Abstract-Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

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Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.gene expression | gene transcription | RNA processing | Gaussian process inference | RNA splicing I nduction of transcription through extracellular signaling can yield rapid changes in gene expression for many genes. Establishing the timing of events during this process is important for understanding the rate-limiting mechanisms regulating the response and vital for inferring causality of regulatory events. Several processes influence the patterns of mRNA abundance observed in the cell, including the kinetics of transcriptional initiation, elongation, splicing, and mRNA degradation. It was recently demonstrated that significant delays attributable to the kinetics of splicing can be an important factor in a focused study of genes induced by tumor necrosis factor (TNF-α) (1). Delayed transcription can play an important functional role in the cell, for example, inducing oscillations within negative feedback loops (2) or facilitating "justin-time" transcriptional programs with optimal efficiency (3). It is therefore important to identify such delays and to better understand how they are regulated. In this study, we combine RNA polymerase (pol-II) ChIP-Seq data with RNA-Seq data to study transcription kinetics of estrogen receptor (ER) signaling in breast cancer cells. Using an unbiased genome-wide modeling approach, we find evidence for large delays in mRNA production in 11% of the genes with a quantifiable signal in our data. A statistical analysis of gene...