FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples

Abstract Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers. The rich literature of pioneering studies on tumor heterogeneity analysis spurred a recent community-wide benchmark study that compares diverse modeling algorithms. Here we present FastClone, a top-performing algorithm in accuracy in this benchmark. FastClone improves over existing methods by allowing the deconvolution of subclones that have independent copy number variation events within the same chromosome regions. We characterize the behavior of FastClone in identifying subclones using stage III colon cancer primary tumor samples as well as simulated data. It achieves approximately 100-fold acceleration in computation for both simulated and patient data. The efficacy of FastClone will allow its application to large-scale data and clinical data, and facilitate personalized medicine in cancers.

Download Full-text

An Evaluation of Supervised Dimensionality Reduction For Large Scale Data

Journal of Machine and Computing ◽

10.53759/7669/jmc202202003 ◽

2022 ◽

pp. 17-25

Author(s):

Nancy Jan Sliper

Keyword(s):

Dimensionality Reduction ◽

Large Scale ◽

Simulated Data ◽

Principal Component ◽

Low Rank ◽

Learning Tools ◽

Large Scale Data ◽

Reduction Methods ◽

Low Dimensional ◽

Scale Data

Experimenters today frequently quantify millions or even billions of characteristics (measurements) each sample to address critical biological issues, in the hopes that machine learning tools would be able to make correct data-driven judgments. An efficient analysis requires a low-dimensional representation that preserves the differentiating features in data whose size and complexity are orders of magnitude apart (e.g., if a certain ailment is present in the person's body). While there are several systems that can handle millions of variables and yet have strong empirical and conceptual guarantees, there are few that can be clearly understood. This research presents an evaluation of supervised dimensionality reduction for large scale data. We provide a methodology for expanding Principal Component Analysis (PCA) by including category moment estimations in low-dimensional projections. Linear Optimum Low-Rank (LOLR) projection, the cheapest variant, includes the class-conditional means. We show that LOLR projections and its extensions enhance representations of data for future classifications while retaining computing flexibility and reliability using both experimental and simulated data benchmark. When it comes to accuracy, LOLR prediction outperforms other modular linear dimension reduction methods that require much longer computation times on conventional computers. LOLR uses more than 150 million attributes in brain image processing datasets, and many genome sequencing datasets have more than half a million attributes.

Download Full-text

A Local Algorithm for Product Return Prediction in E-Commerce

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/517 ◽

2018 ◽

Author(s):

Yada Zhu ◽

Jianbo Li ◽

Jingrui He ◽

Brian L. Quanz ◽

Ajay A. Deshpande

Keyword(s):

Large Scale ◽

Local Algorithm ◽

Data Sets ◽

Product Returns ◽

Product Return ◽

Large Scale Data ◽

Rich Information ◽

The Rich ◽

Scale Data ◽

Large Scale Data Sets

With the rapid growth of e-tail, the cost to handle returned online orders also increases significantly and has become a major challenge in the e-commerce industry. Accurate prediction of product returns allows e-tailers to prevent problematic transactions in advance. However, the limited existing work for modeling customer online shopping behaviors and predicting their return actions fail to integrate the rich information in the product purchase and return history (e.g., return history, purchase-no-return behavior, and customer/product similarity). Furthermore, the large-scale data sets involved in this problem, typically consisting of millions of customers and tens of thousands of products, also render existing methods inefficient and ineffective at predicting the product returns. To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns. The proposed graph consists of both customer nodes and product nodes, undirected edges reflecting customer return history and customer/product similarity based on their attributes, as well as directed edges discriminating purchase-no-return and no-purchase actions. Based on this representation, we study a random-walk-based local algorithm for predicting product return propensity for each customer, whose computational complexity depends only on the size of the output cluster rather than the entire graph. Such a property makes the proposed local algorithm particularly suitable for processing the large-scale data sets to predict product returns. To test the performance of the proposed techniques, we evaluate the graph model and algorithm on multiple e-commerce data sets, showing improved performance over state-of-the-art methods.

Download Full-text

MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data

Computational Intelligence and Neuroscience ◽

10.1155/2015/217216 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Jingjing Wang ◽

Chen Lin

Keyword(s):

Large Scale ◽

False Negative ◽

Experimental Studies ◽

Simulated Data ◽

False Positives ◽

Locality Sensitive Hashing ◽

False Negatives ◽

Large Scale Data ◽

Similarity Joins ◽

Scale Data

Locality Sensitive Hashing (LSH) has been proposed as an efficient technique for similarity joins for high dimensional data. The efficiency and approximation rate of LSH depend on the number of generated false positive instances and false negative instances. In many domains, reducing the number of false positives is crucial. Furthermore, in some application scenarios, balancing false positives and false negatives is favored. To address these problems, in this paper we propose Personalized Locality Sensitive Hashing (PLSH), where a new banding scheme is embedded to tailor the number of false positives, false negatives, and the sum of both. PLSH is implemented in parallel using MapReduce framework to deal with similarity joins on large scale data. Experimental studies on real and simulated data verify the efficiency and effectiveness of our proposed PLSH technique, compared with state-of-the-art methods.

Download Full-text