Yuanning Liu

CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data.

Motivation: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore, efficient computational methods are required to address these issues. Results: We introduce CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors), a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B-allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples. Availability and implementation: The CLImAT software package can be freely downloaded at http://bioinformatics.ustc.edu.cn/CLImAT/. Contact: aoli@ustc.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

A Kinect based gesture recognition algorithm using GMM and HMM

Gesture recognition is a quite promising field in robotics and many Human-Computer Interaction (HCI) related areas. This research uses Microsoft® Kinect to capture the 3D position data of joints, and uses Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) to model full-body gestures. We propose a gesture recognition algorithm to segment gestures from real-time data flow, and finally achieved to recognize predefined full-body gestures in real-time. This proposed method gives a high recognition rate of 94.36%, indicating the capability of the new method.

A novel method for user-defined human posture recognition using Kinect

Human posture recognition is very critical in human computer interaction studies. With the release of Microsoft Kinect sensor, there has been an increasing interest in using Kinect for vision based human posture recognition as users skeleton information can be precisely inferred from the depth images generated by Kinect. In this paper we proposed a novel human posture recognition method using Microsoft Kinect sensor, which can automatically identify any user-defined postures. Skeleton information inferred from depth image of users posture was utilized to generate 9 features representing specific body parts such as forearm, thigh, etc. These features are fed into SVM to generate posture-learning models that are then used to identify pre-defined postures. Totally 22 different postures including body, arm, leg postures were collected and PCA analysis demonstrated they were in general well separated in the feature space. Further performance evaluation using 10-fold cross-validation showed a final overall accuracy of 99.14% was successfully achieved in the test including all postures, indicating the outstanding capability of this proposed methods.

Genome-Wide Identification of Somatic Aberrations from Paired Normal-Tumor Samples

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.

View independent human posture identification using Kinect

As an important part of human computer interaction (HCI) system, posture identification has been extensively studied over last years. Recently, Microsoft Kinect Sensor has become a hot spot for posture identification because it is efficient in acquiring body joint location information. In this study, based on Kinect, we proposed a framework for view independent human posture identification. In this framework, a viewpoint rotation transformation was performed on original skeleton location data and then total 9 features were extracted for building a SVM classifier. About 4200 samples including five postures taken from different body orientations were collected to construct a dataset for performance evaluation. The results of PCA analysis showed that the transformation was efficient in distinguishing different postures. Further analysis demonstrated that this method achieved a superior performance of 98.0% when the orientation angle was between -60° and 60°. These results show that this view independent framework is powerful and efficient in viewpoint invariant posture identification.

TAFFYS: An Integrated Tool for Comprehensive Analysis of Genomic Aberrations in Tumor Samples

Background Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification. Results We propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate. Conclusions TAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.

Identification of genomic aberrations in cancer subclones from heterogeneous tumor samples

Tumor samples are usually heterogeneous, containing admixture of more than one kind of tumor subclones. Studies of genomic aberrations from heterogeneous tumor data are hindered by the mixed signal of tumor subclone cells. Most of the existing algorithms cannot distinguish contributions of different subclones from the measured single nucleotide polymorphism (SNP) array signals, which may cause erroneous estimation of genomic aberrations. Here, we have introduced a computational method, Cancer Heterogeneity Analysis from SNP-array Experiments (CHASE), to automatically detect subclone proportions and genomic aberrations from heterogeneous tumor samples. Our method is based on HMM, and incorporates EM algorithm to build a statistical model for modeling mixed signal of multiple tumor subclones. We tested the proposed approach on simulated datasets and two real datasets, and the results show that the proposed method can efficiently estimate tumor subclone proportions and recovery the genomic aberrations.

Comprehensive study of tumour single nucleotide polymorphism array data reveals significant driver aberrations and disrupted signalling pathways in human hepatocellular cancer.

The authors describe an integrated method for analysing cancer driver aberrations and disrupted pathways by using tumour single nucleotide polymorphism (SNP) arrays. The authors new method adopts a novel statistical model to explicitly quantify the SNP signals, and therefore infers the genomic aberrations, including copy number alteration and loss of heterozygosity. Examination on the dilution series dataset shows that this method can correctly identify the genomic aberrations even with the existence of severe normal cell contamination in tumour sample. Furthermore, with the results of the aberration identification obtained from multiple tumour samples, a permutation-based approach is proposed for identifying the statistically significant driver aberrations, which are further incorporated with the known signalling pathways for pathway enrichment analysis. By applying the approach to 286 hepatocellular tumour samples, they successfully uncover numerous driver aberration regions across the cancer genome, for example, chromosomes 4p and 5q, which harbour many known hepatocellular cancer related genes such as alpha-fetoprotein (AFP) and ectodermal-neural cortex (ENC1). In addition, they identify nine disrupted pathways that are highly enriched by the driver aberrations, including the systemic lupus erythematosus pathway, the vascular endothelial growth factor (VEGF) signalling pathway and so on. These results support the feasibility and the utility of the proposed method on the characterisation of the cancer genome and the downstream analysis of the driver aberrations and the disrupted signalling pathways.

A novel HMM for analyzing chromosomal aberrations in heterogeneous tumor samples

Comprehensive detection and identification of copy number and LOH of chromosomal aberration is required to provide an accurate therapy of human cancer. As a cost-saving and high-throughput tool, SNP arrays facilitate analysis of chromosomal aberration throughout the whole genome. The performance of previous approaches has been limited to several critical issues such as normal cell contamination, aneuploidy and tumor heterogeneity. For these reasons we present a Hidden Markov Model (HMM) based approach called TH-HMM (Tumor Heterogeneity HMM), for simultaneous detection of copy number and LOH in heterogeneous tumor samples using data from Illumina SNP arrays. Through adopting an efficient EM algorithm, our method can correctly detect chromosomal aberration events in tumor subclones. Evaluation on simulated data series indicated that TH-HMM could accurately estimate both normal cell and subclone proportions, and finally recovery the aberration profiles for each clones.

A Novel CNA/LOH Detection Algorithm Using Normal-Tumor SNP-Array Data

Recently single nucleotide polymorphism (SNP) genotyping arrays attracts lots of attentions, which can provide high resolution profiling chromosomal rearrangements. It facilitates whole genome detection of two common aberrations: copy number alteration (CNA) and loss of heterozygosity (LOH), which are frequently found in cancer cells. At present, many computational approaches have been introduced for this purpose, however, most of them fail to incorporate the intrinsic genetic relationship between tumor and paired normal SNP-array data, which may greatly improve the performance in identifying CNA and LOH in cancer genome. To address this issue, we proposed a novel algorithm to handle paired SNP-array data from both tumor and paired normal samples, which can make best use of the genotype information to assist the detection. This algorithm employs the statistical framework of HMM and EM method to precisely model the relationship between normal and tumor SNP-array data. Results on public datasets show that our method outperforms all other investigated algorithms with precise parameter estimation and sensitive aberration identification.