Julia Wrobel

An Analytic Pipeline to Obtain Reliable Genetic Ancestry Estimates from Tumor-Derived RNA Sequencing Data

Abstract Background: Germline genetics may influence tumor molecular characteristics and ultimately cancer survival. Studies of tumor characteristics, including our epithelial ovarian cancer (EOC) studies of Black women in the United States, may have RNA sequencing (RNA-seq) data from archival tumor tissue but lack germline DNA for at least some individuals. Incomplete germline DNA measurements impede analyses of important measures such as global genetic ancestry, often used in downstream analyses, by reducing sample sizes. Methods: The study population consists of 184 women who participated in two population-based studies of EOC with both germline and formalin-fixed, paraffin-embedded (FFPE) tumor samples and an additional 58 women diagnosed with EOC from the same two studies with only FFPE tumor tissue. We used tumor RNA-seq data to calculate proportions of African, European, and Asian genetic ancestry using a pipeline built on the packages SeqKit, HISAT2, SAMtools, BCFtools, PLINK, and ADMIXTURE. Women from the 1000 Genomes Project were used as the reference populations, and germline genetic ancestry estimates from blood or saliva were used as the baseline comparison. We evaluated multiple quality control strategies to improve genetic ancestry estimation. Results: Correlations between tumor RNA-seq–derived estimates of genetic ancestry from our pipeline and germline-derived African and European genetic ancestry ranged between 0.76 and 0.94. Conclusions: RNA-seq data from archival FFPE tumor tissue can be confidently and efficiently used to approximate global genetic ancestry in an admixed population when germline DNA is unavailable. Impact: This approach supports analyses of genetic ancestry and cancer when germline samples are not available.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

Abstract Motivation Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. Results To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. Availability and implementation The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.