This page describes the supplementary material for the
derfinder software paper. All the
R Markdown source files used to analyze the data for this project as well as generate the HTML reports are available in this website. However, it is easier to view them at github.com/leekgroup/derSupplement.
This section of the website describes the code and reports associated with the BrainSpan data set that are referred to in the paper and Supplementary Methods and Results.
There are 9 main
bash scripts named _step1-*_ through _step9-*_ for running the expressed regions-level and single base-level approaches.
bash script, run-all.sh, can be used to run the main 9 steps (or a subset of them).
All scripts show at the beginning the way they were used. Some of them generate intermediate small
bash scripts, for example one script per chromosome for the analyzeChr step. For some steps, there is a companion
R Markdown code file when the code is more involved or an HTML file is generated in the particular step.
The check-analysis-time.R script was useful for checking the progress of the step3-analyzeChr jobs and detect whenever a node in the cluster was presenting problems.
We expect that these scripts will be useful to
derfinder users who want to automate the single base-level and/or expressed regions-level analyses for several data sets and/or have the jobs run automatically without having to check if each step has finished running.
Note that all
bash scripts are tailored for the cluster we have access to which administer job queues with Sun Grid Engine (SGE).
This HTML report contains basic information on the
derfinder (Collado-Torres, Frazee, Love, Irizarry, et al., 2015) results from the BrainSpan data set. The report answers basic questions such as:
It also illustrates three clusters of candidate differentially expressed regions (DERs) from the single base-level analysis. You can view the report by following this link:
This HTML report has the code for loading the R data files and generating the CSV files. The report also has Venn diagrams showing the number of candidate DERs from the single base-level analysis that overlap known exons, introns and intergenic regions using the UCSC hg19 annotation. It also includes a detailed description of the columns in the CSV file.
This HTML report has code for reading and processing the time and memory information for each job extracted with efficiency_analytics (Frazee, 2014). The report contains a detailed description of the analysis steps and tables summarizing the maximum memory and time for each analysis step if all the jobs for that particular step were running simultaneously. Finally, there is an interactive table with the timing results.
The script mergeInfo.R takes several phenotype tables and merges them into a single one. This information is then used by the select_samples.R script for choosing the 24 samples to analyze. These samples have a RIN greater than 7 and are from subjects that have samples from the heart (left ventricle), testis and liver. The script create_meanCov.R creates a mean coverage BigWig file just as you would get from using
Rail-RNA (Nellore, Collado-Torres, Jaffe, Alquicira-Hernández, et al., 2015) on only these 24 samples. The actual script for running
Rail-RNA on the GTEx data are described at the nellore/runs GitHub repository. The scripts run-railMatrix.sh and railMatrix.R then run
railMatrix() using derfinder version 1.5.19 to identify the expressed regions. The resulting set of regions is then analyzed with the analyze_gtex.R script.
We analyzed the simulation reads with the following pipelines:
HISAT(Kim, Langmead, and Salzberg, 2015), summarize with
Rsubread::featureCounts()at the exon-level with and without the complete annotation, identify differentially expressed exons with
DESeq2(Love, Huber, and Anders, 2014) or
edgeR-robust (Zhou, Lindsay, and Robinson, 2014).
HISAT, summarize transcripts with
StringTie(Pertea, Pertea, Antonescu, Chang, et al., 2015), and test at the transcript and exon levels with
HISAT, summarize with
derfinder::regionMatrix(), and test with
Rail-RNA(Nellore, Collado-Torres, Jaffe, Alquicira-Hernández, et al., 2015), summarize with
derfinder::railMatrix(), and test with
Here we list the role of different scripts.
HISATis in the run-paired-hisat.sh script while the code for aligning with
Rail-RNAis in prep-manifest.R and run-rail.sh scripts.
Rsubread::featureCounts()at the exon level with the complete and incomplete annotation respectively.
StringTiewith the complete and incomplete annotation creating the input needed to run
ballgown(Frazee, Pertea, Jaffe, Langmead, et al., 2015) analyses at the transcript and exon levels.
featureCounts()to perform differential expression tests using
The report evaluate (source evaluate.Rmd) defines different reference sets one could consider. It then takes the results from all the different pipelines and compares them against these reference sets. The report includes summary tables from these results showing the minimum and maximum empirical power, false positive rate and false discovery rate. The main results are highlighted in the paper. Finally timing (source timing.Rmd) shows information about the timing and computer resources used by the different pipelines for the simulation analysis.
The code used for generating the panels using in figure showing the expressed regions-level approach is available in the figure-expressed-regions.R file.
The code used for generating the panels using in the figure showing the single base-level approach is available in the figure-single-base.R file.
R source files have the code for reproducing additional analyses described in the paper
These scripts also include other exploratory code.
Date this page was generated.
##  "2016-03-21 10:08:47 EDT"
Wallclock time spent generating the report.
## Time difference of 1.351 secs
R session information.
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## Packages ---------------------------------------------------------------------------------------------------------------
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You can view the source
R Markdown file for this page at index.Rmd.
 L. Collado-Torres, A. C. Frazee, M. I. Love, R. A. Irizarry, et al. “derfinder: Software for annotation-agnostic RNA-seq differential expression analysis”. In: bioRxiv (2015). DOI: 10.1101/015370. URL: http://www.biorxiv.org/content/early/2015/02/19/015370.abstract.
 A. C. Frazee, G. Pertea, A. E. Jaffe, B. Langmead, et al. “Ballgown bridges the gap between transcriptome assembly and expression analysis”. In: Nature Biotechnology (2015).
 D. Kim, B. Langmead and S. L. Salzberg. “HISAT: a fast spliced aligner with low memory requirements”. In: Nature Methods (2015).
 A. Nellore, L. Collado-Torres, A. E. Jaffe, J. Alquicira-Hernández, et al. “Rail-RNA: Scalable analysis of RNA-seq splicing and coverage”. In: bioRxiv (2015).
 M. Pertea, G. M. Pertea, C. M. Antonescu, T. Chang, et al. “StringTie enables improved reconstruction of a transcriptome from RNA-seq reads”. In: Nature Biotechnology (2015).
 Y. Xie. “knitr: A Comprehensive Tool for Reproducible Research in R”. In: Implementing Reproducible Computational Research. Ed. by V. Stodden, F. Leisch and R. D. Peng. ISBN 978-1466561595. Chapman and Hall/CRC, 2014. URL: http://www.crcpress.com/product/isbn/9781466561595.
 X. Zhou, H. Lindsay and M. D. Robinson. “Robustly detecting differential expression in RNA sequencing data using observation weights”. In: Nucleic Acids Research 42 (2014), p. e91.