ctsGE-clustering subgroups of expression data.
CONCLUSIONS
A pre-requisite to clustering noisy data, such as gene-expression data, is the filtering step. As an alternative to this step, the ctsGE R-package applies a sorting step in which all of the data are divided into small groups. The groups are divided according to how the time points are related to the time-series median. Then clustering is performed separately on each group. Thus, the clustering is done in two steps. First, an expression index (i.e. a sequence of 1, -1 and 0) is defined and genes with the same index are grouped together, and then each group of genes is clustered by k-means to create subgroups. The ctsGE package also provides an interactive tool to visualize and explore the gene-expression patterns and their subclusters. ctsGE proposes a way of organizing and exploring expression data without eliminating valuable information.


METHODS
Freely available as part of the Bioconductor project at https://bioconductor.org/packages/ctsGE/ .


BACKGROUND
ron@agri.gov.il.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Fruit Tree Sciences, Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon Lezion, Israel.
Michal
M
Sharabi-Schwager
Department of Fruit Tree Sciences, Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon Lezion, Israel.
Etti
E
Or
Department of Fruit Tree Sciences, Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon Lezion, Israel.
Ron
R
Ophir
D000230:Adenocarcinoma / Q000235:genetics / Q000378:metabolism; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software