Benchmarking transcriptional host response signatures for infection diagnosis

Daniel G. Chawla, Antonio Cappuccio, Andrea Tamminga, Stuart C. Sealfon, Elena Zaslavsky, Steven H. Kleinstein

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.

Original languageEnglish
Pages (from-to)974-988.e7
JournalCell Systems
Issue number12
StatePublished - 21 Dec 2022


  • aging
  • bacteria
  • cross-reactivity
  • data compendium
  • infection diagnosis
  • influenza signature
  • non-infectious conditions
  • robustness
  • signature evaluation framework
  • transcriptional host response signature
  • virus


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