暗能星系

    • 登录
    • 搜索

    单细胞数据分析概要

    单细胞分析
    2
    20
    76
    正在加载更多帖子
    • 从旧到新
    • 从新到旧
    • 最多赞同
    回复
    • 在新帖中回复
    登录后回复
    此主题已被删除。只有拥有主题管理权限的用户可以查看。
    • 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.

      1 条回复 最后回复 回复 引用 0
      • 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

        1 条回复 最后回复 回复 引用 0
        • 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

          1 条回复 最后回复 回复 引用 0
          • 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

            1 条回复 最后回复 回复 引用 0
            • A
              anneng 最后由 编辑

              SingleCellRNASeq_10X_June19.pdf

              1 条回复 最后回复 回复 引用 0
              • A
                anneng 最后由 编辑

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

                1 条回复 最后回复 回复 引用 0
                • 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

                  1 条回复 最后回复 回复 引用 0
                  • 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/

                    1 条回复 最后回复 回复 引用 0
                    • A
                      anneng 最后由 编辑

                      scapy 培训视频

                      1 条回复 最后回复 回复 引用 0
                      • A
                        anneng 最后由 编辑

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

                        1 条回复 最后回复 回复 引用 0
                        • 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)

                          1 条回复 最后回复 回复 引用 0
                          • A
                            anneng 最后由 编辑

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

                            1 条回复 最后回复 回复 引用 0
                            • A
                              anneng 最后由 编辑

                              https://humancellatlas.usegalaxy.eu/

                              1 条回复 最后回复 回复 引用 0
                              • A
                                anneng 最后由 编辑

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

                                1 条回复 最后回复 回复 引用 0
                                • A
                                  anneng 最后由 编辑

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

                                  1 条回复 最后回复 回复 引用 0
                                  • A
                                    anneng 最后由 编辑

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

                                    1 条回复 最后回复 回复 引用 0
                                    • A
                                      anneng 最后由 编辑

                                      单细胞大数据解决方案.pptx

                                      1 条回复 最后回复 回复 引用 0
                                      • First post
                                        Last post
                                      Powered by 暗能星系