<![CDATA[单细胞数据分析概要]]>单细胞分析涉及的几个方面：
https://asap.epfl.ch/home/tutorial?t=getting_started
Cell filtering
Gene filtering
Normalization
Scaling
Dimension reduction
Clustering
Differential expression
Gene enrichment

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6582955/
5fdae3b2-1f06-4088-9c0f-a9544cc15f79-image.png
https://scrnaseq-course.cog.sanger.ac.uk/website/introduction-to-single-cell-rna-seq.html
11ace4e3-4811-443b-be19-31bda66efca8-image.png

调研的几个分析工具：
1.ASAP Automated Single-cell Analysis Pipeline
https://academic.oup.com/nar/article/48/W1/W403/5843818
https://asap.epfl.ch/
这个平台的思路和我们GenoStack一样 通过web的方式提供单细胞分析环境

2.M3Drop

3.Seurat
https://www.nature.com/articles/nbt.3192?cookies=accepted

4.BioTuring Single-Cell Browser (Bbrowser)
5. the Loupe cell browser
6.Scanpy 一个python工具集
https://link.springer.com/article/10.1186/s13059-017-1382-0
7. Cumulus 基于Terra的WDL机制做的流程 比较符合我们的场景
Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.pdf
https://cumulus.readthedocs.io/en/stable/index.html
https://pegasus.readthedocs.io/en/stable/_static/tutorials/pegasus_analysis.html
一个应用案例
https://www.nature.com/articles/s41591-020-0844-1

8.一个商业化的单细胞数据分析浏览器
https://bioturing.com/bbrowser
dda00118-b592-4c9f-82b1-d86bcac5f45f-image.png
对Seurat \ Scanpy的支持：
https://blog.bioturing.com/2019/09/26/a-new-tool-to-interactively-visualize-single-cell-objects-seurat-scanpy-singlecellexperiments/
9.Broad出品的single cell portal
https://github.com/broadinstitute/single_cell_portal

10.https://www.archrproject.com/bookdown/index.html#section
参考资料：
1.https://singlecell.usegalaxy.eu/
Galaxy的单细胞工作台 有一些培训和软件说明
2.Terra 推荐cumulus
https://support.terra.bio/hc/en-us/articles/360060041772-Human-Cell-Atlas-Exploring-HCA-data-on-Terra
3. Sanger的培训材料
https://scrnaseq-course.cog.sanger.ac.uk/website/introduction-to-single-cell-rna-seq.html
4.Bioconductor中的一本书 比较全面 重点看看
https://bioconductor.org/books/release/OSCA/ Orchestrating Single-Cell Analysis with Bioconductor
5.Current best practices in single‐cell RNA‐seq analysis: a tutorial
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6582955/
https://github.com/theislab/single-cell-tutorial
6.single-cell transcriptomics essentials
https://swaruplab.bio.uci.edu/tutorial/snRNA-essentials/snRNA-essentials.html
7.https://broadinstitute.github.io/KrumlovSingleCellWorkshop2020/
8.https://support.terra.bio/hc/en-us/articles/360060041772-Human-Cell-Atlas-Exploring-HCA-data-on-Terra

]]>http://an.forum.genostack.com/topic/350/单细胞数据分析概要RSS for NodeSat, 13 Jun 2026 12:36:25 GMTWed, 21 Jul 2021 03:58:45 GMT60<![CDATA[Reply to 单细胞数据分析概要 on Fri, 26 Nov 2021 06:57:56 GMT]]>单细胞大数据解决方案.pptx

]]>http://an.forum.genostack.com/post/912http://an.forum.genostack.com/post/912Fri, 26 Nov 2021 06:57:56 GMT<![CDATA[Reply to 单细胞数据分析概要 on Tue, 23 Nov 2021 01:57:56 GMT]]>https://squidpy.readthedocs.io/en/latest/
squidpy 空间转录组分析

]]>http://an.forum.genostack.com/post/901http://an.forum.genostack.com/post/901Tue, 23 Nov 2021 01:57:56 GMT<![CDATA[Reply to 单细胞数据分析概要 on Mon, 22 Nov 2021 10:10:00 GMT]]>https://github.com/hbctraining/scRNA-seq

]]>http://an.forum.genostack.com/post/900http://an.forum.genostack.com/post/900Mon, 22 Nov 2021 10:10:00 GMT<![CDATA[Reply to 单细胞数据分析概要 on Mon, 22 Nov 2021 10:02:23 GMT]]>https://www.singlecellcourse.org/
sanger的单细胞教程

]]>http://an.forum.genostack.com/post/899http://an.forum.genostack.com/post/899Mon, 22 Nov 2021 10:02:23 GMT<![CDATA[Reply to 单细胞数据分析概要 on Mon, 22 Nov 2021 06:17:54 GMT]]>https://humancellatlas.usegalaxy.eu/

