scholarly journals Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

2020 ◽  
Author(s):  
Eric R. Reed ◽  
Stefano Monti
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Large Scale ◽  
Recursive Partitioning ◽  
A Priori ◽  
Cost Effective ◽  
R Package ◽  
Cancer Tissue ◽  
Data Types ◽  
Transcriptomics Data

AbstractAs high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a “taxonomy-like” structure (https://github.com/montilab/K2Taxonomer). K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.

scholarly journals muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Helena L. Crowell ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Daniela Calini ◽  
Ludovic Collin ◽  
...  
Keyword(s):  
Single Cell ◽  
Mixed Models ◽  
Large Scale ◽  
R Package ◽  
State Transitions ◽  
Cell Level ◽  
Method Performance ◽  
Statistical Framework ◽  
Mouse Cortex ◽  
Transcriptomics Data

AbstractSingle-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

scholarly journals Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
C. Loukas ◽  
S. Kostopoulos ◽  
A. Tanoglidi ◽  
D. Glotsos ◽  
C. Sfikas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Rapid Assessment ◽  
Breast Cancer Diagnosis ◽  
Critical Issue ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Textural Features ◽  
Tissue Sections ◽  
Leave One Out

Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination.

scholarly journals CD24 is a Potential Biomarker for Prognosis in Human Breast Carcinoma

2018 ◽  
Vol 48 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Xuan Jing ◽  
Xiangrong Cui ◽  
Hongping Liang ◽  
Chonghua Hao ◽  
Zhining Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mrna Expression ◽  
Large Scale ◽  
Prognostic Indicator ◽  
Normal Breast ◽  
Prognostic Indicators ◽  
Cancer Tissue ◽  
Potential Biomarker ◽  
Increased Risk ◽  
Cd24 Mrna

Background/Aims: CD24 is a highly glycosylated mucin-like antigen on the cell surface, which has recently emerged as a novel oncogene and metastasis promoter. We performed bioinformatics analysis to investigate whether CD24 can serve as a prognostic indicator in breast cancer. Methods: CD24 expression was assessed using SAGE Genie tools and Oncomine analysis. The PrognoScan database, Kaplan-Meier Plotter, and bc-GenExMiner were used to identify the prognostic roles of CD24 in breast cancer. Results: We found that CD24 was more frequently overexpressed in breast cancer than in normal breast tissue and correlated with worse prognosis. Meanwhile, high CD24 expression was associated with increased risk of HER2, basal-like, triple-negative breast cancer, and higher Scarff-Bloom-Richardson grade. Data mining in multiple big databases confirmed a positive correlation between CD24 mRNA expression and SDC1 mRNA expression in breast cancer tissue. Conclusions: Our findings suggest that CD24 overexpression is more common in breast cancer than in corresponding normal tissue. In addition, CD24 and SDC1 can serve as prognostic indicators for breast cancer. However, large-scale and comprehensive research is needed to further confirm these results.

scholarly journals Scale-Specific Multifractal Medical Image Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Boris Braverman ◽  
Mauro Tambasco
Keyword(s):  
Breast Cancer ◽  
Fractal Analysis ◽  
Large Scale ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Cancer Tissue ◽  
Clinical Value ◽  
Box Counting ◽  
Dependent Parameter ◽  
Single Number

Fractal geometry has been applied widely in the analysis of medical images to characterize the irregular complex tissue structures that do not lend themselves to straightforward analysis with traditional Euclidean geometry. In this study, we treat the nonfractal behaviour of medical images over large-scale ranges by considering their box-counting fractal dimension as a scale-dependent parameter rather than a single number. We describe this approach in the context of the more generalized Rényi entropy, in which we can also compute the information and correlation dimensions of images. In addition, we describe and validate a computational improvement to box-counting fractal analysis. This improvement is based on integral images, which allows the speedup of any box-counting or similar fractal analysis algorithm, including estimation of scale-dependent dimensions. Finally, we applied our technique to images of invasive breast cancer tissue from 157 patients to show a relationship between the fractal analysis of these images over certain scale ranges and pathologic tumour grade (a standard prognosticator for breast cancer). Our approach is general and can be applied to any medical imaging application in which the complexity of pathological image structures may have clinical value.

