Maintained by the Love Lab

highly used

DESeq2 usage stats

  • Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. Collaboration with Simon Anders and Wolfgang Huber (EMBL Heidelberg).

tximport usage stats

  • Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages such as edgeR, DESeq2, limma-voom. Collaboration with Charlotte Soneson and Mark Robinson (UZH Zürich)

newly developed


  • Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. Developed by Anqi Zhu (UNC-CH), collaboration with Joseph Ibrahim (UNC-CH). apeglm methods can be accessed via lfcShrink in the DESeq2 package.


  • Import transcript abundances with automagic population of metadata. Builds on top of tximport, outputs a SummarizedExperiment object with transcriptome metadata automatically added. Collaboration with Rob Patro (SBU), Charlotte Soneson (UZH), and Peter Hickey (JHU).


  • Modeling and correcting fragment sequence bias for RNA-seq transcript abundance estimation. Collaboration with Rafael Irizarry (DFCI Boston).


  • Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. Collaboration with Alena van Bömmel, Stefan Haas and Martin Vingron (MPI Berlin).


  • Efficiently calculate statistics such as group mean, standard deviation and t-statistics on large sparse genomic data sets.



  • RNA-seq workflow: gene-level exploratory analysis and differential expression. F1000R


  • Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification. F1000R



  • Software for quantifying the expression of transcripts using RNA-seq data, developed and maintained by Rob Patro (SBU). The Love lab collaborates with Dr. Patro on bias correction methods, on estimation of uncertainty through Gibbs and bootstrap sampling, and on propagation of metadata from abundance estimation to downstream analysis packages.


  • Provides infrastructure for parallel computations distributed ‘by file’ or ‘by range’. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. Collaboration with Valerie Obenchain and Martin Morgan (Bioconductor core team).


  • A series of shortcuts for routine tasks. Collaboration with Rafael Irizarry (DFCI Boston).

Data packages

airway This package provides a SummarizedExperiment object of read counts in genes for an RNA-Seq experiment on four human airway smooth muscle cell lines treated with dexamethasone. The citation for the experiment is: Himes BE et al (2014).

fission This package provides a SummarizedExperiment object of read counts in genes for a time course RNA-Seq experiment of fission yeast (Schizosaccharomyces pombe) in response to oxidative stress (1M sorbitol treatment) at 0, 15, 30, 60, 120 and 180 mins. The citation for the experiment is: Leong HS et al. (2014).

parathyroidSE This package provides SummarizedExperiment objects of read counts in genes and exonic parts for paired-end RNA-Seq data from experiments on primary cultures of parathyroid tumors. The citation for the experiment is: Haglund F et al (2012).

tximportData This packages provides output files from common transcript estimation software (Salmon, Kallisto, RSEM, Cufflinks) for demonstration of import using tximport. The files are a subset of 6 samples from the GEUVADIS project. The citation for the GEUVADIS project is: Lappalainen et al (2013)

alpineData This packages provides a subset of alignments for demonstration of alpine. The samples aligned are a subset of 4 samples from the GEUVADIS project. The citation for the GEUVADIS project is: Lappalainen et al (2013)