Extraction and comparison of gene expression patterns from 2D RNA <i>in situ</i> hybridization images

Mace, Daniel; Varnado, Nicole; Zhang, Weiping; Frise, Erwin; Ohler, Uwe

doi:10.1093/bioinformatics/btp658

Cited by 28 publications

(19 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More sophisticated methods for contour extraction of Drosophila embryonic images were proposed [8], [7], [6] concurrently with our study. Frise et al [8] developed Peng's method, and proposed a heuristic algorithm to separate the embryo of interest from multiple touching embryos.…”

Section: Introductionmentioning

confidence: 72%

“…1a, indicate expression patterns of genes. Given two standardized images of embryos (of pixel-to-pixel correspondence) at the same developmental stage, the interaction strength of two genes can be quantified by computing the similarity of expression patterns [3], [4], [5], [6], [7], [8], e.g., the ratio of overlapping expression regions of the images. Compared with in situ hybridization, the widely used microarray technique reveals very limited spatial pattern information.…”

Section: Introductionmentioning

confidence: 99%

“…In our study, we charaterize the embryo of interest as an embryo dominating the image region, which is more general than Frise's assumption. Mace et al [7] proposed a signed distance map based extraction method. Given an image I and a contour, the signed distance map has the same dimension as I, and the value of a point in the map is defined as the distance between the point and the contour with a signpositive sign is assigned if the point is outside the contour, and negative otherwise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contour Extraction of Drosophila Embryos

Kambhamettu

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including 1) the size, orientation, shape, and appearance of an embryo of interest; 2) the neighboring context of an embryo of interest (such as nontouching and touching neighboring embryos); and 3) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image. The proposed framework contains three components. Its first component applies a mixture model of quadratic curves, with statistical features, to initialize the contour of the embryo of interest. An efficient method based on imbalanced image points is proposed to compute model parameters. The second component applies active contour model to refine embryo contours. The third component applies eigen-shape modeling to smooth jaggy contours caused by blurred embryo boundaries. We test the proposed framework on a data set of 8,000 embryonic images, and achieve promising accuracy (88 percent), that is, substantially higher than the-state-of-the-art results.

show abstract

Section: Introductionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contour Extraction of Drosophila Embryos

Kambhamettu

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

“…Futhermore, Frise et al [8] proposed a heuristic algorithm to separate the embryo of interest from multiple touching embryos, with the assumption that the center of the embryo of interest is the image center. Mace et al [9] proposed an eigen-embryo method to extract the contour of embryos, where a particle swarm optimizer was used to reduce the computational cost of searching optimal eigen parameters. Li and Kambhamettu [3] proposed a quadratic curve model to initialize the contour of the embryo of interest based on edge pixels, and applied an active contour model to refine embryo contours.…”

Section: Introductionmentioning

confidence: 99%

A geometric method for contour extraction of Drosophila embryos

Gong

2017

BMC Syst Biol

View full text Add to dashboard Cite

BackgroundHigh resolution images of Drosophila embryos in their developmental stages contain rich spatial and temporal information of gene expression. Automatic extraction of the contour of an embryo of interest in an embryonic image is a critical step of a computational system used to discover gene-gene interaction on Drosophila.ResultsWe propose a geometric method for contour extraction of Drosophila embryos. The key of the proposed geometric method is k-dominant point extraction that is a generalization of 3-dominant point extraction proposed in our previous work. Based on k-dominant point extraction, we can approximate a connected component of edge pixels by a polygon that can be either convex or concave. The test on BDGP data shows that the proposed method outputforms two existing methods designed for contour extraction of Drosophila embryos.ConclusionsThe main advantage of the proposed geometric method in the context of contour extraction of Drosophila embryos is its ability of segmenting embryos touching each other. The proposed geometric method can also be applied to applications relevant to contour extraction.

show abstract

“…For example, the image-based profiling of gene expression patterns via in situ hybridization (ISH) requires the development of accurate and automatic image analysis systems for using such data, to understand regulatory networks and development of multicellular organisms. Images are affected by multiple sources of noise due to experiments or microscopy (incomplete or multiple embryos, variance of probes across genes, illumination artifacts), making the extraction and registration of embryos non-trivial (Fowlkes et al , 2005, 2008; Harmon et al , 2007; Keranen et al , 2006; Kumar et al , 2002; Mace et al , 2010; Puniyani et al , 2010;). Peng and Myers (2004) and Peng et al (2007) introduced an automatic image annotation framework using various high-dimensional feature representations and classifying frameworks: Principal Component Analysis (PCA), wavelets, Gaussian mixture models, Support Vector Machines (SVM), Quadratic Discriminant Analysis.…”

Section: Introductionmentioning

confidence: 99%

Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields

Pruteanu-Malinici¹,

Majoros²,

Ohler³

2013

Bioinformatics

View full text Add to dashboard Cite

Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously.Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm. The approach is based on an effective feature selection technique, consisting of a non-parametric sparse Bayesian factor analysis model. The result is a flexible framework, which can handle large-scale data with noisy incomplete samples, i.e. it can tolerate data missing from individual time points.Results: Using the annotation of gene expression patterns across stages of Drosophila embryonic development as an example, we demonstrate that our method achieves superior accuracy, gained by jointly annotating phenotype sequences, when compared with previous models that annotate each stage in isolation. The experimental results on missing data indicate that our joint learning method successfully annotates genes for which no expression data are available for one or more stages.Contact: uwe.ohler@duke.edu

show abstract

Extraction and comparison of gene expression patterns from 2D RNA in situ hybridization images

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 28 publications

References 42 publications

Contour Extraction of Drosophila Embryos

Contour Extraction of Drosophila Embryos

A geometric method for contour extraction of Drosophila embryos

Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields

Contact Info

Product

Resources

About