Detecting Trivariate Associations in High-Dimensional Datasets.

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.

Detecting Unbiased Associations in Large Data Sets.

Maximal information coefficient (MIC) explores the associations between pairwise variables in complex relationships. It approaches the correlation by optimized partition on the axis. However, when the relationships meet special noise, MIC may overestimate the correlated value, which leads to the misidentification of the relationship without noiseless. In this article, a novel method of weighted information coefficient mean (WICM) is proposed to detect unbiased associations in large data sets. First, we mathematically analyze the cause of giving an abnormal correlation value to a noisy relationship. Then, the WICM is presented in two core steps. One is to detect the potential overestimation from the relationships with high value, and the other is to rectify the overestimation by calculating information coefficient mean instead of just selecting the maximum element in the characteristic matrix. Finally, experiments in functional relationships and real-world data relationships show that the overestimation can be solved by WICM with both feasibility and effectiveness.

PD-1hi Identifies a Novel Regulatory B-cell Population in Human Hepatoma That Promotes Disease Progression.

UNLABELLED: B cells often constitute abundant cellular components in human tumors. Regulatory B cells that are functionally defined by their ability to produce IL10 downregulate inflammation and control T-cell immunity. Here, we identified a protumorigenic subset of B cells that constitutively expressed higher levels of programmed cell death-1 (PD-1) and constituted ∼10% of all B cells in advanced-stage hepatocellular carcinoma (HCC). These PD-1(hi) B cells exhibited a unique CD5(hi)CD24(-/+)CD27(hi/+)CD38(dim) phenotype different from the phenotype of conventional CD24(hi)CD38(hi) peripheral regulatory B cells. TLR4-mediated BCL6 upregulation was crucial for PD-1(hi) B-cell induction by HCC environmental factors, and that effect was abolished by IL4-elicited STAT6 phosphorylation. Importantly, upon encountering PD-L1(+) cells or undergoing PD-1 triggering, PD-1(hi) B cells acquired regulatory functions that suppressed tumor-specific T-cell immunity and promoted cancer growth via IL10 signals. Our findings provide significant new insights for human cancer immunosuppression and anticancer therapies regarding PD-1/PD-L1. SIGNIFICANCE: We identify a novel protumorigenic PD-1(hi) B-cell subset in human HCC that exhibits a phenotype distinct from that of peripheral regulatory B cells. TLR4-mediated BCL6 upregulation is critical for induction of PD-1(hi) B cells, which operate via IL10-dependent pathways upon interacting with PD-L1 to cause T-cell dysfunction and foster disease progression. Cancer Discov; 6(5); 546-59. ©2016 AACR.See related commentary by Ren et al., p. 477This article is highlighted in the In This Issue feature, p. 461.