This function plots a series of gene sets dynamic trends heatmaps. One heatmap is drawn for each patient. NOT IMPLEMENTED YET (TODO)
plotPat.TcGSA(
x,
threshold = 0.05,
myproc = "BY",
nbsimu_pval = 1e+06,
expr,
Subject_ID,
TimePoint,
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 = 500,
max_trends = 4,
aggreg.fun = "median",
na.rm.aggreg = TRUE,
methodOptiClust = "firstSEmax",
verbose = TRUE,
clust_trends = NULL,
N_clusters = NULL,
myclusters = NULL,
label.clusters = NULL,
prev_rowCL = NULL,
descript = TRUE,
plotAll = TRUE,
color.vec = c("darkred", "#D73027", "#FC8D59", "snow", "#91BFDB", "#4575B4",
"darkblue"),
legend.breaks = NULL,
label.column = NULL,
time_unit = "",
cex.label.row = 1,
cex.label.column = 1,
margins = c(5, 25),
heatKey.size = 1,
dendrogram.size = 1,
heatmap.height = 1,
heatmap.width = 1,
cex.clusterKey = 1,
cex.main = 1,
horiz.clusterKey = TRUE,
main = NULL,
subtitle = NULL,
...
)a tcgsa object.
the threshold at which the FDR or the FWER should be controlled.
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" or "none".
"none" indicates no adjustment for multiple testing. 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 recommend to use "Holm", the
Holm (1979) step-down adjusted p-values for strong control of the FWER.
the number of observations under the null distribution to
be generated in order to compute the p-values. Default is 1e+06.
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.
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.
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.
a character string which is the value of TimePoint
used as baseline.
logical flag for plotting only the significant gene sets.
If FALSE, all the gene sets from the gmt object contained in
x are plotted. Default is TRUE.
in the case of several treatment groups, this is a factor of
length \(p\) that is in the same order as Timepoint,
Subject_ID, sample_name 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. See
Details.
a character vector of length \(p\) that is in the
same order as Timepoint, Subject_ID, sample_name,
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.
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.
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.
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.
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.
integer specifying the number of Monte Carlo ("bootstrap") samples
used to compute the gap statistics. Default is 500. See
clusGap.
integer specifying the maximum number of different clusters
to be tested. Default is 4.
a character string such as "mean", "median"
or the name of any other statistics function defined 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".
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.
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.
logical flag enabling verbose messages to track the computing
status of the function. Default is TRUE.
object of class ClusteredTrends containing
already computed trends for the plotted gene sets. Default is NULL.
an integer that is the number of clusters in which the
dynamics should be regrouped. The cutoff of the clustering tree is
automatically calculated accordingly. Default is NULL, in which case
the dendrogram is not cut and no clusters are identified.
a character vector of colors for predefined clusters of the
represented gene sets, with as many levels as the value of N_clusters.
Default is NULL, in which case the clusters are automatically
identified and colored via the cutree function and the
N_clusters argument only.
if N_clusters is not NULL, a character
vector of length N_clusterss. Default is NULL, in which case
if N_clusters is not NULL, clusters are simply labeled with
numbers.
a hclust object, such as the one return by the
present plotting function (see Value) for instance. If not NULL, no
clustering is calculated by the present plotting function and this tree is
used to represent the gene sets dynamics. Default is NULL.
logical flag indicating that the description of the gene sets
should appear after their name on the right side of the plot if TRUE.
