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Cascade

Mis à jour le 31 May 2013


The Cascade package is an implementation of the statistical methodology developed in Vallat et al. 2013. [1]

It was published as an Application Note - Gene Expression - in Bioinformatics :

Cascade: a R package to study, predict and simulate the diffusion of a signal through a temporal gene network
Nicolas Jung, Frédéric Bertrand, Seiamak Bahram, Laurent Vallat, and Myriam Maumy-Bertrand
Bioinformatics (2014) 30 (4): 571-573 first published online December 3, 2013 doi:10.1093/bioinformatics/btt705

Cascade ressource page
Cascade source code
Cascade CRAN page

Feel free to email me for any comment on the package : fbertran@math.unistra.fr.
Other tools for deciphering networks on my webpage

Two package’s vignettes package, carrying out the comprehensive analysis of two example datasets, and the user manual are available below.

PDF - 1.2 Mb
Vignette 1 : Introduction to Cascade
PDF - 556.1 kb
Vignette 2 : E-MTAB-1475 Re-analysis
PDF - 115.1 kb
Cascade manual

Supplemental files for the second vignette :

Excel - 428 kb
genelists.xls
PDF - 291.2 kb
genelists.pdf

The first dataset, extract from GSE39411, is based on the transcriptional response of healthy lymphocyte B-cells after antigenic stimulation. [2]

The three next graphical displays are extracted from the first vignette: "Introduction to the Cascade package and application to the GSE39411 dataset"


Figure 1. Step 1: gene selection in GSE39411 and assignment to a time cluster.

Figure 2. Step 2: reverse-engineering of the network in GSE39411, nodes represent genes and the arrows statistical links between the genes. Arrows’ thickness depicts the intensity of the link.

Figure 3. Step 3: predicted perturbations in the network, at the 2nd time point, after gene expression modulation at an early time in the temporal GRN of GSE39411. The green influential gene is supposed to be knocked-down. Color scale legend from downregulated (blue) to upregulated (red) genes.

The second dataset (E-MTAB-1475) has a different experimental design and is based on the transcriptional response of murine lymphocytes T-cells after an in vitro stimulation that sustains cellular differentiation. [3]

The three next graphical displays are extracted from the second vignette: "Additional application of the Cascade package to E-MTAB-1475 dataset"


Figure 1. Step 1: gene selection in E-MTAB-1475 and assignment to a time cluster.

Figure 2. Step 2: reverse-engineering of the network in E-MTAB-1475, nodes represent genes and the arrows statistical links between the genes. Arrows’ thickness depicts the intensity of the link.

Figure 3. Step 3: predicted perturbations in the network, at the 2nd time point, after gene expression modulation at an early time in the temporal GRN of E-MTAB-1475. The green influential gene is supposed to be knocked-down. Color scale legend from downregulated (blue) to upregulated (red) genes.

You can export the results of the Cascade package to your favorite tool for visualizing networks. Applying cytoscape to an extract of the first dataset GSE39411, we produced the following graphical outputs.




Footnotes

[1Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F. , Meyer N., Pocheville A., Fisher J. W. III, Gribbenh J. G. and Bahram S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110 (2), 459-464

[2Vallat, L., Park, Y., Li, C., and Gribben, J. G. (2007). Temporal genetic program following b-cell receptor cross-linking: altered balance between proliferation and death in healthy and malignant b-cells. Blood, 109(9), 3989-3997

[3Van den Ham, H.-J., Waal, L., Zaaraoui-Boutahar, F., Bijl, M., IJcken, W. F., Osterhaus, A. D., Boer, R. J., and Andeweg, A. C. (2013). Early divergence of th1 and th2 transcriptomes involves a small core response and sets of transiently expressed genes. European Journal of Immunology, 43(4), 1074-1084

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