Trustworthy dimension reduction for visualization different data sets

EMBEDR: Distinguishing signal from noise in single-cell omics data

Single-cell “omics”-based measurements are often high dimensional so that dimensionality reduction (DR) algorithms are necessary for data visualization and analysis. The lack of methods for separating signal from noise in DR outputs has limited their utility in generating data-driven discoveries in single-cell data. In this work we present EMBEDR, which assesses the output of any DR algorithm to distinguish evidence of structure from algorithm-induced noise in DR outputs. We apply EMBEDR to DR-generated representations of single-cell omics data of several modalities to show where they visually show real—not spurious—structure. EMBEDR generates a “p” value for each sample, allowing for direct comparisons of DR algorithms and facilitating optimization of algorithm hyperparameters. We show that the scale of a sample’s neighborhood can thus be determined and used to generate a novel “cell-wise optimal” embedding. EMBEDR is available as a Python package for immediate use.

An overview of the benefits and difficulties of dimensionality reduction

A novel algorithm for quantifying and identifying quality within embeddings of data

Quality can be optimized to find data scales and set algorithm parameters

A cell-wise view of quality generates robust and interpretable representations of data

Modern technologies have enabled biologists to construct enormous datasets containing millions of observations of thousands of measurements. These datasets push the limits of traditional analysis techniques, leaving doubts about the quality and fidelity of these methods. In this work, we present a sort of meta-algorithm, called EMBEDR, which seeks to evaluate when a certain class of methods, known as dimensionality reduction methods, are generating high-quality representations of data. We show that EMBEDR allows for visualizations of even large datasets to be interpreted with confidence. Furthermore, we show how asking about the method quality itself can lead to improved analyses of data. These improved analyses may directly impact our understanding of cellular biology, including how cells behave, grow, and respond to stimuli.

Emotion computing using Word Mover's Distance features based on Ren_CECps

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover’s Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.