来工作，与大量的基因。作为一个粗略的指南，它不可能是一个标准的台式机，搭配4GB内存或以下，将能与块大于7000个基因。
（作者）----------Author(s)----------
Peter Langfelder
参考文献----------References----------
参见----------See Also----------
goodSamplesGenesMS for basic quality control and filtering;
goodSamplesGenesMS基本的质量控制和过滤;
adjacency, TOMsimilarity for network construction;
adjacency，TOMsimilarity网络建设;
hclust for hierarchical clustering;
hclust层次聚类;
cutreeDynamic for adaptive branch cutting in hierarchical clustering dendrograms;
cutreeDynamic自适应枝扦插层次聚类树状图;
mergeCloseModules for merging of close modules.
mergeCloseModules合并密切模块。
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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 2012-10-1 21:07:26
