Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.
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(miaSim)
# 1. Generate species interaction matrix
A_normal <- powerlawA(n_species = 4, alpha = 3)
# 2. Simulate time series using generalized Lotka-Volterra (gLV) model
tse_glv <- simulateGLV(
n_species = 4,
A = A_normal,
t_start = 0,
t_store = 1000,
stochastic = FALSE,
norm = FALSE
)
# 3. Simulate community using Hubbell Neutral model
tse_hubbell <- simulateHubbell(
n_species = 8,
M = 10,
carrying_capacity = 1000,
k_events = 50,
migration_p = 0.02,
t_end = 100
)
Input/Output Note: Inputs are ecological parameters (species count, interaction matrices, carrying capacity); outputs are simulated abundance matrices stored in TreeSummarizedExperiment objects.
simulateGLV) or its discrete version, the Ricker model (simulateRicker).simulateHubbell).powerlawA or randomA.TreeSummarizedExperiment data container.powerlawA or randomA.TreeSummarizedExperiment objects containing simulated abundance matrices.powerlawA.randomA.randomA.simulateHubbell and simulateSOI.simulateHubbell and simulateHubbellRates.powerlawA or randomA before running models like GLV, SOI, or Ricker.randomE to generate production and consumption rate matrices specifically for the consumer-resource model (simulateConsumerResource).n_species matches the dimensions of A.micodymora: A Python package for microbiome simulation.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。