Description Usage Arguments Details Value Author(s) See Also Examples
This is a function to do SOM (Self Organising Maps) clustering
analysis for objects of classes maigesDEcluster
.
1 2 3 |
data |
object of class |
group |
character string giving the type of grouping: by rows 'R' or columns 'C' (default). |
distance |
char string giving the type of distance to use. Only two options are available here: 'euclidean' and 'correlation' (default). |
method |
char string specifying the linkage method for the hierarchical cluster. Possible values are 'ward', 'single', 'complete' (default), 'average', 'mcquitty', 'median' or 'centroid' |
sampleT |
list with 2 vectors. The first one specify the first letter of different sample types to be coloured by distinct colours, that are given in the second vector. If NULL (default) no colour is used. |
doHier |
logical indicating if you want to do the hierarchical branch in the opposite dimension of clustering. Defaults to FALSE. |
sLabelID |
character string specifying the sample label ID to be used to label the samples. |
gLabelID |
character string specifying the gene label ID to be used to label the genes. |
idxTest |
numerical index of the test to be used to sort the
genes when clustering objects of class |
adjP |
string specifying the method of p-value adjustment. May be 'none', 'Bonferroni', 'Holm', 'Hochberg', 'SidakSS', 'SidakSD', 'BH', 'BY'. |
nDEgenes |
number of DE genes to be selected. If a real number
in (0,1) all genes with p.value <= |
... |
additional parameters for |
This function implements the SOM clustering method for
objects resulted from differential expression analysis. The method uses
the function som
from package som. For
the adjustment of p-values in the selection of genes differentially
expressed, we use the function mt.rawp2adjp
from package multtest.
This function display the heatmaps and return invisibly an object
of class som
resulted from the function som
.
Gustavo H. Esteves <gesteves@vision.ime.usp.br>
som
from package som.
kmeansM
and hierM
for displaying k-means and
hierarchical clusters, respectively.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Loading the dataset
data(gastro)
## Doing bootstrap from t statistic test fot 'Type' sample label, k=1000
## specifies one thousand bootstraps
gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")
## SOM cluster with 2 groups adjusting p-values by FDR, and showing all genes
## with p-value < 0.05
somMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05,
xdim=2, ydim=1, topol="rect")
## SOM cluster with 4 groups adjusting p-values by FDR, and showing all genes
## with p-value < 0.05
somMde(gastro.ttest, sLabelID="Type", adjP="BH", nDEgenes=0.05,
xdim=2, ydim=2, topol="rect")
|
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