Description Usage Arguments Examples
Generate MN plots and select clusters based on them
1 2 3 4 
unclesResult 
The result of the "uncles" function as it is, or a similarly structred list as explained here. This argument is a list which must include two named elements at least:  unclesResult$X: A list of the datasets as matrices or data frames. If a single dataset is provided, it may be provided as it is rather than as a list containing a single matrix or data frame.  unclesResult$B: This can simply be a list of partitions, or can be a multidimensional list array of partitions. Any of the partitions, for example unclesResult$B[[i]], is a binary partition of M rows representing M genes, and K columns representing K clusters. The element unclesResult$B[[i]][j,k] is the binary membership (either 1 or 0) of the (j)th gene in the (k)th cluster as per the (i)th partition. The partitions can have different numbers of clusters (K values) represented by their columns, but they all have to have the same numbers of genes, represented by rows. Moreover, the gene represented by the (j)th row in one of the partitions has to be the same gene represented by the (j)th row in all of the other partitions. In other words, the rows of the partitions must be aligned. If this argument is passed as the output of the "uncles" function, it will be a 4D list array of binary partitions with the dimensions of: (T)x(NBP1)x(NBP2)x(NKs). where (T) is the number of the CoPaM final trials; (NBP1) is the number of the different values of the parameter of the binarisation technique if the UNCLES type is "A", and is the number of the different values of the parameter of the positive binarisation technique if the UNCLES type is "B"; (NBP2) is 1 if the UNCLES type is "A", and is the number of the different values of the parameter of the negative binarisation technique if UNCLES type is "B"; (NKs) is the number of the different numbers of clusters (K values). For example: if 5 trials of the final CoPaM were considered, UNCLES type "A" was used with a DTB binarisation technique whose parameter delta ranges from 0.0 to 1.0 with steps of 0.1, and 4 different K values were considered (e.g. K = 4, 8, 12, and 16), then the dimensions of unclesResult$B will be (5x11x1x4). Other optional named elements of the argument unclesResult include:  unclesResult$GDM: Genedataset logical matrix of M rows representing M genes and L columns representing L datasets. A value of 1 in an element of this matrix indicates that the corresponding gene is found in the corresponding dataset, i.e. it is represented by some probe(s) in that dataset. Default: All ones (all considered genes are found in all of the datasets).  unclesResult$params$type: The type of UNCLES, 'A' or 'B'. Default: 'A'.  unclesResult$params$setsP: For UNCLES type 'B', these are the datasets considered in the positive set of datasets. See the description of the argument "setsP" of the function "uncles".  unclesResult$params$setsN: For UNCLES type 'B', these are the datasets considered in the negative set of datasets. See the description of the argument "setsN" of the function "uncles".  unclesResult$params$wsets: For L datasets, this is a vector of L numeric values representing the relative weights of the datasets. The vector does not have to be normalised as it will be normalised within the "uncles" function. Valid examples for 5 datasets include: wsets = c(0.2, 0.2, 0.2, 0.2, 0.2) wsets = rep(1, 5) wsets = c(4, 4, 4, 4, 4) wsets = c(1, 2, 2, 0, 1) wsets = c(0.2, 0.3, 0, 0.4, 0.4) Note that the first three examples result in the same weighting, which is to treat all datasets equally. If the weight of a dataset was set to zero, this implies excluding it of the analysis. Default: numeric() # which will be read as equal weights for all datasets. 
MCs 
The number of clusters to be selected by the MN scatter plots technique. This is also the number of iterations, as in each iteration one cluster is selected. Default: 10. 
corner 
The coordinates of point at the unitynormalised MN plots which is considered as the reference point from which the distance is measured for the points of all of the clusters. Better clusters are those which are closer to this corner. Its default is the topleft corner of the plot with the coordinates of (0.0, 1.0). As the horizontal axis represents the dispersion within the cluster and the vertical axis represents the size of the cluster, clusters closer to that topleft corner minimise dispersion while maximize their size. If the reference was moved a bit towards the right on the horizontal axis (e.g. to become at (0.2, 1.0)), wider clusters will be selected. While if it was moved towards the left (e.g. to become at (0.2, 1.0)), tighter clusters will be selected. Default: c(0.0, 1.0). 
removedtype 
This is either 'perc' or 'abs'. When a cluster is selected as the best cluster (closest to the "corner" argument), how do we identify the other clusters which overlap with it? Read the description of the argument "removedval" below for details. Default: 'abs' 
removedval 
A numeric value indicating the minimum amount of overlap between the cluster selected as the best cluster in the current iteration and the other clusters for these other clusters to be removed before the following iteration. If "removedtype" is 'perc', "removedval" represents the percentage of the overlap out of the smaller cluster between the two clusters being compared. "removedval" in this case should be in the range 0.0 to 1.0. For example, if "removedval" is 0.25, the overlap between the two clusters has to be at least 25% of the smaller cluster of the two to consider it a significant overlap. If "removedtype" is 'abs', "removedval" represents the minimum number of genes in the overlap to consider it as a significant overlap. "removedval" in this case should be an integer greater than zero. Default: 1. As the default of "removedtype" is 'abs', this means that if a single gene was found in the overlap, the overlap is considered significant. 
Vmse 

mseCache 

doplot 
If TRUE, the function plots the MN plots in addition to providing the calculated results in the output. If FALSE, it just calculates the results and provides them without plotting. 
subplotdim 

subplotind 

minimiseDistance 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # This is the simplist way to apply UNCLES and MN plots.
# Just pass the datasets to the "uncles" function and then pass
# the UNCLES result to the "mnplots" function.
# Both functions will use default values for all other arguments.
#
# Define three random gene expression datasets for 1000 genes.
# The number of samples in the datasets are 6, 4, and 9, respectively.
#
# X = list()
# X[[1]] = matrix(rnorm(6000), 1000, 6)
# X[[2]] = matrix(rnorm(4000), 1000, 4)
# X[[3]] = matrix(rnorm(9000), 1000, 9)
#
# unclesResult < uncles(X)
# mnResult < mnplots(unclesResult)
#
# The clusters will be available in the form of a partition matrix in the variable:
# mnResult$B;

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