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. Hspa1b, . Hspa6, . Fos, . Tyms, . Gadph et al., We then hierarchically clustered the correlation matrix between these genes (filtering genes with low coverage and computing correlation using a down-sampled UMI matrix) and selected the gene clusters that contained the above anchor genes. We thus retained 402 genes as features (Table S3). We used metacell to build a kNN graph, perform boot-strapped co-clustering, Gene features for metacell covers were selected using the parameter Tvm = 0.4, total umi > 30, and more than 4 UMI in at least 3 cells (using the functions mcell_gset_filter_varmean, and mcell_gset_filter_cov)

, Annotation of the metacell model was done using the metacell confusion matrix and analysis of marker genes. Detailed annotation within the myeloid, lymphoid and epithelial compartments was performed using hierarchical clustering of the metacell confusion matrix (Figure S3A) and supervised analysis of enriched genes. Metacells enriched for markers from more than one lineage (either T (TRBC2), myeloid (S100A8, C1QB), epithel (KRT18), and plasma cells (XBP1)) were marked as doublets and discarded from further analysis. We additionally discarded metacells of erythrocytes or plasma cells from further analysis. To derive cell cycle and type I interferon response co-expressed gene modules, we used a clustering-approach as described in the previous paragraphs (using the functions mcell_mat_rpt_cor_anchors and mcell_gset_split_by_dsmat) on a set of cell cycle and interferon genes. We clustered, and manually inspected the resulting clusters, Outlier cells featuring gene expresssion higher than 4-fold than the geometric mean in the metacells in at least one gene were discarded

, To extract proportion of proliferating cells (Figure 3G), we calculated for each cells the number of cell-cycle related transcripts per

, We then computed for each metacell its expected vUMI cout, based on its total UMI count (hUMI + vUMI) and the total vUMI proportion across all cells. Figure 4C shows log 2 fold change between the observed and expected UMI in each metacell, after adding a regularization factor ( = 5) for each factor. Log 2 fold change for the 27 subsets in Figure 3A, and calculated for each severe patient separately is shown in Table S2. Testing for hMPV infection specificity was done in a similar manner. However, since UMI distribution across cells was abundant and heavy-tailed, we computed for each metacell the expected number of vUMI + cells instead of its total vUMI count. A cell was determined vUMI+ if it had more than 10 viral UMI, as determined by automatic thresholding (Figure 4F). Figure 4G shows log 2 fold change between the observed and expected vUMI+ cells in each metacell, after adding a regularization factor ( = 5) for each factor. Dichotomized differential gene expression analysis ScRNA-seq data are intrinsically noisy data with a large proportion of zeros values, test for SARS-CoV-2 infection specificity in different cell populations, we computed for each metacell the total number of host UMIs (hUMI) and viral UMIs (vUMI) in the three severe patients, pp.1-3