plot1GS: Plotting a Specific Gene Set

Description

This function can plot different representations of the gene expression in a specific gene set.

Usage

plot1GS(
  expr,
  gmt,
  Subject_ID,
  TimePoint,
  geneset.name,
  baseline = NULL,
  group.var = NULL,
  Group_ID_paired = NULL,
  ref = NULL,
  group_of_interest = NULL,
  FUNcluster = NULL,
  clustering_metric = "euclidian",
  clustering_method = "ward",
  B = 500,
  max_trends = 4,
  aggreg.fun = "median",
  na.rm.aggreg = TRUE,
  trend.fun = "median",
  methodOptiClust = "firstSEmax",
  indiv = "genes",
  verbose = TRUE,
  clustering = TRUE,
  showTrend = TRUE,
  smooth = TRUE,
  precluster = NULL,
  time_unit = "",
  title = NULL,
  y.lab = NULL,
  desc = TRUE,
  lab.cex = 1,
  axis.cex = 1,
  main.cex = 1,
  y.lab.angle = 90,
  x.axis.angle = 45,
  margins = 1,
  line.size = 1,
  y.lim = NULL,
  x.lim = NULL,
  gg.add = list(theme()),
  plot = TRUE
)

Value

A list with 2 elements:

classif: a data.frame with the 2 following variables: ProbeID which contains the IDs of the probes of the plotted gene set, and Cluster containing $ which cluster the probe belongs to. If clustering is FALSE, then Cluster is NA for all the probes.
p: a ggplot object containing the plot

Arguments

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.
gmt: a gmt object containing the gene sets definition. See GSA.read.gmt and https://docs.gsea-msigdb.org/.
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.
geneset.name: a character string containing the name of the gene set to be plotted, that must appear in the "geneset.names" element of gmt.
baseline: a character string which is the value of TimePoint that can be used as a baseline. Default is NULL, in which case no time point is used as a baseline value for gene expression. Has to be NULL when comparing two treatment groups.
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. See Details.
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: a function which accepts as first argument a matrix x and as second argument the number of clusters desired k, and which returns a list with a component named 'cluster' which is a vector of length n = nrow(x) of integers in 1:k, determining the clustering or grouping of the n observations. Default is NULL, in which case a hierarchical clustering is performed via the function agnes, using the metric clustering_metric and the method clustering_method. See 'FUNcluster' in clusGap and Details.
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 "median" or "mean" 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 "mean".
na.rm.aggreg: a logical flag indicating whether NA should be remove to prevent propagation through aggreg.fun. Can be useful to set to TRUE with unbalanced design as those will generate structural NAs in $Estimations. Default is TRUE.
trend.fun: a character string such as "mean" 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 "mean".
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". See Details.
verbose: logical flag enabling verbose messages to track the computing status of the function. Default is TRUE.
clustering: logical flag. If FALSE, there is no clustering representation; if TRUE, the lines are colored according to which cluster they belong to. Default is TRUE. See Details.
showTrend: logical flag. If TRUE, a black line is added for each cluster, representing the corresponding trend.fun. Default is TRUE.
smooth: logical flag. If TRUE and showTrend is also TRUE, the representation of each cluster trend.fun is smoothed using cubic polynomials (see geom_smooth. Default is TRUE. At the moment, must accept parameter "na.rm" (which is automatically set to TRUE). This might change in future versions
precluster: a vector of length $p$ that is in the same order as Subject_ID, TimePoint and the columns of expr (when it is a dataframe), and that contains a prior clustering of the subjects. Default is NULL.
time_unit: the time unit to be displayed (such as "Y", "M", "W", "D", "H", etc) next to the values of TimePoint on the x-axis. Default is "", in which case the time scale on the x-axis is proportional to the time values.
title: character specifying the title of the plot. If NULL, a title is automatically generated, if "", no title appears. Default is NULL.
y.lab: character specifying the annotation of the y axis. If NULL, an annotation is automatically generated, if "", no annotation appears. Default is NULL.
desc: a logical flag. If TRUE, a line is added to the title of the plot with the description of the gene set plotted (from the gmt file). Default is TRUE.
lab.cex: a numerical value giving the amount by which lab labels text should be magnified relative to the default 1.
axis.cex: a numerical value giving the amount by which axis annotation text should be magnified relative to the default 1.
main.cex: a numerical value giving the amount by which title text should be magnified relative to the default 1.
y.lab.angle: a numerical value (in [0, 360]) giving the orientation by which y-label text should be turned (anti-clockwise). Default is 90. See element_text.
x.axis.angle: a numerical value (in [0, 360]) giving the orientation by which x-axis annotation text should be turned (anti-clockwise). Default is 45.
margins: a numerical value giving the amount by which the margins should be reduced or increased relative to the default 1.
line.size: a numerical value giving the amount by which the line sizes should be reduced or increased relative to the default 1.
y.lim: a numeric vector of length 2 giving the range of the y-axis. See plot.default.
x.lim: if numeric, will create a continuous scale, if factor or character, will create a discrete scale. Observations not in this range will be dropped. See xlim.
gg.add: A list of instructions to add to the ggplot2 instructions. See +.gg. Default is list(theme()), which adds nothing to the plot.
plot: logical flag. If FALSE, no plot is drawn. Default is TRUE.