]]>http://an.forum.genostack.com/post/894http://an.forum.genostack.com/post/894Mon, 22 Nov 2021 06:17:54 GMT<![CDATA[Reply to 单细胞数据分析概要 on Mon, 22 Nov 2021 06:17:33 GMT]]>https://tertiary-workflows-docs.readthedocs.io/en/v1.0.0/index.html

]]>http://an.forum.genostack.com/post/893http://an.forum.genostack.com/post/893Mon, 22 Nov 2021 06:17:33 GMT<![CDATA[Reply to 单细胞数据分析概要 on Sat, 13 Nov 2021 02:53:44 GMT]]>https://scanpy.discourse.group/t/highly-variable-genes-best-practice/29/5
scanpy hvgs
You can find our current best-practices in the recent publication here 98. Typically the pre-processing steps in an analysis workflow would be:

Cell & Gene QC
Normalization
Batch correction (or data integration)
HVG selection
Dimensionality reduction (including visualization)

]]>http://an.forum.genostack.com/post/876http://an.forum.genostack.com/post/876Sat, 13 Nov 2021 02:53:44 GMT<![CDATA[Reply to 单细胞数据分析概要 on Sat, 13 Nov 2021 02:30:03 GMT]]>https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/scrna-scanpy-pbmc3k/tutorial.html

]]>http://an.forum.genostack.com/post/875http://an.forum.genostack.com/post/875Sat, 13 Nov 2021 02:30:03 GMT<![CDATA[Reply to 单细胞数据分析概要 on Thu, 04 Nov 2021 07:17:59 GMT]]>scapy 培训视频

]]>http://an.forum.genostack.com/post/854http://an.forum.genostack.com/post/854Thu, 04 Nov 2021 07:17:59 GMT<![CDATA[Reply to 单细胞数据分析概要 on Thu, 04 Nov 2021 08:35:39 GMT]]>单细胞类型注释：
https://www.cellkb.com/
https://www.biostars.org/p/433235/
scCATCH(https://github.com/ZJUFanLab/scCATCH)
SCSA (https://github.com/bioinfo-ibms-pumc/SCSA).
https://www.frontiersin.org/articles/10.3389/fgene.2020.00490/full
SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
单细胞待解决的问题：
https://openproblems.bio/

]]>http://an.forum.genostack.com/post/850http://an.forum.genostack.com/post/850Thu, 04 Nov 2021 08:35:39 GMT<![CDATA[Reply to 单细胞数据分析概要 on Tue, 02 Nov 2021 08:23:53 GMT]]>数据集
https://cellxgene.cziscience.com/
https://www.covid19cellatlas.org/index.patient.html

https://www.biorxiv.org/content/10.1101/2021.07.19.452956v1
The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors

]]>http://an.forum.genostack.com/post/845http://an.forum.genostack.com/post/845Tue, 02 Nov 2021 08:23:53 GMT<![CDATA[Reply to 单细胞数据分析概要 on Thu, 26 Aug 2021 08:54:01 GMT]]>https://rnabioco.github.io/cellar/
Single-cell RNA-seq Workshop

]]>http://an.forum.genostack.com/post/799http://an.forum.genostack.com/post/799Thu, 26 Aug 2021 08:54:01 GMT<![CDATA[Reply to 单细胞数据分析概要 on Tue, 17 Aug 2021 09:55:34 GMT]]>SingleCellRNASeq_10X_June19.pdf

]]>http://an.forum.genostack.com/post/769http://an.forum.genostack.com/post/769Tue, 17 Aug 2021 09:55:34 GMT<![CDATA[Reply to 单细胞数据分析概要 on Thu, 12 Aug 2021 10:32:21 GMT]]>https://terra.bio/speed-up-your-machine-learning-work-with-gpus/

GPU 支持 例子也用的是pegasus

]]>http://an.forum.genostack.com/post/750http://an.forum.genostack.com/post/750Thu, 12 Aug 2021 10:32:21 GMT<![CDATA[Reply to 单细胞数据分析概要 on Thu, 12 Aug 2021 06:49:22 GMT]]>https://sci-hub.st/10.1093/nar/gkx949
单细胞数据库的整合问题

]]>http://an.forum.genostack.com/post/747http://an.forum.genostack.com/post/747Thu, 12 Aug 2021 06:49:22 GMT<![CDATA[Reply to 单细胞数据分析概要 on Wed, 04 Aug 2021 08:50:53 GMT]]>分析可视化
https://github.com/klarman-cell-observatory/scPlot  这个项目是专门针对单细胞的图表
The plots provided include scatter plots, feature plots, dot plots and violin plots and can scale to millions of cells by plotting cells on a 2D grid. scPlot uses HoloViews (http://holoviews. org/), thus allowing generation of interactive plots with Bokeh for a Jupyter notebook.
https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html
Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons

