Biology subjects

Huebschmann, D.

Publications and source records attributed to Huebschmann, D..

23 records · Page 2Linked to original sources

bioRxiv2021

Make Interactive Complex Heatmaps in R

Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In R, there are many packages that make heatmaps. Among them, ComplexHeatmap provides rich tools for constructing highly customizable heatmaps. It can easily establish connections between information from multiple sources by automatically concatenating and adjusting multiple heatmaps as well as complex annotations, which makes it widely applied in data analysis in various fields, especially in Bioinformatics. Nevertheless, the limit of ComplexHeatmap still exists. It only generates static plots which restricts deeper inspections on complex heatmaps, e.g., to look into a subset of rows and columns when a specific pattern of interest is observed from the heatmap. In this work, we described a new R/Bioconductor package InteractiveComplexHeatmap that brings interactivity to ComplexHeatmap. InteractiveComplexHeatmap is designed with an easy-to-use interface where static complex heatmaps can be directly exported to an interactive Shiny web application only with one extra line of code. The interactive application contains comprehensive tools for manipulating heatmaps. Besides common tools as supported in other interactive heatmap packages, InteractiveComplexHeatmap additionally supports, e.g., selecting over multiple heatmaps and searching heatmaps via row or column labels. Also, InteractiveComplexHeatmap provides methods for exporting static heatmaps from other popular heatmap functions, e.g., heatmap.2() or pheatmap(), to interactive heatmap applications. Finally, InteractiveComplexHeatmap provides flexible functionalities for integrating interactive heatmap widgets into other Shiny applications. InteractiveComplexHeatmap provides a user interface for self-defining response to the selection events on heatmaps, which helps to implement more complex Shiny web applications.

bioinformatics↗

bioRxiv2020

simplifyEnrichment: an R/Bioconductor package for Clustering and Visualizing Functional Enrichment Results

Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut can efficiently cluster functional terms into groups where terms showed more consistent similarities within groups and were more mutually exclusive between groups. We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that the semantic similarity worked well with binary cut while similarity matrices based on gene overlap showed less consistent patterns. We implemented the binary cut algorithm in the R package simplifyEnrichment which additionally provides functionalities for visualizing, summarizing and comparing the clusterings.

bioinformatics↗

bioRxiv2017

ACEseq - allele specific copy number estimation from whole genome sequencing

ACEseq is a computational tool for allele-specific copy number estimation in tumor genomes based on whole genome sequencing. In contrast to other tools it features GC-bias correction, unique replication timing-bias correction and integration of structural variant (SV) breakpoints for improved genome segmentation. ACEseq clearly outperforms widely used state-of-the art methods, provides a fully automated estimation of tumor cell content and ploidy, and additionally computes homologous recombination deficiency scores.

bioinformatics↗

bioRxiv2017

Deciphering programs of transcriptional regulation by combined deconvolution of multiple omics layers

Metazoans are crucially dependent on multiple layers of gene regulatory mechanisms which allow them to control gene expression across developmental stages, tissues and cell types. Multiple recent research consortia have aimed to generate comprehensive datasets to profile the activity of these cell type- and condition-specific regulatory landscapes across many different cell lines and primary cells. However, extraction of genes or regulatory elements specific to certain entities from these datasets remains challenging. We here propose a novel method based on non-negative matrix factorization for disentangling and associating huge multi-assay datasets including chromatin accessibility and gene expression data. Taking advantage of implementations of NMF algorithms in the GPU CUDA environment full datasets composed of tens of thousands of genes as well as hundreds of samples can be processed without the need for prior feature selection to reduce the input size. Applying this framework to multiple layers of genomic data derived from human blood cells we unravel mechanisms of regulation of cell type-specific expression in T-cells and monocytes.

genomics↗

bioRxiv2017

Framework For Quality Assessment Of Whole Genome, Cancer Sequences

Working with cancer whole genomes sequenced over a period of many years in different sequencing centres requires a validated framework to compare the quality of these sequences. The Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC), a project a cohort of over 2800 donors provided us with the challenge of assessing the quality of the genome sequences. A non-redundant set of five quality control (QC) measurements were assembled and used to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses of these whole genome sequences. The resulting QC measures also allowed for exclusion samples of poor quality, providing researchers within PCAWG, and when the data is released for other researchers, a good idea of the sequencing quality. For a researcher wishing to apply the QC measures for their data we provide a Docker Container of the software used to calculate them. We believe that this is an effective framework of quality measures for whole genome, cancer sequences, which will be a useful addition to analytical pipelines, as it has to the PCAWG project.

genomics↗