Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species.

■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

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From Data Mining to Knowledge Discovery in Databases

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Principles of data mining

Knowledge discovery in databases presents many interesting challenges within the context of providing computer tools for exploring large data archives. Electronic data repositories are growing quickly and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detecting patterns in such data streams or time series is an important knowledge discovery task. This paper describes some preliminary experiments with a dynamic programming approach to the problem. The pattern detection algorithm is based on the dynamic time warping technique used in the speech recognition field.

Using dynamic time warping to find patterns in time series

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

A Comprehensive Survey on Transfer Learning

Temporal Databases: Theory, Design, and Implementation

Finding patterns in time series: a dynamic programming approach

This glossary contains concepts specific to temporal databases that are well-defined, well understood, and widely used. In addition to defining and naming the concepts, the glossary also explains the decisions made. It lists competing alternatives and discusses the pros and cons of these. It also includes evaluation criteria for the naming of concepts.This paper is a structured presentation of the results of e-mail discussions initiated during the preparation of the first book on temporal databases, Temporal Databases: Theory, Design, and Implementation, published by Benjamin Cummings, to appear January 1993. Independently of the book, an initiative aimed at designing a consensus Temporal SQL is under way. The paper is a contribution towards establishing common terminology, an initial subtask of this initiative.

A glossary of temporal database concepts

In search of the appropriate semantics -for the inclusion of structures and operations that ~111 meet the needs of a wide class of users interested in a database system supporting temporal views of their data, the paper includes both a discussion of many problems that must be addressed, and a proposal for an extended relational algebra The informal dlscusslon of Issues motivates the proposal to time-stamp the attributes of relations, rather than the tuples The proposed algebra incorporates this view with a general treatment of non-first-normal-form relations Note Parts I and II were written and submitted independently by their respective authors to the 1985 SIGMOD Conference, they have been combined at the invitation of the 1985 SIGMOD Program Committee The names of the authors are llsted In alphabetlcal order

James Clifford

Papers

Using dynamic time warping to find patterns in time series

Temporal Databases: Theory, Design, and Implementation

Finding patterns in time series: a dynamic programming approach

A glossary of temporal database concepts

On an algebra for historical relational databases: two views