An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.
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。
Perform batch effect correction on a data frame containing missing values using ComBat or limma.
library(HarmonizR)
# 1. Prepare the input data frame and sample description data frame
df <- data.frame(matrix(rnorm(n = 3*6), ncol = 6))
colnames(df) <- c("A", "B", "C", "D", "E", "F")
rownames(df) <- c("F1", "F2", "F3")
batch <- rep(1:3, each = 2)
des <- data.frame(ID = colnames(df), sample = 1:6, batch = batch)
# 2. Run harmonizR and write results
result <- harmonizR(df, des, output_file = FALSE, cores = 1)
Input: A data frame of features with potential missing values and a sample description data frame; Output: A batch-corrected data frame.
harmonizR().SummarizedExperiment object containing batch metadata in colData.sva::ComBat or limma::removeBatchEffect directly because HarmonizR's matrix dissection overhead is unnecessary.HarmonizR assumes log-transformed input for plotting and standard processing.data.frame, matrix, or SummarizedExperiment object.SummarizedExperiment with batch info in colData.** ("ComBat"): Batch correction algorithm to use, either "ComBat"or"limma"`."samplemeans", "featuremeans", or "CV"..tsv file.ur = TRUE enabled to maximize the rescue of features across batches.harmonizR() to ensure correct behavior of the underlying ComBat/limma algorithms and plotting functions.block parameter to group batches together during matrix dissection to reduce runtime on large datasets.harmonizR() with plotting enabled.block parameter to an integer greater than 1 to pack batches together during dissection.ID, sample, and batch columns matching the input matrix column names.sva (specifically ComBat) for batch correction when there are no missing values.limma (specifically removeBatchEffect) for linear model-based batch correction without missing values.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.
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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).
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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。