Co-fuse: A recurrent fusions identification and analysis tool based on RNA-sequencing

Co-fuse implements a technique described in "Co-fuse: a recurrent fusions identification and analysis tool based on RNA-sequencing". Co-fuse identifies and compares recurrent fusion genes across different samples. It relies on the results of existing fusion gene detection tools. The program is written in R.

Requirements

In order to use Co-fuse, RStudio needs to be installed. RStudio can be installed by following this link.

Please follow this link for instuctions on how to install the following R Libraries/packages.

install.packages("dplyr")
install.packages("tsne")
install.packages("ggplots")
install.packages("rpart")
install.packages("rpart.plot")

Usage

Recurrent Fusions

The algorithm identify a set of recurrent fusion genes occurred across multiple samples. Assuming that the output of fusion software can be found in the folder SOFTWARE_RESULTS/muliple_samples (e.g., TopHat/samples), the re-current fusion algorithm can be executed as follow:

Rscript --vanilla co-fuse.R 'software' 'SOFTWARE_RESULTS/muliple_samples' './output_dir' _TSNE_PERPLEXITY_

Here 'software' is set to one of the following softwares: 'defuse', 'fusioncatcher', 'tophat', 'soapfuse' or 'generic'. _TSNE_PERPLEXITY_ represents the perplexity parameter. It can be thought as a parameter that sets the number of effective nearest neighbors. A larger or denser dataset requires a larger perplexity. Typical values for the perplexity range between 5 and 50. We use the perplexity of 6 in our experiments.

The output will be stored in the folder ./output_dir. It consists of multiple files:

• summary.AB.csv A CSV file listing recurrent genes and samples containing these recurrent genes
• barplot.AB.pdf Display recurrent genes sorted by their frequency
• heatmap.pdf Cluster analysis to discover relationship between samples
• tsne_plot.pdf Dimensionality reduction for visualizing samples using t-Distributed Stochastic Neighbor Embedding (t-SNE)

Fisher's Exact Test

Fisher’s exact test examines the relationship between the two dimensions of the contingency table. The null hypothesis is that the relative proportions of gene fusions in group 1 are the same as the relative proportions of gene fusions in group 2. In other words, these two groups are not different.

For each fusion, we calculate a two-sided p-value. If the p-value (prbability) is small, the null hypothesis can be rejected. In other words, there is a significant difference between two groups. The Fisher's exact test can be executed as follow:

Rscript --vanilla co-fuse2.R 'software' 'SOFTWARE_RESULTS/GROUP_1' 'SOFTWARE_RESULTS/GROUP_2' './output_dir'

Here 'software' is set to one of the following softwares: 'defuse', 'fusioncatcher', 'tophat', 'soapfuse' or 'generic'. 'SOFTWARE_RESULTS/GROUP_1' represents the directory containing fusion results from the first group. 'SOFTWARE_RESULTS/GROUP_2' represents the directory containing fusion results from the second group. The output table (twoGroups.csv --- in the csv format) will be stored in the folder ./output_dir.

Demonstration

Reproducing experimental results reported in the paper

We provide the demo script which will reproduce the results reported in our paper. The script will download the data set and run both R scripts as described in the paper. Please execute:

./demoFusionCatcher.sh

The downloaded data set contains FusionCatcher results (Nicorici et al., 2014) on two groups of samples: AML and MM. Each group consists of multiple samples. The first experiment (Recurrent Fusions) calls the R script co-fuse.R to compute recurrent fusions from both AML and MM. Experimental results can be found in the folder ./output_reproduce_results/AML_MM.

The second experiment (Fisher's Exact test) tests whether there exists a significant difference between two groups of samples. It calls the R script co-fuse2.R to examine the relationship between gene fusions in AML and MM. Experimental results (twoGroups.csv) will be stored in the folder ./output_reproduce_results/fisher_test.

FusionCatcher, SoapFuse, TopHat and DeFuse

We provide the demo script to illustrate the use of our software with four of popular fusion softwares: FusionCatcher (Nicorici et al., 2014), SoapFuse (Jia et al., 2013), TopHat (Kim and Salzberg, 2011) and DeFuse (McPherson et al., 2011). The demo script can be executed by:

./demoFusionCatcher.sh
./demoSoapFuse.sh
./demoTopHat.sh
./demoDeFuse.sh

Generic fusion softwares

For other popular fusion softwares, we expect the first two columns to be Gene1 and Gene2. The demo script can be executed by:

./demoGeneric.sh

If you have any questions on use fusion softwares not described here, please feel free to contact us.

Known errors

./demoXXXXXX.sh: line XX: Rscript: command not found

Rstudio has not been installed. Please follow the instruction above to install RStudio.

References

Jia, W., Qiu, K., He, M., Song, P., Zhou, Q., Zhou, F., et al. (2013). SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome biology, 14(2).

Kim, D. and Salzberg, S. L. (2011). TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome biology, 12(8), R72.

McPherson, A., Hormozdiari, F., Zayed, A., Giuliany, R., Ha, G., Sun, M. G., Griffith, M., Moussavi, A. H., Senz, J., Melnyk, N., et al. (2011). deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Computational Biology, 7(5).

Nicorici, D., Satalan, M., Edgren, H., Kangaspeska, S., Murumagi, A., Kallioniemi, O., et al. (2014). FusionCatcher - a tool for finding somatic fusion genes in paired- end RNA-sequencing data. bioRxiv.