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    单细胞数据分析概要

    单细胞分析
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    • A
      anneng 最后由 anneng 编辑

      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

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      • I
        ice-melt @anneng 最后由 编辑

        @anneng Single-Cell Analysis with Bioconductor.pdf

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        • A
          anneng 最后由 anneng 编辑

          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"

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          • A
            anneng 最后由 编辑

            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.

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            • A
              anneng 最后由 anneng 编辑

              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

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              • A
                anneng 最后由 编辑

                https://sci-hub.st/10.1093/nar/gkx949
                单细胞数据库的整合问题

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                • A
                  anneng 最后由 编辑

                  https://terra.bio/speed-up-your-machine-learning-work-with-gpus/

                  GPU 支持 例子也用的是pegasus

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                  • A
                    anneng 最后由 编辑

                    SingleCellRNASeq_10X_June19.pdf

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                    • A
                      anneng 最后由 编辑

                      https://rnabioco.github.io/cellar/
                      Single-cell RNA-seq Workshop

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                      • A
                        anneng 最后由 编辑

                        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

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                        • A
                          anneng 最后由 anneng 编辑

                          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/

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                          • A
                            anneng 最后由 编辑

                            scapy 培训视频

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                            • A
                              anneng 最后由 编辑

                              https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/scrna-scanpy-pbmc3k/tutorial.html

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                              • A
                                anneng 最后由 编辑

                                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)

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                                • A
                                  anneng 最后由 编辑

                                  https://tertiary-workflows-docs.readthedocs.io/en/v1.0.0/index.html

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                                  • A
                                    anneng 最后由 编辑

                                    https://humancellatlas.usegalaxy.eu/

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                                    • A
                                      anneng 最后由 编辑

                                      https://www.singlecellcourse.org/
                                      sanger的单细胞教程

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                                      • A
                                        anneng 最后由 编辑

                                        https://github.com/hbctraining/scRNA-seq

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                                        • A
                                          anneng 最后由 编辑

                                          https://squidpy.readthedocs.io/en/latest/
                                          squidpy 空间转录组分析

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                                          • A
                                            anneng 最后由 编辑

                                            单细胞大数据解决方案.pptx

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