scholarly journals An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection

2012 ◽  
Vol 28 (19) ◽  
pp. 2534-2536 ◽  
Author(s):  
Xingbin Wang ◽  
Dongwan D. Kang ◽  
Kui Shen ◽  
Chi Song ◽  
Shuya Lu ◽  
...  
Keyword(s):  
Quality Control ◽  
Meta Analysis ◽  
Differentially Expressed Gene ◽  
R Package ◽  
Gene Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment

scholarly journals MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics

2021 ◽  
Author(s):  
Alan E Murphy ◽  
Brian M Schilder ◽  
Nathan G Skene
Keyword(s):  
Quality Control ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Research ◽  
Secondary Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Summary Statistic

Abstract Motivation Genome-wide association studies (GWAS) summary statistics have popularised and accelerated genetic research. However, a lack of standardisation of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. Results To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardisation and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardised, tabular summary statistic file, VCF or R native data object. Availability MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats Supplementary information The analysis deriving the most common summary statistic formats is available at: https://al-murphy.github.io/SumstatFormats

scholarly journals MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics

2021 ◽  
Author(s):  
Alan E Murphy ◽  
Nathan G Skene
Keyword(s):  
Quality Control ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Research ◽  
Secondary Analysis ◽  
R Package ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Summary Statistic ◽  
File Formats

Genome-wide association studies (GWAS) summary statistics have democratised and accelerated genetic research. However, a lack of standardisation of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. To address these issues, we have developed MungeSumstats, a Bioconductor R package for the standardisation and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardised, tabular summary statistic file.

scholarly journals scCODE: an R package for personalized differentially expressed gene detection on single-cell RNA-sequencing data

2021 ◽  
Author(s):  
jiawei Zou ◽  
miaochen Wang ◽  
zhen Zhang ◽  
zheqi Liu ◽  
xiaobin Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Gene ◽  
R Package ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Detection ◽  
Gene Filtering ◽  
Consensus Optimization ◽  
Detection Strategies

Differential expression (DE) gene detection in single-cell RNA-seq (scRNA-seq) data is a key step to understand the biological question investigated. We find that DE methods together with gene filtering have profound impact on DE gene identification, and different datasets will benefit from personalized DE gene detection strategies. Existing tools don't take gene filtering into consideration, and couldn't evaluate DE performance on real datasets without prior knowledge of true results. Based on two new metrics, we propose scCODE (single cell Consensus Optimization of Differentially Expressed gene detection), an R package to automatically optimize DE gene detection for each experimental scRNA-seq dataset.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

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