A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells.

Due to the high cost of flow and mass cytometry, there has been a recent surge in the development of computational methods for estimating the relative distributions of cell types from the gene expression profile of a bulk of cells. Here, we review the five common 'digital cytometry' methods: deconvolution of RNA-Seq, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), CIBERSORTx, single sample gene set enrichment analysis and single-sample scoring of molecular phenotypes deconvolution method. The results show that CIBERSORTx B-mode, which uses batch correction to adjust the gene expression profile of the bulk of cells ('mixture data') to eliminate possible cross-platform variations between the mixture data and the gene expression data of single cells ('signature matrix'), outperforms other methods, especially when signature matrix and mixture data come from different platforms. However, in our tests, CIBERSORTx S-mode, which uses batch correction for adjusting the signature matrix instead of mixture data, did not perform better than the original CIBERSORT method, which does not use any batch correction method. This result suggests the need for further investigations into how to utilize batch correction in deconvolution methods.

Outcome of Hypotensive Trauma Patients by Time and Day of Arrival.

BACKGROUND: Although most studies of trauma patients have not demonstrated a "weekend" or "night" effect on mortality, outcomes of hypotensive (systolic blood pressure <90 mm Hg) patients have not been studied. We sought to evaluate whether outcomes of hypotensive patients were associated with admission time and day. METHODS: We retrospectively analyzed patients from Pennsylvania Level 1 and Level 2 trauma centers with systolic blood pressure of <90 mm Hg over 5 y. Patients were stratified into four groups by arrival day and time: Group 1, weekday days; Group 2, weekday nights; Group 3, weekend days; and Group 4, weekend nights. Patient characteristics and outcomes were compared for the four groups. Adjusted mortality risks for Groups 2, 3, and 4 with Group 1 as the reference were determined using a generalized linear mixed effects model. RESULTS: After exclusions, 27 trauma centers with a total of 4937 patients were analyzed. Overall mortality was 44%. Compared with patients arriving during the day (Groups 1 and 3), those arriving at night (Groups 2 and 4) were more likely to be younger, to be male, to have lower Glasgow Coma Scale scores and blood pressures, to have penetrating injuries, and to die in the emergency room. Controlled for admission variables, odds ratios (95% confidence intervals) for Groups 2, 3, and 4 were 0.92 (0.72-1.17), 0.89 (0.65-1.23), and 0.76 (0.56-1.02), respectively, for mortality with Group 1 as reference. CONCLUSIONS: Patients arriving in shock to Pennsylvania Level 1 and Level 2 trauma centers at night or weekends had no increased mortality risk compared with weekday daytime arrivals.

TumorDecon: A digital cytometry software.

There are many experimental methods for characterizing immune profiles of tumors, such as flow and mass cytometry. However, these approaches are time and resource intensive. Thus, several "digital cytometry" methods have been developed to extract cell frequencies from RNA-seq data. Here, we introduce TumorDecon, named for its potential to deconvolve the distribution of cells from the gene expression levels of a bulk of cells, such as a tumor. The Python package provides an accessible way of applying these methods. It includes four deconvolution methods as well as several gene sets, signature matrices, and functions for generating custom signature matrices.

Data Driven Mathematical Model of Colon Cancer Progression.

Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.