Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages.
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(MSstatsLiP)
# Calculate proteolytic resistance ratios and perform differential analysis
Accessibility <- calculateProteolyticResistance(
MSstatsLiP_Summarized,
fasta_file,
differential_analysis = TRUE
)
# Plot proteolytic resistance results as a barcode along the protein sequence
ResistanceBarcodePlotLiP(
Accessibility,
fasta_file,
which.prot = "P16622",
which.condition = "F1",
differential_analysis = TRUE,
which.comp = "F1 vs F2",
address = FALSE
)
Input: Summarized LiP-MS and TrP-MS datasets (MSstatsLiP_Summarized) and a FASTA file (fasta_file).
Output: Proteolytic resistance ratios and sequence-aligned barcode plots.
library(MSstatsLiP)
# Convert raw Spectronaut data to MSstatsLiP format
msstats_data <- SpectronauttoMSstatsLiPFormat(
raw_lip,
fasta_file,
raw_prot
)
# Summarize the preprocessed data
MSstatsLiP_Summarized <- dataSummarizationLiP(
msstats_data,
normalization.LiP = "equalizeMedians"
)
# Perform group comparison modeling
MSstatsLiP_model <- groupComparisonLiP(MSstatsLiP_Summarized)
Input: Raw Spectronaut LiP and TrP datasets, and a FASTA file. Output: Preprocessed, summarized, and modeled LiP-MS and TrP-MS datasets.
SpectronauttoMSstatsLiPFormat.calculateProteolyticResistance and groupComparisonLiP.ResistanceBarcodePlotLiP.MSstats directly.limma, edgeR, or DESeq2.PG.ProteinAccessions, PEP.GroupingKey, EG.Qvalue, and F.PeakArea.dataSummarizationLiP.calculateProteolyticResistance.ResistanceBarcodePlotLiP.ResistanceBarcodePlotLiP.ResistanceBarcodePlotLiP.calculateTrypticity.unique(msstats_data[["LiP"]]$Condition) %in% unique(msstats_data[["TrP"]]$Condition).rm() and gc() if needed.MSstats for standard proteomics without limited proteolysis.limma for general linear modeling of expression 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。