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OmicVerse 文档
OmicVerse 安装指南
教程
Bulk RNA-seq
使用STAR进行Bulk RNA-seq比对
使用kb-python进行Bulk RNA-seq比对
Bulk RNA-seq或微阵列数据中的批次校正
差异表达分析
使用DEseq2进行差异表达分析
使用String-db进行蛋白质-蛋白质相互作用（PPI）分析
WGCNA（加权基因共表达网络分析）分析
基于参考scRNA-seq的Bulk反卷积
TCGA数据库预处理
单细胞 RNA-seq
单细胞RNA-seq数据的比对和分析
单细胞RNA-seq数据的比对和RNA速度分析
Preprocessing the data of scRNA-seq with omicverse[CPU-GPU-mixed]
Preprocessing the data of scRNA-seq with omicverse[GPU]
单细胞聚类和注释
Data integration and batch correction
cNMF：共识非负矩阵分解
单细胞RNA-seq懒惰分析
无参考自动化单细胞细胞类型注释
使用参考的自动化单细胞细胞类型注释
Mapping Cell Names to the Cell Ontology/Taxonomy
单独的注释方法
使用 SCSA 进行细胞类型自动注释
Automatic cell type annotation with GPT/Other
使用 MetaTiME 的细胞类型自动注释
Using scMulan to annotate cell types in Heart, Lung, Liver, Bone marrow, Blood, Brain, and Thymus
Celltype annotation migration(mapping) with TOSICA
CytoTRACE：细胞轨迹重建和推理
使用 Diffusion Map 和 PAGA 进行轨迹推断
使用 Slingshot 进行轨迹推断
使用 Palantir 进行轨迹推断
使用 scTour 进行轨迹推断
使用 Monocle 2 进行轨迹推断
使用 StaVIA 进行轨迹推断
使用 StaVIA 进行轨迹推断：toy_multifurcating 官方测试数据
使用 VIA 进行轨迹推断
Trajectory Inference with VIA and scVelo
Timing-associated genes analysis with TimeFateKernel
Identify the driver regulators of cell fate decisions
单细胞差异表达分析
Differential expression analysis [Meta Cell]
使用 SCENIC 进行基因调控网络分析
Drug response predict with scDrug
Data integration and batch correction with SIMBA
Single-cell copy-number variation with CopyKAT
Single-cell copy-number variation with inferCNV
Pathway analysis with AUCell
Comparing enrichment-score methods on scRNA-seq
使用 CellPhoneDB 进行细胞通讯分析
使用 LIANA+ 进行细胞通讯分析
Velocity Basic Calculation
Velocity Optimization
使用 RegVelo 进行 RNA velocity 和调控扰动分析
使用推断 GRN 运行 RegVelo
基于 RNA velocity 的 CellRank 分析
Multi omics analysis by MOFA
Multi omics analysis by MOFA and GLUE
转录组数据转移和细胞类型注释
Bulk 转单细胞与跨平台
Bulk RNA-seq generate ‘interrupted’ cells to interpolate scRNA-seq
批量 RNA-seq 到单细胞 RNA-seqBulk2Single 用于批量 RNA-seq 反卷积。我们从 Bulk2Space 算法中提取了 beta-VAE 部分，并构建了一种可以从批量 RNA-seq 反卷积到单细胞 RNA-seq 的算法。此外，我们重新设计了数据的输入和输出，使其更兼容 Python 环境中的分析约定。Paper: De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolutionCode: https://github.com/ZJUFanLab/bulk2spaceColab_Reproducibility：https://colab.research.google.com/drive/1He71hAyeAv1DHQyXUlxtoJ4QvwZwW7I0?usp=sharing本教程介绍了如何从批量 RNA-seq 和参考 scRNA-seq 数据中读取、设置和训练模型。我们使用 pdac 数据集作为示例
Single RNA-seq to Spatial RNA-seq
空间转录组学
裁剪和旋转空间转录组学数据
细胞分割（10x HD）
分析 NanoString 数据
Analyze 10x Atera (WTA Preview) FFPE breast cancer data
分析 Visium HD 数据
空间聚类和降噪表达
空间集成和聚类
识别伪空间映射
带参考 scRNA-seq 的空间反卷积
Spatial deconvolution with RCTD
FlashDeconv：通过结构保留草图实现快速空间反卷积
不使用参考 scRNA-seq 的空间反卷积
单细胞空间转移张量
Spatial CommunicationSpatial communication is a point of interest for us for the 空间转录组学 Society, and we would like to find the conduction process of spatial communication.Here, we introduce two method integrated in OmicVerse named
COMMOT
and
flowsig
.We made three improvements in integrating the
COMMOT
and
flowsig
algorithm in OmicVerse:- We reduced the installation conflict of
COMMOT
and
flowsig
, user only need to update OmicVerse to the latest version.- We optimized the 可视化 of
COMMOT
and
flowsig
and unified the data 预处理 process so that users don’t need to struggle with different data processing flows.- We have fixed some bugs that could occur during function.If you found this tutorial helpful, please cite
COMMOT
,
flowsig
and OmicVerse:- Cang, Z., Zhao, Y., Almet, A.A. et al. Screening cell–cell communication in 空间转录组学 via collective optimal transport. Nat Methods 20, 218–228 (2023). https://doi.org/10.1038/s41592-022-01728-4- Almet, A.A., Tsai, YC., Watanabe, M. et al. Inferring pattern-driving intercellular flows from 单细胞 and 空间转录组学. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02380-w
空间同深度计算
单细胞空间对齐工具
基础模型
ov.fm — 基础模型模块
ov.fm — Unified Foundation Model API
scGPT
scGPT
Geneformer
GeneFormer
UCE
UCE
scFoundation
scFoundation
CellPLM
CellPLM
scBERT
scBERT — 基础模型教程
GeneCompass
GeneCompass — Foundation Model Tutorial
Nicheformer
Nicheformer — 基础模型教程
scMulan
scMulan — 基础模型教程
tGPT
tGPT — Foundation Model Tutorial
CellFM
CellFM — Foundation Model Tutorial
scCello
scCello — 基础模型教程
