MetaDiff is a Java/R-based software package that performs differential expression analysis on RNA-Seq based data. By utilizing a meta-regression framework, it is able to take advantage of the information regarding the variance of the estimates to make the inference more accurate. Meta-regression also enables incorporation of covariates other than experimental group, which makes it extremely simple to adjust for confounding parameters in an experiment.
When performing a post hoc differential expression analysis, the currently available packages only take into account the counts or FPKM output generated by the upstream software, discarding the variance information. This will often inflate the alpha-values, and lead to false positives or overly conservative inference. Meta-regression models address this issue by directly utilizing the variance output from upstream software, and
This program can only be run in a Unix-like operating system. Tested on Ubuntu Linux 14.04 LTS, Red Hat Enterprise Linux Server release 6.6 (Santiago), and OS X Yosemite on my own MacBook Air.
This program requires:
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Java Runtime SE 7/8 http://www.oracle.com/technetwork/java/javase/downloads/jre8-downloads-2133155.html
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R packages: doParallel, metatest, plyr
Make sure Rscript command is in your PATH variable.
A compiled JAR package is ready for download at: http://github.com/jiach/MetaDiff/blob/master/out/artifacts/MetaDiff_jar/MetaDiff.jar?raw=true
Download this JAR file to any location to your liking (for example /home/mason/myfavoriaternaseqprograms/MetaDiff.jar), and invoke the command as described in Usage.
java -jar /home/mason/myfavoriaternaseqprograms/MetaDiff.jar -input_file_list input_list.tsv -output_dir /home/mason/metadiff_results/ -method cufflinks
These three parameters are required:
input_file_list - this is a tab-delimited file that contains the file locations and covariate information separated by tabs with header line. Here's an example:
| Sample | File_Name | C_group | age | C_gender |
|---|---|---|---|---|
| simu1 | /home/mason/awesomeRNASeqData/simu1/isoforms.fpkm_tracking | 0 | 40 | female |
| simu2 | /home/mason/awesomeRNASeqData/simu2/isoforms.fpkm_tracking | 0 | 38 | male |
| simu3 | /home/mason/awesomeRNASeqData/simu3/isoforms.fpkm_tracking | 0 | 56 | male |
| simu4 | /home/mason/awesomeRNASeqData/simu4/isoforms.fpkm_tracking | 1 | 52 | male |
| simu5 | /home/mason/awesomeRNASeqData/simu5/isoforms.fpkm_tracking | 1 | 47 | male |
| simu6 | /home/mason/awesomeRNASeqData/simu6/isoforms.fpkm_tracking | 1 | 41 | female |
The first two columns indicate the sample_id and the file_names associated with that sample_id. Sample_ID can be any unique identifier of your sample (no two sample IDs should be the same). Use absolute path (path starting from root /) in File_name to avoid confusion of the program.
There can be any number of covariates in any order. Names of the covariates need to follow the conventions of R variable names. The rule of thumb is to use letters, underscore and numbers, and to start the name with a letter. Categorical covariates NEED to be prefixed with C_ in their names.
The C_group variable is the default variable name for the experimental group. Please do not use any other name for the group variable. The program will look specifically for this covariate to determine the group to which each subject belongs. If you need cv_threshold adjustment, this variable has to be present in the list file, and needs to be named C_group.
IMPORTANT: Please make sure you have ENOUGH samples for each category. Otherwise, meta-regression will not perform and errors will occur!
output_dir - again, use absolute path to avoid confusion.
method - indicates which software package generated the files in the input_file_list, as of right now the program supports 'cufflinks' and 'mmseq'.
Some optional parameters can also be provided:
cv_threshold - the program automatically removes samples with coefficient of variations from all groups >1. However, if you need further filtering with regards to CV, you can provide your cut off here.
The program will label the samples with CVs from ALL groups > cv_threshold as STATUS = FAILED in the output.
mean_fpkm_threshold - the program by default removes samples with the average FPKM from all groups <= 1. If you want to change this parameter, you can provide your own cut-off here.
num_cores - the program supports parallel computing with plyr and doParallel package in R. This parameter indicates the number of concurrent processes you wish to run when performing meta-regression with R. By default the program will only run 1 process.
If you have a multi-core system, I would highly recommend enabling this option by providing the number of cores you wish to use, because it will dramatically increase the speed of the program.
verbose - print the messages on screen in addition to writing it to log files.
Notice that the program works on both gene and isoform-level DE experiments. Just provide corresponding files generated from upstream software (cufflinks or mmseq). Since the formats are identical for these packages, MetaDiff treats gene and isoform-level DE analyses identically.
The program will generate the following files in the output_dir provided:
metadiff_results.tsv - a tab-delimited file containing the results of meta-regression. Usually it will be made of these columns:
Feature - names of the isoform or gene
Convergence - convergence status output by metatest;
tval_CovariateName - t-test statistics for each covariate;
dfttest - degrees of freedom for the t-test;
pttest_CovariateName - t-test p-values for each covariate;
bartLLR_CovariateName - Bartlett corrected LLR statistics for each covariate;
pBartlett_CovariateName - Bartlett corrected LLR p-value for each covariate;
Status - OK indicates that the model has converged and the CV of FPKM for all groups are <= cv_threshold
Some temporary files are also created:
fpkm.mat - temporary files containing all the fpkm values, their variances and the covariates of the corresponding sample.
run_metatest.R - R script to perform metatest.
metadiff.log - log file containing all the operations performed by MetaDiff.
metadiff_r.log - standard error output from the R script;
NB: As of right now, MetaDiff has yet to implement FDR adjustments of the P-values generated from the script. You need to do it manually in your favorite statistical packages. In future versions, this function will be incorporated into the MetaDiff package.
The author of this package is Cheng Jia, a Ph.D. candidate advised by Dr. Mingyao Li in the Department of Biostatistics and Epidemiology at University of Pennsylvania. Any questions with regards to the functions of this package should be directed to Cheng Jia. The E-mail of the author is: jiacheng AT mail.med.upenn.edu.
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