To allow detection of UMI counts within non-cell-containing droplets, unfiltered count matrices from each modality were loaded into a ‘Seurat’ (v3.1.4) object (Stuart et al., 2019). Samples were demultiplexed by their unique HTO using the Seurat function ‘MULTIseqDemux’ yielding 19,560 demultiplexed cells. This allowed the removal of 3724 (19%) cross-sample doublets. Due to the shallow sequencing of the mRNA library (~4000 reads/cell), expression of at least 60 genes and a percent mitochondrial reads below 15% were used to remove barcodes from non-viable cells or debris (2499 or 15% of cells removed). Intra-sample doublets were removed using the ‘scDblFinder’ (v1.1.8) R package (392 cells removed). UMI counts from ADTs were normalized using default configuration of the DSB (v0.1.0) R package with ADT signal from HTO-negative droplets used as empty drop matrix and using included isotype controls. Gene expression was preprocessed using the default Seurat v3 pipeline, and fine-grained clusters were identified using the ‘FindClusters’ function with a resolution of 1.2. Clusters were annotated by lineages and cell types using their distinct expression of markers within the mRNA or ADT modality and aided by cell-by-cell annotation from the SingleR R package (v1.4.0) using the ‘Monaco reference’ from the celldex R package (v1.0.0) made from bulk RNA-seq samples of sorted immune cell populations from GSE107011 (Monaco et al., 2019). Top five differentially expressed marker genes for each cluster can be found in Figure 1—figure supplement 1D. To allow direct comparison of UMI counts across conditions, each condition was down-sampled by tissue of origin to include the same number of cells within each fine-grained cell-type cluster (resulting in 1777 cells from each PBMC sample and 1681 cells from each lung tumor sample).

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