Differential expression of long non-coding RNAs are related to proliferation and histological diversity in follicular lymphomas.

Long non-coding RNAs (lncRNAs) comprise a family of non-coding transcripts that are emerging as relevant gene expression regulators of different processes, including tumour development. To determine the possible contribution of lncRNA to the pathogenesis of follicular lymphoma (FL) we performed RNA-sequencing at high depth sequencing in primary FL samples ranging from grade 1-3A to aggressive grade 3B variants using unpurified (n = 16) and purified (n = 12) tumour cell suspensions from nodal samples. FL grade 3B had a significantly higher number of differentially expressed lncRNAs (dif-lncRNAs) with potential target coding genes related to cell cycle regulation. Nine out of the 18 selected dif-lncRNAs were validated by quantitative real time polymerase chain reaction in an independent series (n = 43) of FL. RP4-694A7.2 was identified as the top deregulated lncRNA potentially involved in cell proliferation. RP4-694A7.2 silencing in the WSU-FSCCL FL cell line reduced cell proliferation due to a block in the G1/S phase. The relationship between RP4-694A7.2 and proliferation was confirmed in primary samples as its expression levels positively related to the Ki-67 proliferation index. In summary, lncRNAs are differentially expressed across the clinico-biological spectrum of FL and a subset of them, related to cell cycle, may participate in cell proliferation regulation in these tumours.

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

BACKGROUND: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. RESULTS: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. CONCLUSIONS: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue.

The detection of tumoural cells from whole slide images is an essential task in medical diagnosis and research. In this article, we propose and analyse a novel approach that combines computer vision-based models with graph neural networks to improve the accuracy of automated tumoural cell detection in lung tissue. Our proposal leverages the inherent structure and relationships between cells in the tissue. Experimental results on our own curated dataset show that modelling the problem with graphs gives the model a clear advantage over just working at pixel level. This change in perspective provides extra information that makes it possible to improve the performance. The reduction of dimensionality that comes from working with the graph also allows us to increase the field of view with low computational requirements. Code is available at https://github.com/Jerry-Master/lung-tumour-study, models are uploaded to https://huggingface.co/Jerry-Master/Hovernet-plus-Graphs, and the dataset is published on Zenodo https://zenodo.org/doi/10.5281/zenodo.8368122.

Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation.

Purpose: The diagnosis and prognosis of breast cancer relies on histopathology image analysis. In this context, proliferation markers, especially Ki67, are increasingly important. The diagnosis using these markers is based on the quantification of proliferation, which implies the counting of Ki67 positive and negative tumoral cells in epithelial regions, thus excluding stromal cells. However, stromal cells are often very difficult to distinguish from negative tumoral cells in Ki67 images and often lead to errors when automatic analysis is used. Approach: We study the use of automatic semantic segmentation based on convolutional neural networks (CNNs) to separate stromal and epithelial areas on Ki67 stained images. CNNs need to be accurately trained with extensive databases with associated ground truth. As such databases are not publicly available, we propose a method to produce them with minimal manual labelling effort. Inspired by the procedure used by pathologists, we have produced the database relying on knowledge transfer from cytokeratin-19 images to Ki67 using an image-to-image (I2I) translation network. Results: The automatically produced stroma masks are manually corrected and used to train a CNN that predicts very accurate stroma masks for unseen Ki67 images. An F-score value of 0.87 is achieved. Examples of effect on the KI67 score show the importance of the stroma segmentation. Conclusions: An I2I translation method has proved very useful for building ground-truth labeling in a task where manual labeling is unfeasible. With reduced correction effort, a dataset can be built to train neural networks for the difficult problem of separating epithelial regions from stroma in stained images where separation is very hard without additional information.

Unsupervised GRN Ensemble.

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this chapter, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because they are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

Binary partition trees for object detection.

This paper discusses the use of Binary Partition Trees (BPTs) for object detection. BPTs are hierarchical region-based representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the search space. In this paper, several issues related to the use of BPT for object detection are studied. Concerning the tree construction, we analyze the compromise between computational complexity reduction and accuracy. This will lead us to define two parts in the BPT: one providing accuracy and one representing the search space for the object detection task. Then we analyze and objectively compare various similarity measures for the tree construction. We conclude that different similarity criteria should be used for the part providing accuracy in the BPT and for the part defining the search space and specific criteria are proposed for each case. Then we discuss the object detection strategy based on BPT. The notion of node extension is proposed and discussed. Finally, several object detection examples illustrating the generality of the approach and its efficiency are reported.

Monocular depth ordering using T-junctions and convexity occlusion cues.

This paper proposes a system that relates objects in an image using occlusion cues and arranges them according to depth. The system does not rely on a priori knowledge of the scene structure and focuses on detecting special points, such as T-junctions and highly convex contours, to infer the depth relationships between objects in the scene. The system makes extensive use of the binary partition tree as hierarchical region-based image representation jointly with a new approach for candidate T-junction estimation. Since some regions may not involve T-junctions, occlusion is also detected by examining convex shapes on region boundaries. Combining T-junctions and convexity leads to a system which only relies on low level depth cues and does not rely on semantic information. However, it shows a similar or better performance with the state-of-the-art while not assuming any type of scene. As an extension of the automatic depth ordering system, a semi-automatic approach is also proposed. If the user provides the depth order for a subset of regions in the image, the system is able to easily integrate this user information to the final depth order for the complete image. For some applications, user interaction can naturally be integrated, improving the quality of the automatically generated depth map.

Hyperspectral image representation and processing with binary partition trees.

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.

Hierarchical clustering combining numerical and biological similarities for gene expression data classification.

High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results.