TcGSA (version 0.10.1)

clustTrend: Cluster the genes dynamics into different dominant trends.

Description

This function clusters the genes dynamics of one gene sets into different dominant trends. The optimal number of custers is computed thanks to the gap statistics. See clusGap.

Usage

clustTrend(tcgs, expr, Subject_ID, TimePoint, threshold = 0.05, myproc = "BY", nbsimu_pval = 1e+06, baseline = NULL, only.signif = TRUE, group.var = NULL, Group_ID_paired = NULL, ref = NULL, group_of_interest = NULL, FUNcluster = NULL, clustering_metric = "euclidian", clustering_method = "ward", B = 100, max_trends = 4, aggreg.fun = "median", trend.fun = "median", methodOptiClust = "firstSEmax", indiv = "genes", verbose = TRUE)
"print"(x, ...)
"plot"(x, ...)

Arguments

tcgs

a tcgsa object for clustTrend, or a ClusteredTrends object for print.ClusteredTrends and plot.ClusteredTrends.

expr

either a matrix or dataframe of gene expression upon which dynamics are to be calculated, or a list of gene sets estimation of gene expression. In the case of a matrix or dataframe, its dimension are $n$ x $p$, with the $p$ sample in column and the $n$ genes in row. In the case of a list, its length should correspond to the number of gene sets under scrutiny and each element should be an 3 dimension array of estimated gene expression, such as for the list returned in the 'Estimations' element of TcGSA.LR. See details.

Subject_ID

a factor of length $p$ that is in the same order as the columns of expr (when it is a dataframe) and that contains the patient identifier of each sample.

TimePoint

a numeric vector or a factor of length $p$ that is in the same order as Subject_ID and the columns of expr (when it is a dataframe), and that contains the time points at which gene expression was measured.

threshold

the threshold at which the FDR or the FWER should be controlled.

myproc

a vector of character strings containing the names of the multiple testing procedures for which adjusted p-values are to be computed. This vector should include any of the following: "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH", "TSBH". See mt.rawp2adjp for details. Default is "BY", the Benjamini & Yekutieli (2001) step-up FDR-controlling procedure (general dependency structures). In order to control the FWER(in case of an analysis that is more a hypothesis confirmation than an exploration of the expression data), we recommand to use "Holm", the Holm (1979) step-down adjusted p-values for strong control of the FWER.

nbsimu_pval

the number of observations under the null distribution to be generated in order to compute the p-values. Default is 1e+06.

baseline

a character string which is the value of TimePoint that can be used as a baseline. Default is NULL, in which case no timepoint is used as a baseline value for gene expression. Has to be NULL when comparing two treatment groups.

only.signif

logical flag for analysing the trends in only the significant gene sets. If FALSE, all the gene sets from the gmt object contained in x are clustered. Default is TRUE.

group.var

in the case of several treatment groups, this is a factor of length $p$ that is in the same order as Timepoint, Subject_ID and the columns of expr. It indicates to which treatment group each sample belongs to. Default is NULL, which means that there is only one treatment group.

Group_ID_paired

a character vector of length $p$ that is in the same order as Timepoint, Subject_ID, group.var and the columns of expr. This argument must not be NULL in the case of a paired analysis, and must be NULL otherwise. Default is NULL.

ref

the group which is used as reference in the case of several treatment groups. Default is NULL, which means that reference is the first group in alphabetical order of the labels of group.var.

group_of_interest

the group of interest, for which dynamics are to be computed in the case of several treatment groups. Default is NULL, which means that group of interest is the second group in alphabetical order of the labels of group.var.

