arXiv subjects

Niya Wang

Publications and source records attributed to Niya Wang.

6 recordsLinked to original sources

arXiv2015

Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources

Blind Source Separation (BSS) has proven to be a powerful tool for the analysis of composite patterns in engineering and science. We introduce Convex Analysis of Mixtures (CAM) for separating non-negative well-grounded sources, which learns the mixing matrix by identifying the lateral edges of the convex data scatter plot. We prove a sufficient and necessary condition for identifying the mixing matrix through edge detection, which also serves as the foundation for CAM to be applied not only to the exact-determined and over-determined cases, but also to the under-determined case. We show the optimality of the edge detection strategy, even for cases where source well-groundedness is not strictly satisfied. The CAM algorithm integrates plug-in noise filtering using sector-based clustering, an efficient geometric convex analysis scheme, and stability-based model order selection. We demonstrate the principle of CAM on simulated data and numerically mixed natural images. The superior performance of CAM against a panel of benchmark BSS techniques is demonstrated on numerically mixed gene expression data. We then apply CAM to dissect dynamic contrast-enhanced magnetic resonance imaging data taken from breast tumors and time-course microarray gene expression data derived from in-vivo muscle regeneration in mice, both producing biologically plausible decomposition results.

stat.ML↗

arXiv2015

BACOM2: a Java tool for detecting normal cell contamination of copy number in heterogeneous tumor

We develop a cross-platform open-source Java application (BACOM2) with graphic user interface (GUI), and users also can use a XML file to set the parameters of algorithm model, file paths and the dataset of paired samples. BACOM2 implements the new entire pipeline of copy number change analysis for heterogeneous cancer tissues, including extraction of raw copy number signals from CEL files of paired samples, attenuation correction, identification of balanced AB-genotype loci, copy number detection and segmentation, global baseline calculation and absolute normalization, differentiation of deletion types, estimation of the normal tissue fraction and correction of normal tissue contamination. BACOM2 focuses on the common tools for data preparation and absolute normalization for copy number analysis of heterogeneous cancer tissues. The software provides an additional choice for scientists who require a user-friendly, high-speed processing, cross-platform computing environment for large copy number data analysis.

q-bio.GN↗

arXiv2014

A feasible roadmap to identifying significant intercellular genomic heterogeneity in deep sequencing data

Intercellular heterogeneity serves as both a confounding factor in studying individual clones and an information source in characterizing any heterogeneous tissues, such as blood, tumor systems. Due to inevitable sequencing errors and other sample preparation artifacts such as PCR errors, systematic efforts to characterize intercellular genomic heterogeneity must effectively distinguish genuine clonal sequences from fake derivatives. We developed a novel approach (SIGH) for identifying significant genuine clonal sequences directly from mixed sequencing reads that can improve genomic analyses in many biological contexts. This method offers several attractive features: (1) it automatically estimates the error rate from raw sequence reads and identifies genuine clonal sequences; (2) it is robust to the large variety of error rate due to the various experimental conditions; (3) it is supported by a well grounded statistical framework that exploits probabilistic characteristics of sequencing errors; (4) its unbiased strategy allows detecting rare clone(s) despite that clone relative abundance; and (5) it estimates constituent proportions in each sample. Extensive realistic simulation studies show that our method can reliably estimate the error rates and faithfully distinguish the genuine clones from fake derivatives, paving the way for follow up analysis that is otherwise ruined by the often dominant fake clones.

q-bio.GN↗

arXiv2014

Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity

Intratumor heterogeneity is often manifested by vascular compartments with distinct pharmacokinetics that cannot be resolved directly by in vivo dynamic imaging. We developed tissue-specific compartment modeling (TSCM), an unsupervised computational method of deconvolving dynamic imaging series from heterogeneous tumors that can improve vascular phenotyping in many biological contexts. Applying TSCM to dynamic contrast-enhanced MRI of breast cancers revealed characteristic intratumor vascular heterogeneity and therapeutic responses that were otherwise undetectable.

q-bio.QM↗

arXiv2014

BACOM 2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor

BACOM is a statistically principled and unsupervised method that detects copy number deletion types (homozygous versus heterozygous), estimates normal cell fraction, and recovers cancer specific copy number profiles, using allele specific copy number signals. In a subsequent analysis of TCGA ovarian cancer dataset, the average normal cell fraction estimated by BACOM was found higher than expected. In this letter, we first discuss the advantages of the BACOM in relation to alternative approaches. Then, we show that this elevated estimate of normal cell fraction is the combined result of inaccurate signal modeling and normalization. Lastly, we describe an allele specific signal modeling and normalization scheme that can enhance BACOM applications in many biological contexts. An open source MATLAB program was developed to implement our extended method and it is publically available.

q-bio.GN↗

arXiv2013

A feasible roadmap for unsupervised deconvolution of two-source mixed gene expressions

Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we ask whether it is possible to deconvolute two-source mixed expressions (estimating both proportions and cell-specific profiles) from two or more heterogeneous samples without requiring any prior knowledge. Supported by a well-grounded mathematical framework, we argue that both constituent proportions and cell-specific expressions can be estimated in a completely unsupervised mode when cell-specific marker genes exist, which do not have to be known a priori, for each of constituent cell types. We demonstrate the performance of unsupervised deconvolution on both simulation and real gene expression data, together with perspective discussions.

stat.ML↗