]]>http://an.forum.genostack.com/post/727http://an.forum.genostack.com/post/727Wed, 04 Aug 2021 08:50:53 GMT<![CDATA[Reply to 单细胞数据分析概要 on Wed, 04 Aug 2021 03:21:20 GMT]]>https://academic.oup.com/bfg/article/17/4/233/4604806
Experimental design for single-cell RNA sequencing
单细胞的实验设计
plate-based Smart-Seq2
microdroplet-based scRNA-seq（10x Chromium）
droplet-based workflow
3c778065-217e-4831-9ba0-11b7bad0498e-image.png
A key difference between Smart -Seq2 and the 10x Chromium protocol lies in the way the RNA is processed to cDNA. Smart-seq2 captures the full-length mRNA, although with significant 3′ bias because of oligo dT primers used during cDNA generation, while the 10x protocol is based on a 3′-tag sequencing method (Figure 1A). Accordingly, it is important to consider the aim of the study when selecting a method for single-cell RNA-seq. For example, full-length capture is needed for studies concerned with isoforms or gene fusions, while 3′-tag methods can capture more cells and thus give an aggregate view of the transcriptional heterogeneity of a given cell population.

]]>http://an.forum.genostack.com/post/726http://an.forum.genostack.com/post/726Wed, 04 Aug 2021 03:21:20 GMT<![CDATA[Reply to 单细胞数据分析概要 on Thu, 12 Aug 2021 07:51:27 GMT]]>https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger
What is Cell Ranger?
Cell Ranger is a set of analysis pipelines that process Chromium single-cell data to align reads, generate feature-barcode matrices, perform clustering and other secondary analysis, and more. Cell Ranger includes four pipelines relevant to the 3' Single Cell Gene Expression Solution and related products:
cellranger mkfastq demultiplexes raw base call (BCL) files generated by Illumina sequencers into FASTQ files. It is a wrapper around Illumina's bcl2fastq, with additional features that are specific to 10x libraries and a simplified sample sheet format.

cellranger count takes FASTQ files from cellranger mkfastq and performs alignment, filtering, barcode counting, and UMI counting. It uses the Chromium cellular barcodes to generate feature-barcode matrices, determine clusters, and perform gene expression analysis. The count pipeline can take input from multiple sequencing runs on the same GEM well. cellranger count also processes Feature Barcode data alongside Gene Expression reads.

cellranger aggr aggregates outputs from multiple runs of cellranger count, normalizing those runs to the same sequencing depth and then recomputing the feature-barcode matrices and analysis on the combined data. The aggr pipeline can be used to combine data from multiple samples into an experiment-wide feature-barcode matrix and analysis.

cellranger reanalyze takes feature-barcode matrices produced by cellranger count or cellranger aggr and reruns the dimensionality reduction, clustering, and gene expression algorithms using tunable parameter settings.

cellranger multi is used to analyze Cell Multiplexing data. It inputs FASTQ files from cellranger mkfastq and performs alignment, filtering, barcode counting, and UMI counting. It uses the Chromium cellular barcodes to generate feature-barcode matrices, determine clusters, and perform gene expression analysis. The cellranger multi pipeline also supports the analysis of Feature Barcode data.

Cell Ranger下载：
curl -o cellranger-6.1.0.tar.gz "https://cf.10xgenomics.com/releases/cell-exp/cellranger-6.1.0.tar.gz?Expires=1628797758&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZi4xMHhnZW5vbWljcy5jb20vcmVsZWFzZXMvY2VsbC1leHAvY2VsbHJhbmdlci02LjEuMC50YXIuZ3oiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Mjg3OTc3NTh9fX1dfQ__&Signature=JvlfFHkP~2BGKQ6zKPXoW8N~zM9EpRyX-YUmXEiQjqOCO58kvAspZpeJZlN4MW74TdGf6ol4tShJ6X7oII6XPp3qEXPdR4e2TNxo27L2Of3xzTX7wyTAy5K9n62UgIGX33GnG9H5r01x9RRfXEcaFk5HOwT7fVB5FxcxLOOggsS5GJaqFJgvIWH6hEy6u0v0qelaBOiDq-N6mf44a1B~pgf1Bq-9lOBAw~IeAbj2mXnTTzC4RksAjYZRZgCNZdCzncPCUpltBTWPm-JgTC8f52vF-uFNfNxPBI8eedojkOcSP7PeaW8gRRBE-PPde2dF-qpyo2y7arVB-a2bsbpEPA__&Key-Pair-Id=APKAI7S6A5RYOXBWRPDA"

]]>http://an.forum.genostack.com/post/724http://an.forum.genostack.com/post/724Thu, 12 Aug 2021 07:51:27 GMT<![CDATA[Reply to 单细胞数据分析概要 on Thu, 22 Jul 2021 07:24:11 GMT]]>@anneng Single-Cell Analysis with Bioconductor.pdf

]]>http://an.forum.genostack.com/post/689http://an.forum.genostack.com/post/689Thu, 22 Jul 2021 07:24:11 GMT