Towards a gold standard for benchmarking gene set enrichment analysis, bioRxiv, 2019-06-19

AbstractBackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.ResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GOKEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.ConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.Availability<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpbioconductor.orgpackagesGSEABenchmarkeR>httpbioconductor.orgpackagesGSEABenchmarkeR<jatsext-link>

biorxiv bioinformatics 100-200-users 2019

Transcriptome Dynamics Reveals Progressive Transition from Effector to Memory in CD4+ T cells, bioRxiv, 2019-06-19

AbstractCD4+ T cells are repositories of immune memory, conferring enhanced immunity to many infectious agents. Studies of acute viral and bacterial infection suggest that memory CD4+ T cells develop directly from effectors. However, delineating these dynamic developmental pathways has been challenging. Here, we used high-resolution single-cell RNA-seq and temporal mixture modelling to examine the fate of Th1 and Tfh effector cells during non-lethal Plasmodium infection in mice. We observed linear Th1 and Tfh pathways towards memory, characterized by progressive halving in the numbers of genes expressed, and partial transcriptomic coalescence. Low-level persisting infection diverted but did not block these pathways. We observed in the Th1-pathway a linear transition from Th1 through a Tr1 state to TEM cells, which were then poised for Th1 re-call. The Tfh-pathway exhibited a modest Th1-signature throughout, with little evidence of Tr1 development, and co-expression of TCM and memory Tfh markers. Thus, we present a high-resolution atlas of transcriptome dynamics for naïve to memory transitions in CD4+ T cells. We also defined a subset of memory-associated genes, including transcription factors Id2 and Maf, whose expression increased progressively against the background of transcriptomic quiescence. Single-cell ATAC-seq revealed substantial heterogeneity in chromatin accessibility in single effectors, which was extensively, though incompletely reset and homogenized in memory. Our data reveal that linear transitions from effector to memory occur in a progressive manner over several weeks, suggesting opportunities for manipulating CD4+ T cell memory after primary infection.Highlights<jatslist list-type=bullet><jatslist-item>scRNA-seq reveals progressive transition from effector to memory in CD4+ T cells.<jatslist-item><jatslist-item>Transcriptome dynamics suggest linear not branching models for memory development.<jatslist-item><jatslist-item>A subset of genes associates with gradual onset of CD4+ T cell memory.<jatslist-item><jatslist-item>Th1Tfh predisposition varies among clonotypes with identical antigen-specificity.<jatslist-item><jatslist-item>scATAC-seq uncovers non-coding “memory” elements in the genome.<jatslist-item>

biorxiv immunology 0-100-users 2019

 

Created with the audiences framework by Jedidiah Carlson

Powered by Hugo