A Strategy for Large-Scale Phosphoproteomics and SRM-Based Validation of Human Breast Cancer Tissue Samples

2012 ◽  
Vol 11 (11) ◽  
pp. 5311-5322 ◽  
Author(s):  
Ryohei Narumi ◽  
Tatsuo Murakami ◽  
Takahisa Kuga ◽  
Jun Adachi ◽  
Takashi Shiromizu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Large Scale ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Human Breast ◽  
Human Breast Cancer Tissue ◽  
Tissue Samples

scholarly journals VeloViz: RNA-velocity informed 2D embeddings for visualizing cellular trajectories

2021 ◽  
Author(s):  
Lyla Atta ◽  
Jean Fan
Keyword(s):  
Single Cell ◽  
Principal Components ◽  
R Package ◽  
Velocity Analysis ◽  
Cell State ◽  
Link Type ◽  
Transcriptomics Data ◽  
Reliable Representation ◽  
State Changes

0AbstractRNA velocity analysis can predict cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with 2D embeddings derived from principal components, t-distributed stochastic neighbor embedding, among others. However, these 2D embeddings can yield different representations of the underlying trajectories, hindering the interpretation of cell state changes. To address this challenge, we developed VeloViz to create RNA-velocity-informed 2D embeddings. We show that by taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help ensure a more reliable representation of underlying cellular trajectories. VeloViz is available as an R package at https://github.com/JEFworks-Lab/veloviz.

scholarly journals Cell fixation and preservation for droplet-based single-cell transcriptomics

2017 ◽  
Author(s):  
Jonathan Alles ◽  
Nikos Karaiskos ◽  
Samantha D. Praktiknjo ◽  
Stefanie Grosswendt ◽  
Philipp Wahle ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Transcriptional Profiling ◽  
Cost Effective ◽  
R Package ◽  
Fixation Method ◽  
Recent Developments ◽  
Cell Gene Expression ◽  
Rare Cells ◽  
Cell Fixation

ABSTRACTBackgroundRecent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells, in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not compromised by stress or ageing. Another challenge are rare cells that need to be collected over several days, or samples prepared at different times or locations.ResultsHere, we used chemical fixation to overcome these problems. Methanol fixation allowed us to stabilize and preserve dissociated cells for weeks. By using mixtures of fixed human and mouse cells, we showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary single cells from dissociated complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells sorted by FACS, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide ‘dropbead’, an R package for exploratory data analysis, visualization and filtering of Drop-seq data.ConclusionsWe expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single cell resolution.

scholarly journals Calculating Biological Module Enrichment or Depletion and Visualizing Data on Large-scale Molecular Maps with ACSNMineR and RNaviCell R packages

2016 ◽  
Author(s):  
Paul Deveau ◽  
Emmanuel Barillot ◽  
Valentina Boeva ◽  
Andrei Zinovyev ◽  
Eric Bonnet
Keyword(s):  
Large Scale ◽  
Large Body ◽  
R Package ◽  
Biological Pathways ◽  
Biological Data ◽  
Cellular Systems ◽  
Biological Databases ◽  
Data Types ◽  
Functional Relationships ◽  
Body Of Knowledge

AbstractBiological pathways or modules represent sets of interactions or functional relationships occurring at the molecular level in living cells. A large body of knowledge on pathways is organized in public databases such as the KEGG, Reactome, or in more specialized repositories, such as the Atlas of Cancer Signaling Network (ACSN). All these open biological databases facilitate analyses, improving our understanding of cellular systems. We hereby describe the R packageACSNMineRfor calculation of enrichment or depletion of lists of genes of interest in biological pathways. ACSNMineR integrates ACSN molecular pathways, but can use any molecular pathway encoded as a GMT file, for instance sets of genes available in the Molecular Signatures Database (MSigDB). We also present the R packageRNaviCell, that can be used in conjunction withACSNMineRto visualize different data types on web-based, interactive ACSN maps. We illustrate the functionalities of the two packages with biological data taken from large-scale cancer datasets.

scholarly journals Metformin impact on purine metabolism in breast cancer

2016 ◽  
Vol 62 (3) ◽  
pp. 302-305
Author(s):  
O.P. Shatova ◽  
Eu.V. Butenko ◽  
Eu.V. Khomutov ◽  
D.S. Kaplun ◽  
I.Eu. Sedakov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adenosine Deaminase ◽  
Clinical Studies ◽  
Large Scale ◽  
Breast Adenocarcinoma ◽  
Cancer Tissue ◽  
Purine Nucleotides ◽  
Adenosine Concentration ◽  
Adenosine Deaminase Activity ◽  
Oncology Practice

Large-scale epidemiological and clinical studies have demonstrated the efficacy of metformin in oncology practice. However, the mechanisms of implementation of the anti-tumor effect of this drug there is still need understanding. In this study we have investigated the effect of metformin on the activity of adenosine deaminase and respectively adenosinergic immunosuppression in tumors and their microenvironment. The material of the study was taken during surgery of breast cacer patients receiveing metformin, and also patients which did not take this drug. The adenosine deaminase activity and substrate (adenosine) and products (inosine, hypoxanthine) concentrations were determined by HPLC. Results of this study suggest that metformin significantly alters catabolism of purine nucleotides in the node breast adenocarcinoma tisue. However, the metformin-induced increase in the adenosine deaminase activity is not sufficient to reduce the level of adenosine in cancer tissue. Thus, in metformin treated patients the adenosine concentration remained unchanged, and inosine and hypoxanthine concentration significantly increased.

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