ComBat-seq is a batch effect adjustment tool for bulk RNA-seq count data. It is an improved model based on the popular ComBat[1], to address its limitations through novel methods designed specifically for RNA-Seq studies. ComBat-seq takes untransformed, raw count matrix as input. Same as ComBat, it requires a known batch variable.

We use a negative binomial regression to model batch effects, then provide adjusted data by mapping the original data to an expected distribution if there were no batch effects. This approach better captures the properties of RNA-Seq count data compared to the Gaussian distribution assumed by ComBat. ComBat-seq specifies different dispersion parameters across batches, allowing for flexible modeling of the variance of gene expression. In addition, ComBat-seq provides adjusted data which preserves the integer nature of counts, so that the adjusted data are compatible with the assumptions of state-of-the-art differential expression software (e.g. edgeR, DESeq2, which specifically request untransformed count data).

ComBat-seq was recently published in NAR genomics and bioinformatics. Whenever using ComBat-seq, please cite:

Yuqing Zhang, Giovanni Parmigiani, W Evan Johnson, ComBat-seq: batch effect adjustment for RNA-seq count data, NAR Genomics and Bioinformatics, Volume 2, Issue 3, 1 September 2020, lqaa078, https://doi.org/10.1093/nargab/lqaa078

This repository is not a package for ComBat-seq. It stores files to reproduce the results and figures in our paper.

ComBat-seq is available in Bioconductor sva v3.36.0[2,3], please download and install following instructions in the link to Bioconductor sva. I am not a maintainer for the Bioconductor sva package, and may not be able to address some issues in that version (many of the issues are related to package dependencies and versions).

ComBat-seq is also included in a working version of sva which I built. To use ComBat-seq from my GitHub, you will need to properly install devtools package, and download the sva package with

# install.packages("devtools")
devtools::install_github("zhangyuqing/sva-devel")

Basic usage (users need to input at least two parameters - a raw count matrix from RNA-Seq studies, without any normalization or transformation, and a vector for batch separation):

count_matrix <- matrix(rnbinom(400, size=10, prob=0.1), nrow=50, ncol=8)
batch <- c(rep(1, 4), rep(2, 4))

adjusted <- ComBat_seq(count_matrix, batch=batch, group=NULL)

In ComBat-seq, user may specify biological covariates, whose signals will be preserved in the adjusted data. If the user would like to specify one biological variable, they may use the group parameter:

group <- rep(c(0,1), 4)
adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=group)

If users wish to specify multiple biological variables, they may pass them as a matrix or data frame to the covar_mod parameter:

cov1 <- rep(c(0,1), 4)
cov2 <- c(0,0,1,1,0,0,1,1)
covar_mat <- cbind(cov1, cov2)
adjusted_counts <- ComBat_seq(count_matrix, batch=batch, group=NULL, covar_mod=covar_mat)

Thanks for trying out ComBat-seq! Though I hope to answer as many of your questions as I can, my bandwidth is quite limited, unfortunately. Please refer to the list below for guidance first before reaching out to me. Thank you for your patience to any delay in my response to questions and issues.

I can't find ComBat-seq after installing.

I do not have that issue on my end, and was able to find the function following these steps: 1) remove the previous sva installed: remove.packages("sva"), 2) install with devtools (see Installation section), 3) check packageVersion("sva") to make sure it is version 3.35.2, and 4) make sure to call function by the right name sva::ComBat_seq.

If the problem persists, others have cloned the repository, and sourced both the ComBat_seq.R and helper_seq.R scripts, which works for them. I do not have a better solution at the moment.

Also, there is a auxiliary function named vec2mat contained in helper_seq.R. If you ran into any issue that R cannot find this function, helper_seq.R is where it is.

The function returns a list rather than the adjusted matrix.

I have fixed this issue in the sva on my GitHub, by converting input data into a matrix. If you are using the Bioconductor version (which we hope will be updated soon), please convert the input data (as.matrix(count_matrix)), and make sure the count matrix has R matrix data type.

Can I use ComBat-seq to set a reference batch / adjust multiple variables / on (or to create) TPM, FPKM, other normalized data / in other creative ways?

My first version of ComBat-seq is pretty simple. It does not allow reference batch options, and only considers 1 batch variable. There are certainly many ways to work with ComBat-seq to deal with flexible settings, so please feel free to be creative :)

• sim_DEpipe.R, sim_DEpipe_helpers.R
  • Pipeline and helper functions for simulations. Run sim_DEpipe.R to produce the simulation results.
  • Usage: Rscript sim_DEpipe.R <mean batch effect> <dispersion batch effect> <total number of samples>
  • True and false positive rates will be stored in CSV files.
  • Modify the parameters to change the study design, level of biological signal, sequencing depth, etc.
• qsub_simDEpipe.py
  • Script to run (qsub to cluster) multiple experiments
• visualize.R, visualize_helpers.R
  • Script and helper functions to visualize the simulation results. Run visualize.R to generate the plot based on the CSV result files.
  • Change the paths to files if necessary.

• gfrn_application.R, gfrn_DE.R, gfrn_helpers.R
  • Script and helper functions for application example on the GFRN signature dataset. Run gfrn_application.R for the PCA analysis. Run gfrn_DE.R for differential expression analysis.
  • Change the paths to files at the top of the script, if necessary.
• signature_data.rds
  • RDS object for the cleaned signature dataset, published[4] and used in our previous work[5,6].
• ras-pathway-gene-names.csv
  • genes in RAS signaling pathway, obtained from NCI website.

1. Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118-127.
2. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012). The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics, 28(6), 882-883.
3. Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE, Storey JD, Zhang Y, Torres LC (2019). sva: Surrogate Variable Analysis. R package version 3.34.0.
4. Rahman, M., MacNeil, S. M., Jenkins, D. F., Shrestha, G., Wyatt, S. R., McQuerry, J. A., ... & Bild, A. H. (2017). Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes. Genome medicine, 9(1), 40.
5. McQuerry, J. A., Jenkins, D. F., Yost, S. E., Zhang, Y., Schmolze, D., Johnson, W. E., ... & Bild, A. H. (2019). Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes. BMC cancer, 19(1), 881.
6. Zhang, Y., Jenkins, D. F., Manimaran, S., & Johnson, W. E. (2018). Alternative empirical Bayes models for adjusting for batch effects in genomic studies. BMC bioinformatics, 19(1), 262.