A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set–specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, showing greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low- and high-background data sets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to data set–specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.
Post-infectious uveitis describes the condition of chronic immune mediated ocular inflammation associated with pathogens such as Mycobacterium tuberculosis (Mtb). Mtb associated post-infectious uveitis can be modeled in mice by intravitreal injection of heat-killed Mtb (HKMtb). To better understand how prior systemic exposure to the pathogen alters the local immune response to Mtb, we used flow cytometry and multiplex ELISAs to compare ocular responses to intravitreal HKMtb in the presence or absence of a systemic “prime” of HKMtb. Priming resulted in exacerbation of local inflammation with significantly increased clinical and histologic inflammation scores and increased vitreous cytokines concentrations one day after intravitreal injection of HKMtb. Seven days after injection, uveitis in unprimed animals had largely resolved. In contrast in primed animals, clinical signs of chronic inflammation were associated with a significant increase in the number of ocular T cells, NK cells, and Ly6Chi macrophages and increasing vitreous concentrations of IL-17, VEGF, MIG(CXCL9), IP-10(CXCL10), IL-12p40 and MIP-1α(CCL3). In mice lacking mature T and B cells (RAG2 deficient), the impact of priming on the ocular immune response was ameliorated with significantly lower vitreous cytokine concentrations and spontaneous resolution of uveitis. Altogether these results suggest that the ocular response to Mtb is exacerbated by prior systemic Mtb infection and chronic post-infectious uveitis is mediated by local production of cytokines and chemokines that amplify Th17 and Th1 responses. This mouse model of chronic Mtb associated uveitis will help elucidate mechanisms of disease in patients with post-infectious uveitis.
Automated cell filtering for single-cell RNA sequencing data. See the Genome Research paper for details on dropkick's guiding principles and validation.
A Python script using NetworkX to write a paper that investigates the eigenvector centrality, betweenness, and closeness of the patient transfer network due to the influx of COVID-19 patients
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