Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
library(ggmanh)
# Format chromosome column as a factor
simdata$chromosome <- factor(simdata$chromosome, c(1:22,"X"))
# Generate standard Manhattan plot
g1 <- manhattan_plot(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
# Generate rescaled Manhattan plot
g2 <- manhattan_plot(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", rescale = TRUE)
# Preprocess data
mpdata <- manhattan_data_preprocess(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
# Generate plots using preprocessed data with labels
g3 <- manhattan_plot(x = mpdata, label.colname = "label")
# Zoom into Chromosome 5
manhattan_plot(simdata, chromosome = 5, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
Note: Input is a data frame of GWAS summary statistics; output is a customized Manhattan plot object.
library(ggmanh)
# Basic Binned Manhattan Plot
binned_manhattan_plot(simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
# Preprocess binned data
mpdat <- binned_manhattan_preprocess(simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", bins.x = 7, bins.y = 100)
# Plot preprocessed binned data
binned_manhattan_plot(mpdat, bin.outline = TRUE)
Note: Input is a data frame of GWAS summary statistics; output is a binned grid-based Manhattan plot.
library(ggmanh)
# Annotate variants using gds_annotate
simdata_label$label <- gds_annotate(x = simdata_label, annot.method = "position", chr = "chromosome", pos = "position", ref = "Reference", alt = "Alternate")
# Plot annotated data
manhattan_plot(simdata_label, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", label.colname = "label")
Note: Inputs are a data frame of GWAS summary statistics and a GDS file; output is an annotated Manhattan plot.
manhattan_plot).rescale = TRUE).binned_manhattan_plot).gds_annotate.binned_manhattan_plot, as the plot will fail.annotation/symbol, annotation/consequence).manhattan_data_preprocess or binned_manhattan_preprocess first if you plan to customize the plot multiple times, avoiding redundant computation."" or NA to avoid overlapping labels and extremely slow plotting times."" or NA.binned_manhattan_plot.qqman for basic Manhattan and QQ plots.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
日本語の概要は準備中です。原文の説明を表示しています。
This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
日本語の概要は準備中です。原文の説明を表示しています。
This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
日本語の概要は準備中です。原文の説明を表示しています。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
日本語の概要は準備中です。原文の説明を表示しています。
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
日本語の概要は準備中です。原文の説明を表示しています。
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。