Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases von Vallania, Francesco, Tam, Andrew, Lofgren, Shane, Schaffert, Steven, Azad, Tej D., Bongen, Erika, Haynes, Winston, Alsup, Meia, Alonso, Michael, Davis, Mark, Engleman, Edgar, Khatri, Purvesh

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Sex Differences in the Blood Transcriptome Identify Robust Changes in Immune Cell Proportions with Aging and Influenza Infection von Bongen, Erika, Lucian, Haley, Khatri, Avani, Fragiadakis, Gabriela K., Bjornson, Zachary B., Nolan, Garry P., Utz, Paul J., Khatri, Purvesh

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KLRD1-expressing natural killer cells predict influenza susceptibility von Bongen, Erika, Vallania, Francesco, Utz, Paul J, Khatri, Purvesh

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Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus von Haynes, Winston A., Haddon, D. James, Diep, Vivian K., Khatri, Avani, Bongen, Erika, Yiu, Gloria, Balboni, Imelda, Bolen, Christopher R., Mao, Rong, Utz, Paul J., Khatri, Purvesh

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Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses von Andres-Terre, Marta, McGuire, Helen M., Pouliot, Yannick, Bongen, Erika, Sweeney, Timothy E., Tato, Cristina M., Khatri, Purvesh

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Machine learning to predict lung nodule biopsy method using CT image features: A pilot study von Sumathipala, Yohan, Shafiq, Majid, Bongen, Erika, Brinton, Connor, Paik, David

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A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection von Fourati, Slim, Talla, Aarthi, Mahmoudian, Mehrad, Burkhart, Joshua G., Klén, Riku, Henao, Ricardo, Yu, Thomas, Aydın, Zafer, Yeung, Ka Yee, Ahsen, Mehmet Eren, Almugbel, Reem, Jahandideh, Samad, Liang, Xiao, Nordling, Torbjörn E. M., Shiga, Motoki, Stanescu, Ana, Vogel, Robert, Pandey, Gaurav, Chiu, Christopher, McClain, Micah T., Woods, Christopher W., Ginsburg, Geoffrey S., Elo, Laura L., Tsalik, Ephraim L., Mangravite, Lara M., Sieberts, Solveig K.

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