The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.
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。
The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.
library(MsDataHub)
library(Spectra)
library(QFeatures)
# 1. Load raw MS data (TripleTOF DDA) and create a Spectra object
f_dda <- PestMix1_DDA.mzML()
s_dda <- Spectra(f_dda)
# 2. Load DIA-NN label-free DIA data and read into QFeatures
lfdia <- read.delim(MsDataHub::benchmarkingDIA.tsv())
qf <- readQFeaturesFromDIANN(lfdia)
Note: Inputs are cached file paths retrieved from MsDataHub accessor functions; outputs are Spectra or QFeatures objects containing mass spectrometry data.
.mzML, .CDF) from standard proteomics/metabolomics experiments via ExperimentHub caching..mzid) using PSM() from the PSMatch package.SummarizedExperiment or QFeatures structures using readSummarizedExperiment() or readQFeaturesFromDIANN().xcms because MsDataHub only distributes pre-existing example datasets.MSstats because MsDataHub is a data repository rather than an analytical tool.AnnotationDbi or biomaRt because MsDataHub only provides mass spectrometry-specific data and contaminant databases..mzML (e.g., PestMix1_DDA.mzML()), .CDF (e.g., ko15.CDF()), or compressed .mzML.gz..mzid format (e.g., TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()).cptac_peptides.txt(), benchmarkingDIA.tsv()) containing peptide intensities or DIA-NN report outputs.readQFeaturesFromDIANN(), set to "mTRAQ" for multiplexed plexDIA data.readSummarizedExperiment(), specifies the field separator character for reading text files.readSummarizedExperiment(), specifies the column indices containing quantitative intensity data.MsDataHub() inside DT::datatable() to browse the complete list of available datasets and their metadata.Spectra object using Spectra(f) to leverage memory-efficient backends.read.delim() to load the file path returned by MsDataHub::benchmarkingDIA.tsv() before passing it to readQFeaturesFromDIANN().PestMix1_DDA.mzML()) to download and retrieve the local cache path first.grep("Intensity\\.", names(read.delim(f))) to dynamically identify quantitative columns before calling readSummarizedExperiment().multiplexing is omitted or set correctly when using readQFeaturesFromDIANN() on label-free vs mTRAQ plexDIA data.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。