Author

Boris P. Hejblum

Details

If expr is a matrix or a dataframe, then the "original" data are plotted. On the other hand, if expr is a list returned in the 'Estimations' element of TcGSA.LR, then it is those "estimations" made by the TcGSA.LR function that are plotted.

If indiv is 'genes', then each line of the plot is the median of a gene expression over the patients. On the other hand, if indiv is 'patients', then each line of the plot is the median of a patient genes expression in this gene set.

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: 411--423.

Examples

Run this code


if(interactive()){
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)

plot1GS(expr=expr_1grp, TimePoint=design$TimePoint, 
       Subject_ID=design$Patient_ID, gmt=gmt_sim,
       geneset.name="Gene set 4",
       indiv="genes", clustering=FALSE,
       time_unit="H",
       lab.cex=0.7)

plot1GS(expr=expr_1grp, TimePoint=design$TimePoint, 
       Subject_ID=design$Patient_ID, gmt=gmt_sim,
       geneset.name="Gene set 5",
       indiv="patients", clustering=FALSE, baseline=1,
       time_unit="H",
       lab.cex=0.7)
}
if(interactive()){      
geneclusters <- plot1GS(expr=tcgsa_sim_1grp$Estimations, TimePoint=design$TimePoint, 
                       Subject_ID=design$Patient_ID, gmt=gmt_sim,
                       geneset.name="Gene set 5",
                       indiv="genes",
                       time_unit="H",
                       lab.cex=0.7
)
geneclusters
}

if(interactive()){
library(grDevices)
library(graphics)
colval <- c(hsv(0.56, 0.9, 1),
           hsv(0, 0.27, 1),
           hsv(0.52, 1, 0.5),
           hsv(0, 0.55, 0.97),
           hsv(0.66, 0.15, 1),
           hsv(0, 0.81, 0.55),
           hsv(0.7, 1, 0.7),
           hsv(0.42, 0.33, 1)
)
n <- length(colval);  y <- 1:n
op <- par(mar=rep(1.5,4))
plot(y, axes = FALSE, frame.plot = TRUE,
	 xlab = "", ylab = "", pch = 21, cex = 8,
	 bg = colval, ylim=c(-1,n+1), xlim=c(-1,n+1),
	 main = "Color scale"
)
par(op)

plot1GS(expr=expr_1grp, TimePoint=design$TimePoint, 
       Subject_ID=design$Patient_ID, gmt=gmt_sim,
       geneset.name="Gene set 5",
       indiv="genes",
       time_unit="H",
       title="",
       gg.add=list(scale_color_manual(values=colval), 
                   guides(colour = guide_legend(reverse=TRUE))),
       lab.cex=0.7
)

plot1GS(expr=expr_2grp, TimePoint=design$TimePoint, 
       Subject_ID=design$Patient_ID, gmt=gmt_sim,
       geneset.name="Gene set 3",
       indiv="genes",
       group.var = design$group.var,
       time_unit="H",
       gg.add=list(scale_color_manual(values=colval), 
                   guides(colour = guide_legend(reverse=TRUE))),
       lab.cex=0.7
)

}

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