The quantity of tumor-infiltrating lymphocytes (TILs) in breast cancer (BC) is a robust prognostic factor for improved patient survival, particularly in triple-negative and HER2-overexpressing BC subtypes. Although T cells are the predominant TIL population, the relationship between quantitative and qualitative differences in T cell subpopulations and patient prognosis remains unknown. We performed single-cell RNA sequencing (scRNA-seq) of 6,311 T cells isolated from human BCs and show that significant heterogeneity exists in the infiltrating T cell population. We demonstrate that BCs with a high number of TILs contained CD8 T cells with features of tissue-resident memory T (T) cell differentiation and that these CD8 T cells expressed high levels of immune checkpoint molecules and effector proteins. A CD8 T gene signature developed from the scRNA-seq data was significantly associated with improved patient survival in early-stage triple-negative breast cancer (TNBC) and provided better prognostication than CD8 expression alone. Our data suggest that CD8 T cells contribute to BC immunosurveillance and are the key targets of modulation by immune checkpoint inhibition. Further understanding of the development, maintenance and regulation of T cells will be crucial for successful immunotherapeutic development in BC.

Single-cell RNA sequencing (scRNA-seq) data is noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions, and divergent species to denoise target new datasets.In scRNA-seq studies, technical noise blurs precise distinctions between cell states, and genes with low expression cannot be accurately quantified. Existing methods [1][2][3][4][5][6] to denoise scRNA-seq data often underperform when sequencing depth is low or when the cell type of interest is rare, and also ignore datasets in public domain, which may contain relevant information to aid denoising. Ensuing the mouse cell atlases 7,8 , we will soon have detailed atlases for each anatomic organ in the human body 9 . Publicly available scRNA-seq datasets contain information about cell types and gene signatures that is relevant to newly generated data. Yet, it is unclear how to borrow information across platforms, subjects and tissues. Moreover, such transfer learning must not introduce bias or force the new data to lose its distinctive features.Here, we describe a denoising method, Single-cell Analysis via Expression Recovery harnessing eXternal data (SAVER-X), which couples a Bayesian hierarchical model to a pretrainable deep autoencoder 10 . Although neural networks have formed the basis of other *

While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Protein prediction with deep neural network (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources.

Motivation Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. Results We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. Availability and implementation The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. Supplementary information Supplementary data are available at Bioinformatics online.