scPRINT
scPRINT — 基础模型教程
AIDO.Cell
AIDO.Cell — 基础模型教程
PULSAR
PULSAR — Foundation Model Tutorial
Tabula
Tabula — 基础模型教程
ATACformer
ATACformer — Foundation Model Tutorial
scPlantLLM
scPlantLLM — 基础模型教程
LangCell
LangCell — 基础模型教程
Cell2Sentence
Cell2Sentence — Foundation Model Tutorial
GenePT
GenePT — 基础模型教程
ChatCell
ChatCell — Foundation Model Tutorial
可视化与绘图
Visualization of single cell RNA-seq
Bulk RNA-seq 可视化
Palette optimization for publication-quality single-cell & spatial plots
Color system
Circular UMAP with plot1cell
OmicClaw
网关与频道
OmicClaw Gateway 概述
OmicClaw 配置与认证
OmicClaw Telegram 教程
OmicClaw 飞书 教程
OmicClaw iMessage 教程
OmicClaw QQ 教程
OmicClaw 会话工作流程
OmicClaw 故障排查
MCP 服务器
OmicVerse MCP 服务器
OmicVerse MCP 快速开始
OmicVerse MCP 完整启动
OmicVerse MCP 工具目录
OmicVerse MCP 客户端与部署
OmicVerse MCP 运行时与故障排查
OmicVerse MCP 参考文档
在 Claude Code 中使用 OmicVerse MCP — 逐步指南
通用 Notebook
J.A.R.V.I.S. 与 PBMC3k
J.A.R.V.I.S. 十任务套件
API 参考
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omicverse.generate_reference_table
omicverse.settings.cpu_gpu_mixed_init
omicverse.settings.gpu_init
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omicverse.io.read_10x_h5
omicverse.io.read_10x_mtx
omicverse.io.read_csv
omicverse.io.read_h5ad
omicverse.io.read_nanostring
omicverse.io.read_visium_hd
omicverse.io.read_visium_hd_bin
omicverse.io.read_visium_hd_seg
omicverse.io.read_xenium
omicverse.io.save
omicverse.io.spatial.read_visium
omicverse.alignment.amplicon_16s_pipeline
omicverse.alignment.build_amplicon_anndata
omicverse.alignment.build_phylogeny
omicverse.alignment.bulk_rnaseq_pipeline
omicverse.alignment.count
omicverse.alignment.cutadapt
omicverse.alignment.dada2_pipeline
omicverse.alignment.fastp
omicverse.alignment.fasttree
omicverse.alignment.featureCount
omicverse.alignment.fetch_rdp
omicverse.alignment.fetch_silva
omicverse.alignment.fetch_sintax_ref
omicverse.alignment.fqdump
omicverse.alignment.mafft
omicverse.alignment.parallel_fastq_dump
omicverse.alignment.prefetch
omicverse.alignment.ref
omicverse.alignment.STAR
omicverse.pp.anndata_to_CPU
omicverse.pp.anndata_to_GPU
omicverse.pp.binary_search
omicverse.pp.champ
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omicverse.pp.preprocess
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omicverse.pp.recover_counts
omicverse.pp.regress
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omicverse.pp.score_genes_cell_cycle
omicverse.pp.scrublet
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omicverse.pp.select_hvf_pegasus
omicverse.pp.sude
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omicverse.single.cosg
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omicverse.single.download_cl
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omicverse.bulk.pyDEG
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omicverse.metabol.anova
omicverse.metabol.asca
omicverse.metabol.asca_variance_bar
omicverse.metabol.biomarker_panel
omicverse.metabol.blank_filter
omicverse.metabol.corr_network
omicverse.metabol.corr_network_plot
omicverse.metabol.cv_filter
omicverse.metabol.dgca
omicverse.metabol.dgca_class_bar
omicverse.metabol.differential
omicverse.metabol.drift_correct
omicverse.metabol.fetch_chebi_compounds
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omicverse.metabol.mummichog_basic
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omicverse.metabol.opls_da
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omicverse.metabol.plsda
omicverse.metabol.pyMetabo
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omicverse.micro.Alpha
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omicverse.micro.fetch_franzosa_ibd_2019
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omicverse.micro.ilr
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omicverse.micro.rarefy
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omicverse.space.bin2cell
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omicverse.space.CAST
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omicverse.datasets.hl60
目录
hl60()
omicverse.datasets.hl60
#
omicverse.datasets.
hl60
(
)
[源代码]
#
TODO: add data here
目录
hl60()
