A step-by-step guide to analyzing CAGE data using R/Bioconductor.
Ontology highlight
ABSTRACT:
SUBMITTER: Thodberg M
PROVIDER: S-EPMC6613478 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMeta-analyses are necessary to synthesize data obtained from primary research, and in many situations reviews of observational studies are the only available alternative. General purpose statistical packages can meta-analyze data, but usually require external macros or coding. Commercial specialist software is available, but may be expensive and focused in a particular type of primary data. Most available softwares have limitations in dealing with descriptive data, and the graphical display of summary statistics such as incidence and prevalence is unsatisfactory. Analyses can be conducted using Microsoft Excel, but there was no previous guide available.FindingsWe constructed a step-by-step guide to perform a meta-analysis in a Microsoft Excel spreadsheet, using either fixed-effect or random-effects models. We have also developed a second spreadsheet capable of producing customized forest plots.ConclusionsIt is possible to conduct a meta-analysis using only Microsoft Excel. More important, to our knowledge this is the first description of a method for producing a statistically adequate but graphically appealing forest plot summarizing descriptive data, using widely available software.
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Project description:Flow cytometry is one of the fundamental research tools available to the life scientist. The ability to observe multidimensional changes in protein expression and activity at single-cell resolution for a large number of cells provides a unique perspective on the behavior of cell populations. However, the analysis of complex multidimensional data is one of the obstacles for wider use of polychromatic flow cytometry. Recent enhancements to an open-source platform-R/Bioconductor-enable the graphical and data analysis of flow cytometry data. Prior examples have focused on high-throughput applications. To facilitate wider use of this platform for flow cytometry, the analysis of a dataset, obtained following isolation of CD4(+)CD62L(+) T cells from Balb/c splenocytes using magnetic microbeads, is presented as a form of tutorial. A common workflow for analyzing flow cytometry data was presented using R/Bioconductor. In addition, density function estimation and principal component analysis are provided as examples of more complex analyses. The compendium presented here is intended to help illuminate a path for inquisitive readers to explore their own data using R/Bioconductor (available as Supporting Information).
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Project description:BACKGROUND:5'-end sequencing assays, and Cap Analysis of Gene Expression (CAGE) in particular, have been instrumental in studying transcriptional regulation. 5'-end methods provide genome-wide maps of transcription start sites (TSSs) with base pair resolution. Because active enhancers often feature bidirectional TSSs, such data can also be used to predict enhancer candidates. The current availability of mature and comprehensive computational tools for the analysis of 5'-end data is limited, preventing efficient analysis of new and existing 5'-end data. RESULTS:We present CAGEfightR, a framework for analysis of CAGE and other 5'-end data implemented as an R/Bioconductor-package. CAGEfightR can import data from BigWig files and allows for fast and memory efficient prediction and analysis of TSSs and enhancers. Downstream analyses include quantification, normalization, annotation with transcript and gene models, TSS shape statistics, linking TSSs to enhancers via co-expression, identification of enhancer clusters, and genome-browser style visualization. While built to analyze CAGE data, we demonstrate the utility of CAGEfightR in analyzing nascent RNA 5'-data (PRO-Cap). CAGEfightR is implemented using standard Bioconductor classes, making it easy to learn, use and combine with other Bioconductor packages, for example popular differential expression tools such as limma, DESeq2 and edgeR. CONCLUSIONS:CAGEfightR provides a single, scalable and easy-to-use framework for comprehensive downstream analysis of 5'-end data. CAGEfightR is designed to be interoperable with other Bioconductor packages, thereby unlocking hundreds of mature transcriptomic analysis tools for 5'-end data. CAGEfightR is freely available via Bioconductor: bioconductor.org/packages/CAGEfightR .
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