FaStaNMF: a Fast and Stable Non-negative Matrix Factorization for Gene Expression.

Gene expression analysis of samples with mixed cell types only provides limited insight to the characteristics of specific tissues. In silico deconvolution can be applied to extract cell type specific expression, thus avoiding prohibitively expensive techniques such as cell sorting or single-cell sequencing. Non-negative matrix factorization (NMF) is a deconvolution method shown to be useful for gene expression data, in part due to its constraint of non-negativity. Unlike other methods, NMF provides the capability to deconvolve without prior knowledge of the components of the model. However, NMF is not guaranteed to provide a globally unique solution. In this work, we present FaStaNMF, a method that balances achieving global stability of the NMF results, which is essential for inter-experiment and inter-lab reproducibility, with accuracy and speed. Results: FaStaNMF was applied to four datasets with known ground truth, created based on publicly available data or by using our simulation infrastructure, RNAGinesis. We assessed FaStaNMF on three criteria - speed, accuracy, and stability, and it favorably compared to the standard approach of achieving reproduceable results with NMF. We expect that FaStaNMF can be applied successfully to a wide array of biological data, such as different tumor/immune and other disease microenvironments.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

Click Chemistry Approaches to Expand the Repertoire of PEG-based Fluorinated Surfactants for Droplet Microfluidics.

We report the novel and simplified synthesis of fluorinated surfactants for droplet microfluidics. The range of applications of droplet microfluidics has greatly expanded during the last decade thanks to its ability to manipulate and process tiny amount of sample and reagents at high throughput in independent reactors. A critical component of the technology is the formulation of the immiscible oil phase that contains surfactants to stabilize droplets. The success of droplet microfluidics relies mostly on a single fluorinated formulation that uses a PFPE-PEG tri-block surfactant. The synthesis of this surfactant is laborious and requires skills in synthetic chemistry preventing the wider community to explore the synthesis of alternate surfactants. We sought to provide a simplified synthesis for novel PFPE-PEG surfactants based on click chemistry approaches such as copper-catalyzed azide-alkyne cycloaddition (CuAAC) and UV-activated thiol-yne reactions. Our strategy is based on converting a moisture sensitive intermediate typically used in the synthesis of the tri-block PFPE-PEG surfactant into a stable and click ready molecule. We successfully combined that fluorinated tail with differently functionalized PEG and glycerol ethoxylate molecules to generate surfactants with diverse structures via CuACC and thiol-yne reactions. We report the characterization, biocompatibility and ability to stabilize emulsions of those surfactants, as well as the unique advantages and challenges of the strategy.

Correction: Click chemistry approaches to expand the repertoire of PEG-based fluorinated surfactants for droplet microfluidics.

[This corrects the article DOI: 10.1039/C8RA01254G.].