GranatumX: A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis.

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.

DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson's correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at https://github.com/lanagarmire/DeepImpute .

Maternal cardiovascular-related single nucleotide polymorphisms, genes, and pathways associated with early-onset preeclampsia.

INTRODUCTION: Preeclampsia is a medical condition complicated with hypertension and proteinuria during pregnancy. While preeclampsia affects approximately 5% of pregnancies, it remains without a cure. In addition, women who had preeclampsia during pregnancy have been reported to have an increased risk for cardiovascular disease later in life. However, the disease etiology and molecular mechanisms remain poorly understood. The paucity in the literature on preeclampsia associated maternal cardiovascular risk in different ethnic populations also present a need for more research. Therefore, the objective of this study was to identify cardiovascular/metabolic single nucleotide polymorphisms (SNPs), genes, and regulatory pathways associated with early-onset preeclampsia. MATERIALS AND METHODS: We compared maternal DNAs from 31 women with early-onset preeclampsia with those from a control group of 29 women without preeclampsia who delivered full-term normal birthweight infants. Women with multiple gestations and/or known medical disorders associated with preeclampsia (pregestational diabetes, chronic hypertension, renal disease, hyperthyroidism, and lupus) were excluded. The MetaboChip genotyping array with approximately 197,000 SNPs associated with metabolic and cardiovascular traits was used. Single nucleotide polymorphism analysis was performed using the SNPAssoc program in R. The Truncated Product Method was used to identify significantly associated genes. Ingenuity Pathway Analysis and Ingenuity Causal Network Analysis were used to identify significantly associated disease processes and regulatory gene networks respectively. RESULTS: The early-onset preeclampsia group included 45% Filipino, 26% White, 16% other Asian, and 13% Native Hawaiian and other Pacific Islanders, which did not differ from the control group. There were no SNPs associated with early-onset preeclampsia after correction for multiple comparisons. However, through gene-based tests, 68 genes and 23 cardiovascular disease-related processes were found to be significantly associated. Associated gene regulatory networks involved cellular movement, cardiovascular disease, and inflammatory disease. CONCLUSIONS: Multiple cardiovascular genes and diseases demonstrate associations with early-onset preeclampsia. This unfolds new areas of research regarding the genetic determinants of early-onset preeclampsia and their relation to future cardiovascular disease.

Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data.

Lilikoi (the Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway-based classification modeling using metabolomics data. Four basic modules are presented as the backbone of the package: feature mapping module, which standardizes the metabolite names provided by users and maps them to pathways; dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores; feature selection module, which helps to select the significant pathway features related to the disease phenotypes; and classification and prediction module, which offers various machine learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi and CRAN: https://cran.r-project.org/web/packages/lilikoi/index.html.