Default is TRUE. See Details.
logical flag indicating whether a first heatmap with the median
over all the patients should be plotted, or not. Default is TRUE.
a character strings vector used to define the color
palette used in the plot. Default is
c("#D73027", "#FC8D59","lightyellow", "#91BFDB", "#4575B4").
a numeric vector indicating the splitting points for
coloring. Default is NULL, in which case the break points will be
spaced equally and symmetrically about 0.
a vector of character strings with the labels to be
displayed for the columns (i.e. the time points). Default is NULL.
the time unit to be displayed (such as "Y",
"M", "W", "D", "H", etc) next to the values of
TimePoint in the columns labels when label.column is
NULL. Default is "".
a numerical value giving the amount by which row labels
text should be magnified relative to the default 1.
a numerical value giving the amount by which column
labels text should be magnified relative to the default 1.
numeric vector of length 2 containing the margins (see
par(mar= *)) for column and row names, respectively. Default
is c(15, 100). See Details.
the size of the color key for the heatmap fill. Default
is 1.
the horizontal size of the dendrogram. Default is
1
the height of the heatmap. Default is 1
the width of the heatmap. Default is 1
a numerical value giving the amount by which the
clusters legend text should be magnified relative to the default 1,
when N_clusters is not NULL.
a numerical value giving the amount by which title text
should be magnified relative to the default 1.
a logical flag; if TRUE, set the legend for
clusters horizontally rather than vertically. Only used if the
N_clusters argument is not NULL. Default is TRUE.
a character string for an optional title. Default is
NULL.
a character string for an optional subtitle. Default is
NULL.
other parameters to be passed through to plotting functions.
An object of class hclust which describes the tree produced by the clustering process. The object is a list with components:
merge an \(n-1\) by \(2\) matrix. Row \(i\) of
merge describes the merging of clusters at step i of the clustering.
If an element \(j\) in the row is negative, then observation -\(j\) was
merged at this stage. If \(j\) is positive then the merge was with the
cluster formed at the (earlier) stage \(j\) of the algorithm. Thus
negative entries in merge indicate agglomerations of singletons, and positive
entries indicate agglomerations of non-singletons.
height a set of \(n-1\) real values (non-decreasing for
ultrametric trees). The clustering height: that is, the value of the
criterion associated with the Ward clustering method.
order a vector giving the permutation of the original
observations suitable for plotting, in the sense that a cluster plot using
this ordering and matrix merge will not have crossings of the branches.
labels the gene sets name.
call the call which produced the result clustering:
hclust(d = dist(map2heat, method = "euclidean"), method = "ward.D2")
method "ward.D2", as it is the clustering method that has been used for clustering the gene set trends.
dist.method "euclidean", as it is the distance that has been used
for clustering the gene set trends.
legend.breaks a numeric vector giving the splitting points used
for coloring the heatmap. If plot is FALSE, then it is
NULL.
myclusters a character vector of colors for clusters of the
represented gene sets, with as many levels as the value of N_clusters.
If no clusters were represented, than this is NULL.
ddr a dendrogram object with the reordering used for the
heatmap. See heatmap.2 function from package
gplots.
clustersExport a data frame with 2 variables containing the two
following variables :
GeneSet: the gene sets
clustered.
Cluster: the cluster they belong to.
The data
frame is order by the variable Cluster.
On the heatmap, each line corresponds to a gene set, and each column to a time point.
First a heatmap is computed on all the patients (see plot.TcGSA
and clustTrend) to define the clustering. Then, the clustering
and coloring thus defined on all the patients are consistently used in the
separate heatmaps that are plotted by patient.
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 descript is FALSE, the second element of margins can
be reduced (for instance use margins = c(5, 10)), as there is not so
much need for space in order to display only the gene set names, without
their description.
The median shown in the heatmap uses the respectively standardized (reduced and centered) expression of the genes over the patients.
Hejblum BP, Skinner J, Thiebaut R, (2015) Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. PLOS Comput. Biol. 11(6):e1004310. doi: 10.1371/journal.pcbi.1004310
# NOT RUN {
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)
plotPat.TcGSA(x=tcgsa_sim_1grp, expr=expr_1grp,
Subject_ID=design$Patient_ID, TimePoint=design$TimePoint,
B=100,
time_unit="H"
)
plotPat.TcGSA(x=tcgsa_sim_1grp, expr=tcgsa_sim_1grp$Estimations,
Subject_ID=design$Patient_ID, TimePoint=design$TimePoint,
baseline=1,
B=100,
time_unit="H"
)
}
# }
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