The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.
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The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.
library(cellxgenedp)
library(dplyr)
# 1. Initialize database
cellxgene_db <- db()
# 2. Discover collections, datasets, and authors
cols <- collections(cellxgene_db)
dsets <- datasets(cellxgene_db)
auths <- authors()
# 3. Query datasets by specific author
author_datasets <- left_join(
authors(),
datasets(),
by = "collection_id",
relationship = "many-to-many"
)
# 4. Filter by disease using facets
covid_datasets <- author_datasets |>
filter(facets_filter(disease, "label", "COVID-19"))
Inputs/Outputs: Takes online metadata from the CELLxGENE data portal retrieved via db(), and outputs filtered tibbles of collections, datasets, and authors matching specific search criteria.
authors() and datasets().facets() and facets_filter().cxg().scran, scater, or Seurat instead).db().zellkonverter.left_join when merging authors and datasets.facets() to retrieve ontology terms and counts.facets_filter().db() to fetch the latest database state from the CELLxGENE portal at the start of your session.distinct(authors()) to deduplicate author records before performing downstream joins, as some collections contain duplicate author entries.facets(db(), "disease") or other facet fields to inspect available ontology terms and their exact labels before filtering.relationship = "many-to-many" in left_join().disease column is a list-of-lists because a dataset can contain multiple diseases; use facets_filter() or tidyr::unnest_longer() to handle it.scRNAseq for accessing curated public single-cell datasets from Bioconductor.HCAData for accessing Human Cell Atlas 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.
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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。