Reviewer 3: It will be nice if you can share the codes… (CAF index MS)

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  • 哎呦我的媽，醜醜的code見得了人嗎?
    NoteBook多一個 .html檔，多麽邪魔

    Daniel
    太好了! 這一篇有望被接受!
    我是安裝Gossamer bioinformatics suite (https://github.com/data61/gossamer)使用裡面的Xenome模組 (https://github.com/data61/gossamer/blob/master/docs/xenome.md)
    Xenome 我需要給一個workflow的 script file 裡面包含所有指令跟檔案路徑嗎?
    XenofilteR 應該可以直接給GitHub連結 (https://github.com/PeeperLab/XenofilteR)
    對, 可以建立一個github account for this publication. 有兩種方式, 1, 直接fork (像給link, 可以追朔源頭) 別人的project, 2. 另外寫一套流程 python code for pipeline 把別人的程式碼放/包進來, 我之前都是一個個下指令, 沒有寫成腳本script 執行
    這是我以前的github account …. :p  https://github.com/danielsu0523
    都是直接implement成web application, Epimolas有放在江博士的github上
    好, 我先試著在github上編輯文件, 再轉給您後續增減內容
    我周末來回憶一下這些程式執行過程..
    前陣子念scRNA-seq paper, 許多人用相當一致的方法描述code availability😉
    • 2017.05.5610_A Transcriptional Program for Detecting TGFb-Induced EMT in Cancer
      • All computational and statistical analyses were performed using R (versions 3.1.1, 3.2.2, and 3.2.4) and Bioconductor (version 3.0). Further details are given in the Supplementary Methods, and the digital archive reproducing our results is also available at https://github.com/DavisLaboratory/mforoutan_tgfb_paper_2016.
    • 2018.08.5591_Nat Med_Allergic inflammatory memory in human respiratory epithelial progenitor cells. Ordovas-Montanes et al. The Shalek Lab.
      • Following alignment, reads were binned onto 12-bp cell barcodes and collapsed by their 8-bp UMI. Digital gene expression matrices (for example, cells-by-genes tables) for each sample were obtained from quality filtered and mapped reads, with an automatically determined threshold for cell count. UMI-collapsed data was used as input into Seurat (https://github. com/satijalab/seurat) for further analysis. Before incorporating a sample into our merged dataset, we individually inspected the cells-by-genes matrix of each as a Seurat object.
    • 2019.07.5599_Nat Med_A cellular census of human lungs identifies novel cell states in health and in asthma. Vieira Braga et al.
      • Normalization and scaling. Downstream analyses including, normalization, scaling, clustering of cells, and identifying cluster marker genes were performed using the R software package Seurat version 2.1 (https://github.com/satijalab/seurat).
      • Samples were log normalized and scaled for the number of genes, number
      • of UMIs, and percentage of mitochondrial reads. The epithelial biopsy dataset comparing healthy and asthma was also scaled for XIST expression, as we observed some gender specific clusters of cells that shared lineage markers with the other observed clusters.
    • 2019.12.5598_Genome Biol_scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation. Maddison et al.
      • Statistical analysis was performed in R ver- sion 3.5.0, and plotting was in Python via scanpy or cus- tom script and in R using ggplot2 version 2.2.1 or by using custom scripts. Cells which contained more than 10% mitochondrial reads were assigned by similarity to their closest cell type within a tissue with scmap tool [46], using cells with less than 10% mitochondrial reads as a reference. The high and low mitochondrial percent- age cells were then combined for calculating the mito- chondrial percentage per each cell type. All code for the analysis is available at https://github.com/elo073/TissStab.
      • Expression of known markers and re-analysis of bigger clusters were used to annotate cell types, with cell markers shown in Additional file 1: Figure S9. The major cell types were annotated for the lung, esophagus, and spleen by looking at expression of known cell type markers. Three subsets from the lung (mononuclear phagocytes and plasma cells; lymphocytes; dividing cells), two subsets from the esophagus (immune; small clusters), and two subsets from the spleen (DC, small clusters and dividing cells; CD4 and CD8 T cells) were extracted, further analyzed by re-clustering, and anno- tated using known markers. These updated annotations then replaced the original bigger ones.
    • 2020.04.5567_EMBO J_SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells. Lukassens et al.
      • Low-quality cells were removed during pre-processing using Seurat version 3.0.0 (https://github.com/satijalab/seurat) based on the following crite- ria: (a) > 200 or, depending on the sample, < 6,000–9,000 genes (surgical lung tissues)/< 3,000–5,000 genes (ALI cultures) and (b) < 15% mitochondrial reads (QC plots in Appendix Fig S3).
    • 2020.04.5568_Nat Med_SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes  Sungnak et al.
    • 2020.05.5569_Cell_CoV-2 Receptor ACE2 Is an Interferon- Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues Ziegler et al.

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