Author: Christoph Muus; Malte D Luecken; Gokcen Eraslan; Avinash Waghray; Graham Heimberg; Lisa Sikkema; Yoshihiko Kobayashi; Eeshit Dhaval Vaishnav; Ayshwarya Subramanian; Christopher Smillie; Karthik Jagadeesh; Elizabeth Thu Duong; Evgenij Fiskin; Elena Torlai Triglia; Christophe Becavin; Meshal Ansari; Peiwen Cai; Brian Lin; Justin Buchanan; Sijia Chen; Jian Shu; Adam L Haber; Hattie Chung; Daniel T Montoro; Taylor Adams; Hananeh Aliee; Samuel J Allon; Zaneta Andrusivova; Ilias Angelidis; Orr Ashenberg; Kevin Bassler; Christophe Becavin; Inbal Benhar; Joseph Bergenstrahle; Ludvig Bergenstrahle; Liam Bolt; Emelie Braun; Linh T Bui; Mark Chaffin; Evgeny Chichelnitskiy; Joshua Chiou; Thomas M Conlon; Michael S Cuoco; Marie Deprez; David S Fischer; Astrid Gillich; Joshua Gould; Minzhe Guo; Austin J Gutierrez; Arun C Habermann; Tyler Harvey; Peng He; Xiaomeng Hou; Lijuan Hu; Alok Jaiswal; Peiyong Jiang; Theodoros Kapellos; Christin S Kuo; Ludvig Larsson; Michael A Leney-Greene; Kyungtae Lim; Monika Litvinukova; Ji Lu; Leif S Ludwig; Wendy Luo; Henrike Maatz; Elo Maddissoon; Lira Mamanova; Kasidet Manakongtreecheep; Charles-Hugo Marquette; Ian Mbano; Alexi M McAdams; Ross J Metzger; Ahmad N Nabhan; Sarah K Nyquist; Jose Ordovas-Montanes; Lolita Penland; Olivier B Poirion; Segio Poli; CanCan Qi; Daniel Reichart; Ivan Rosas; Jonas Schupp; Rahul Sinha; Rene V Sit; Kamil Slowikowski; Michal Slyper; Neal Smith; Alex Sountoulidis; Maximilian Strunz; Dawei Sun; Carlos Talavera-Lopez; Peng Tan; Jessica Tantivit; Kyle J Travaglini; Nathan R Tucker; Katherine Vernon; Marc H Wadsworth; Julia Waldman; Xiuting Wang; Wenjun Yan; Ali Onder Yildirim; William Zhao; Carly G K Ziegler; Aviv Regev
Title: Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells Document date: 2020_4_20
ID: nkql7h9x_91
Snippet: Pseudo-bulk data was generated by computing the mean for each gene expression value and nUMI covariate for cells in the same cell type and donor. After filtering as described above, models (2) and (3) were fit to the data. In contrast to the single-cell model, pseudo-bulk analysis underestimates certainty in modeled effects as uncertainty in the pseudo-bulk means are not taken into account. Thus, we used only effect directions from pseudo-bulk an.....
Document: Pseudo-bulk data was generated by computing the mean for each gene expression value and nUMI covariate for cells in the same cell type and donor. After filtering as described above, models (2) and (3) were fit to the data. In contrast to the single-cell model, pseudo-bulk analysis underestimates certainty in modeled effects as uncertainty in the pseudo-bulk means are not taken into account. Thus, we used only effect directions from pseudo-bulk analysis to validate singlecell associations. We regarded only those associations as significant, where the FDR-corrected pvalue in the single-cell model is below 0.05, and the sign of the estimated effect is consistent in both the single-cell and the pseudo-bulk analysis.
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