1,331 research outputs found
Virtual Tissue Expression Analysis
No full textAbstract Motivation Bulk RNA expression data is widely accessible, whereas single-cell data is relatively scarce in comparison. However, single-cell data offers profound insights into the cellular composition of tissues and cell type-specific gene regulation, both of which remain hidden in bulk expression analysis. Results Here, we present tissueResolver, an algorithm designed to extract single-cell information from bulk data, enabling us to attribute expression changes to individual cell types. When validated on simulated data tissueResolver outperforms competing methods. Additionally, our study demonstrates that tissueResolver reveals cell type-specific regulatory distinctions between the activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subtypes of diffuse large B-cell lymphomas (DLBCL). Availability and Implementation R package available at https://github.com/spang-lab/tissueResolver. Code for reproducing the results of this paper is available at https://github.com/spang-lab/tissueResolver-docs1. Supplementary material Supplementary material and additional analyses available online
Genomic data integration using guided clustering
No full textMotivation: In biomedical research transcriptomic, proteomic or metabolomic profiles of patient samples are often combined with genomic profiles from experiments in cell lines or animal models. Integrating experimental data with patient data is still a challenging task due to the lack of tailored statistical tools. Results: Here we introduce guided clustering, a new data integration strategy that combines experimental and clinical high-throughput data. Guided clustering identifies sets of genes that stand out in experimental data while at the same time display coherent expression in clinical data. We report on two potential applications: The integration of clinical microarray data with (i) genome-wide chromatin immunoprecipitation assays and (ii) with cell perturbation assays. Unlike other analysis strategies, guided clustering does not analyze the two datasets sequentially but instead in a single joint analysis. In a simulation study and in several biological applications, guided clustering performs favorably when compared with sequential analysis approaches
Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models
No full textMotivation: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. Results: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines
Sequence Database Search Using Jumping Alignments
No full textSpang R, Rehmsmeier M, Stoye J. Sequence Database Search Using Jumping Alignments. In: Proc. of ISMB 2000. 2000: 367-375
Establishing a set of qRT-PCR assays to distinguish Burkitt's lymphoma from diffuse large B-cell lymphoma on a molecular level (first data)
No full textCoat Cooke & Joe Poole | Coat Cooke & Rainer Wiens: Reviews
No full textCoat Cooke album reviews by Randy Raine-Reusch. Coat Cooke (sax); Joe Poole (drums); Rainer Wiens (guitar)
Loss-function learning for digital tissue deconvolution
No full textThe gene expression profile of a tissue averages the expression profiles of all cells in this tissue. Digital tissue deconvolution (DTD) addresses the following inverse problem: Given the expression profile of a tissue, what is the cellular composition of that tissue? If is a matrix whose columns are reference profiles of individual cell types, the composition can be computed by minimizing for a given loss function . Current methods use predefined all-purpose loss functions. They successfully quantify the dominating cells of a tissue, while often falling short in detecting small cell populations. Here we learn the loss function along with the composition . This allows us to adapt to application specific requirements such as focusing on small cell populations or distinguishing phenotypically similar cell populations. Our method quantifies large cell fractions as accurately as existing methods and significantly improves the detection of small cell populations and the distinction of similar cell types
External calibration with Drosophila whole-cell spike-ins delivers absolute mRNA fold changes from human RNA-Seq and qPCR data
No full textGene expression measurements are typically performed on a fixed-weight aliquot of RNA, which assumes that the total number of transcripts per cell stays nearly constant across all conditions. In cases where this assumption does not hold (e.g., when comparing cell types with different cell sizes) the expression data provide a distorted view of cellular events. Assuming constant numbers of total transcripts, increases in expression of some RNAs must be compensated for by decreases in expression of others. Therefore, we propose calibrating gene expression data to an external reference point, the number of cells in the sample, using whole-cell spike-ins. In a systematic dilution experiment, we mixed varying numbers of human cells with fixed numbers of Drosophila melanogaster cells and scaled the expression levels of the human genes relative to those of the Drosophila genes. This approach restored the original gene expression ratios generated by the dilutions. We then used Drosophila whole-cell spike-ins to uncover non-symmetric gene expression changes, in this case much larger numbers of induced than repressed genes, under perturbations of the human cell line P493-6. Drosophila whole-cell spike-ins are an experimentally and computationally easy and low-priced method to derive mRNA fold changes of absolute abundances from RNA sequencing (RNA-Seq) and quantitative real-time PCR (qPCR) data
Robert Rainer and Claud Garner
No full textAuthor Claud Garner, right, autographed copies of his second novel while discussing a tour of other Southwest cities with Robert Rainer, representing his publisher, Creative Age Press. Published in the Fort Worth Star - Telegram morning edition, September 29, 1950.https://mavmatrix.uta.edu/specialcollections_startelegram1950s/6596/thumbnail.jp
Quantum chemistry of 2D-nanomaterials : investigation of graphene, hBN and α-borophene on SiO2 (001)
No full textAuthor: Felix Rainer Serafin Purtscher, BScMasterarbeit University of Innsbruck 202
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