ArticleBiostatistics (Oxford, England)2011
A fused lasso latent feature model for analyzing multi-sample aCGH data.
Article in Biostatistics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- HMFGraph: Novel Bayesian approach for recovering biological networks.PLoS computational biology · 2025Article
- Where Do We Stand in Regularization for Life Science Studies?Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data.Bioinformatics (Oxford, England) · 2022Article
- Statistical Considerations on NGS Data for Inferring Copy Number Variations.Methods in molecular biology (Clifton, N.J.) · 2021Review
- A Density Peak-Based Method to Detect Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2020Article
- Genomic region detection via Spatial Convex Clustering.PloS one · 2018Article
- Robust group fused lasso for multisample copy number variation detection under uncertainty.IET systems biology · 2016Article
- Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations.BMC bioinformatics · 2015Article
- Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm.BMC medical genomics · 2015Article
- Principles and methods of integrative genomic analyses in cancer.Nature reviews. Cancer · 2014Review
- Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data.Microarrays (Basel, Switzerland) · 2013Review
- Multiple samples aCGH analysis for rare CNVs detection.Journal of clinical bioinformatics · 2013Article
- Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping.Bioinformatics (Oxford, England) · 2013Article
- Breakpoint analysis of transcriptional and genomic profiles uncovers novel gene fusions spanning multiple human cancer types.PLoS genetics · 2013Article
- Reconstructing DNA copy number by joint segmentation of multiple sequences.BMC bioinformatics · 2012Article
- Genetic association studies of copy-number variation: should assignment of copy number states precede testing?PloS one · 2012Article
- Multisample aCGH data analysis via total variation and spectral regularization.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Novel multisample scheme for inferring phylogenetic markers from whole genome tumor profiles.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Array-based comparative genomic hybridization (aCGH) enables the measurement of DNA copy number across thousands of locations in a genome. The main goals of analyzing aCGH data are to identify the regions of copy number variation (CNV) and to quantify the amount of CNV. Although there are many methods for analyzing single-sample aCGH data, the analysis of multi-sample aCGH data is a relatively new area of research. Further, many of the current approaches for analyzing multi-sample aCGH data do not appropriately utilize the additional information present in the multiple samples. We propose a procedure called the Fused Lasso Latent Feature Model (FLLat) that provides a statistical framework for modeling multi-sample aCGH data and identifying regions of CNV. The procedure involves modeling each sample of aCGH data as a weighted sum of a fixed number of features. Regions of CNV are then identified through an application of the fused lasso penalty to each feature. Some simulation analyses show that FLLat outperforms single-sample methods when the simulated samples share common information. We also propose a method for estimating the false discovery rate. An analysis of an aCGH data set obtained from human breast tumors, focusing on chromosomes 8 and 17, shows that FLLat and Significance Testing of Aberrant Copy number (an alternative, existing approach) identify similar regions of CNV that are consistent with previous findings. However, through the estimated features and their corresponding weights, FLLat is further able to discern specific relationships between the samples, for example, identifying 3 distinct groups of samples based on their patterns of CNV for chromosome 17.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.