FUNcluster

the clustering function used to agglomerate genes in trends. Default is NULL, in which a hierachical clustering is performed via the function agnes, using the metric clustering_metric and the method clustering_method. See clusGap

clustering_metric

character string specifying the metric to be used for calculating dissimilarities between observations in the hierarchical clustering when FUNcluster is NULL. The currently available options are "euclidean" and "manhattan". Default is "euclidean". See agnes. Also, a "sts" option is available in TcGSA. It implements the 'Short Time Series' distance [Moller-Levet et al., Fuzzy CLustering of short time series and unevenly distributed sampling points, Advances in Intelligent Data Analysis V:330-340 Springer, 2003] designed specifically for clustering time series.

clustering_method

character string defining the agglomerative method to be used in the hierarchical clustering when FUNcluster is NULL. The six methods implemented are "average" ([unweighted pair-]group average method, UPGMA), "single" (single linkage), "complete" (complete linkage), "ward" (Ward's method), "weighted" (weighted average linkage). Default is "ward". See agnes.

B

integer specifying the number of Monte Carlo ("bootstrap") samples used to compute the gap statistics. Default is 500. See clusGap.

max_trends

integer specifying the maximum number of different clusters to be tested. Default is 4.

aggreg.fun

a character string such as "mean", "median" or the name of any other defined statistics function that returns a single numeric value. It specifies the function used to aggregate the observations before the clustering. Default is to median. Default is "median".

trend.fun

a character string such as "mean", "median" or the name of any other function that returns a single numeric value. It specifies the function used to calculate the trends of the identified clustered. Default is to median.

methodOptiClust

character string indicating how the "optimal" number of clusters is computed from the gap statistics and their standard deviations. Possible values are "globalmax", "firstmax", "Tibs2001SEmax", "firstSEmax" and "globalSEmax". Default is "firstSEmax". See 'method' in clusGap, Details and Tibshirani et al., 2001 in References.

indiv

a character string indicating by which unit observations are aggregated (through aggreg.fun) before the clustering. Possible values are "genes" or "patients". Default is "genes".

verbose

logical flag enabling verbose messages to track the computing status of the function. Default is TRUE.

x

an object of class 'ClusteredTrends'.

...

further arguments passed to or from other methods.

Value

An object of class ClusteredTrends which is a list with the 4 following components:

• NbClust a vector that contains the optimal number of clusters for each analysed gene sets.
• ClustsMeds a list of the same length as NsClust (the number of analysed gene sets). Each element of the list is a data frame, in which there is as many column as the optimal number of clusters for the corresponding gene setsfor each cluster. Each column of the data frame contains the median trend values for the corresponding cluster.
• GenesPartition a list of the same length as NsClust (the number of analysed gene sets). Each element of the list is a vector which gives the partition of the genes inside the corresponding gene set.
• MaxNbClust an integer storing the maximum number of different clusters tested, as given by the argument 'max_trends'.

Details

If expr is a matrix or a dataframe, then the genes dynamics are clustered on the "original" data. On the other hand, if expr is a list returned in the 'Estimations' element of TcGSA.LR, then the dynamics are computed on the estimations made by the TcGSA.LR function.

This function uses the Gap statistics to determine the optimal number of clusters in the plotted gene set. See clusGap.

References

Tibshirani, R., Walther, G. and Hastie, T., 2001, Estimating the number of data clusters via the Gap statistic, Journal of the Royal Statistical Society, Series B (Statistical Methodology), 63, 2: 41--423.

See Also

plot1GS, TcGSA.LR, clusGap

Examples

data(data_simu_TcGSA)

tcgsa_sim_1grp <- TcGSA.LR(expr=expr_1grp, gmt=gmt_sim, design=design, 
                          subject_name="Patient_ID", time_name="TimePoint",
                          time_func="linear", crossedRandom=FALSE)
 
CT <- clustTrend(tcgsa_sim_1grp,
    expr=expr_1grp, Subject_ID=design$Subject_ID, TimePoint=design$TimePoint)
CT
plot(CT)

CT$NbClust
CT$NbClust["Gene set 5"]
CT$ClustMeds[["Gene set 4"]]
CT$ClustMeds[["Gene set 5"]]