「analyse」の検索結果

This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.

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。

Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.

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 enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.

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。

A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

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。

Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or fo

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。

Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many po

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。

Intuitive framework for identifying spatially variable genes (SVGs) via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the

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。

InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell comm

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。

syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-bas

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。

The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data.

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。

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.

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。

Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to

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 implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enric

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。

cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat

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。

HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment.

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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.

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。

Remove AI writing patterns from academic prose. Detects and fixes inflated symbolism, promotional language, superficial analyses, vague attributions, AI vocabulary, and formulaic structures. Supports English and Chinese. Triggers on "remove AI patterns", "humanize text", "anti-AI polish", "去除AI写作痕迹", "人性化处理".

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。

拼写变体标准化工具。统一英美拼写差异（colour→color、analyse→analyze 等），内置 153 词条 （含屈折形式 covered/covering 等），支持自定义词典扩展。当用户提到英美拼写统一、拼写变体、 British/American spelling 等需求时使用此skill。

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 skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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。

Guidelines for clinical decision support (CDS) documents: biomarker-stratified cohort analyses and GRADE-graded treatment reports. Covers structure, executive summaries, evidence grading (1A–2C), stats (HR, CI, survival), and biomarker integration. Use for pharma research docs, clinical guidelines, regulatory submissions.

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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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。

Use when a manuscript comes back with reviewer or editor comments. Numbers every comment, classifies it MAJOR/MINOR/REBUTTAL, drafts a point-by-point response with tracked manuscript changes, routes new analyses to /analyze-stats and writes the cover letter.

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。

Use when drafting a medical research manuscript or any IMRAD section. Runs an 8-phase pipeline from outline to submission-ready draft for original articles, AI validation studies, case reports, meta-analyses, technical notes and more. Checking a draft is /self-review.

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。