PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
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。
PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
library(PhIPData)
# 1. Define dimensions and simulate data matrices
n_samples <- 5L
n_peps <- 10L
counts_dat <- matrix(sample(1:1e6, n_samples*n_peps, replace = TRUE), nrow = n_peps)
logfc_dat <- matrix(rnorm(n_samples*n_peps, mean = 0, sd = 10), nrow = n_peps)
prob_dat <- matrix(rbeta(n_samples*n_peps, shape1 = 1, shape2 = 1), nrow = n_peps)
# 2. Define metadata
peptide_meta <- data.frame(pep_id = paste0("pep_", 1:n_peps))
sample_meta <- data.frame(
sample_name = paste0("sample", 1:n_samples),
group = c("ctrl", "beads", "trt", "trt", "trt")
)
# 3. Construct PhIPData object and subset beads-only samples
phip_obj <- PhIPData(counts_dat, logfc_dat, prob_dat, peptide_meta, sample_meta)
beads_only <- subsetBeads(phip_obj)
Note: Inputs are parallel matrices of counts, log fold-changes, probabilities, and metadata data frames; output is a coordinated PhIPData object.
subsetBeads() wrapper.setAlias(), getAlias(), and standard subset() operations.DESeq2 or edgeR because PhIPData is specifically tailored for PhIP-seq antibody enrichment assays.SingleCellExperiment because PhIPData is designed for bulk phage-display sequencing experiments.QFeatures or MSnbase because PhIPData is designed for high-throughput sequencing of phage libraries.counts (non-negative numeric values), logfc (log2 estimated fold-changes), and prob (enrichment probabilities/p-values).DataFrame or data.frame containing peptide identifiers, and optionally start/end positions (pos_start, pos_end).DataFrame or data.frame containing sample names and group designations (e.g., "beads", "ctrl", "trt").librarySize() and propReads(), controls whether output vectors/matrices retain row and column names.group with a "beads" level to enable automatic identification of beads-only control samples via subsetBeads().makeLibrary() to save peptide annotations as reusable templates, avoiding redundant re-importing of large peptide metadata files.PhIPData objects to DGEList using as(phip_obj, "DGEList") when transitioning to edgeR for differential enrichment analysis.pos_start and pos_end are missing from the peptide metadata, the constructor replaces them with 0 and prints a warning.PhIPData object without counts, logfc, or prob will result in empty matrices being initialized for the missing assays."hiv" vs "HIV") are case-sensitive; ensure consistent casing when calling getAlias().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。