Extended Data Fig. 4: Detection of CD45 isoforms and T cell subset classification in 5’ RNA-sequencing data.

a, Illustration of signal used by CD45 isoform detection model. For each read in an illustrative sample, a histogram of the fragment length assuming no splicing (RABC isoform) is shown. The distribution of reads beyond exon 3 become shifted if they come from an isoform lacking an upstream exon. b, A histogram of the expected fragment length after inference of each read is shown for the same sample in blue. The gaussian distribution modeling fragment lengths inferred by EM is plotted in orange. c, U-MAP representation of T cells in 10x healthy PBMC demonstration dataset with both RNA-sequencing and feature-barcoding measurements. Dataset is colored by CD45RA and CD45RO expression measured by RNA (top, with k = 20 knn smoothing as applied in the paper) and feature barcoding (bottom). d, Kernel density estimate plots sorting plots of cells into different memory subsets. Black lines represent cutoff used for gating. e, Confusion matrix showing concordance of cell classification by protein-based and RNA-based approaches. f, Scatterplot showing similarity of cell fraction measurements using either the RNA-based (x-axis) or protein-based (y-axis) measurements. g, Kernel density estimate distributions of knn-smoothed (k = 20) CD45RA (x-axis) and CD45RO (y-axis) expression measurements in our dataset for Baseline T cells, Infusion products, day 7 CAR- cells and day 7 CAR+ cells. A plot is shown for each sample, and the CD45RA cutoff used for classification is drawn with a blue line. All plots share x and y axis scales.