Top 10 pathways of selected miRNAs from the CCLE data using mirPath 3.0^#.

Top 10 pathways of selected miRNAs from the CCLE data using mirPath 3.0#.</p

AIME results using TCGA miRNA and gene expression data, adjusting for confounders including age and ER status.

Points are colored based on PAM50 (Prosigna Breast Cancer Prognostic Gene Signature Assay) subtypes. (TIF)</p

jSVD results using TCGA miRNA and gene expression data.

Points are colored based on PAM50 (Prosigna Breast Cancer Prognostic Gene Signature Assay) subtypes. (TIF)</p

AIME results using TCGA miRNA and gene expression data, with and without adjusting for confounders including age, T1 (tumor size) status, and estrogen receptor (ER) status.

Points are colored based on ER status. Upper-right sub-plots: without adjustment for confounders; lower-left sub-plots: with adjustment for confounders. (TIF)</p

Simulation results on the power and size of the test. <b>In all simulations, the true negative class mean is set at zero for both studies, and the standard deviations of the true negative class and the true positive class are one for both studies.</b>

<p> Miu.1 is the true positive class mean of study 1; miu.2 is the true positive class mean of study 2. All curves are based on 100 simulations. Horizontal line: the alpha level 0.05 for the purpose of examining the size of the test. (a) The simple scenario where both studies have the same sample size (4000) and class ratio. (b) The more complex case where study 1 has 2000 samples, study 2 has 4000 samples, and the class ratios are different.</p

A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data

<div><p>Dynamic correlations are pervasive in high-throughput data. Large numbers of gene pairs can change their correlation patterns in response to observed/unobserved changes in physiological states. Finding changes in correlation patterns can reveal important regulatory mechanisms. Currently there is no method that can effectively detect global dynamic correlation patterns in a dataset. Given the challenging nature of the problem, the currently available methods use genes as surrogate measurements of physiological states, which cannot faithfully represent true underlying biological signals. In this study we develop a new method that directly identifies strong latent dynamic correlation signals from the data matrix, named DCA: Dynamic Correlation Analysis. At the center of the method is a new metric for the identification of pairs of variables that are highly likely to be dynamically correlated, without knowing the underlying physiological states that govern the dynamic correlation. We validate the performance of the method with extensive simulations. We applied the method to three real datasets: a single cell RNA-seq dataset, a bulk RNA-seq dataset, and a microarray gene expression dataset. In all three datasets, the method reveals novel latent factors with clear biological meaning, bringing new insights into the data.</p></div

CCA results using TCGA miRNA and gene expression data.

Points are colored based on ER status. (TIF)</p

The top 5 microRNAs (fdr≤0.01) and their major associated genes (fdr≤0.001), after adjusting for age, T1 (tumor size) status, and estrogen receptor (ER) status.

Red nodes: microRNAs; blue nodes/edges: validated by multiMir.</p

jSVD results of CCLE miRNA and gene expression data.

(TIF)</p

Venn Diagram of the top genes and functional analysis of the top genes.

(a) Venn diagram of the top 270 genes selected by AIME_adjusted (fdr≤0.1), and the top 270 genes from AIME_unadjusted, MOFA2 and iCluster2. (b) The overrepresented pathways (FDR≤0.1) with 100~500 genes. Group 1: pathways selected by MOFA2, AIME_adjusted and AIME_unadjusted; group 2: pathways selected by AIME_adjusted and AIME_unadjusted; group 3: pathways selected by AIME_adjusted and MOFA2.</p