Selected article for: "data analysis and principal component analysis"
Author: Guangchun Han; Ansam Sinjab; Warapen Treekitkarnmongkol; Patrick Brennan; Kieko Hara; Kyle Chang; Elena Bogatenkova; Beatriz Sanchez-Espiridion; Carmen Behrens; Boning Gao; Luc Girard; Jianjun Zhang; Boris Sepesi; Tina Cascone; Lauren Byers; Don L. Gibbons; Jichao Chen; Seyed Javad Moghaddam; Edwin J. Ostrin; Junya Fujimoto; Jerry Shay; John V. Heymach; John D. Minna; Steven Dubinett; Paul A. Scheet; Ignacio I. Wistuba; Edward Hill; Shannon Telesco; Christopher Stevenson; Avrum E. Spira; Linghua Wang; Humam Kadara
Title: Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells
  • Document date: 2020_4_17
    • ID: j3vruni3_16
    Snippet: Single-cell decoding was performed using available computational framework. Raw scRNA-seq data was pre-processed, demultiplexed, aligned to human GRCh38 reference and feature-barcodes generated using CellRanger (10X Genomics, version 3.0.2). Detailed quality control metrics were generated and evaluated. Genes detected in < 3 cells and cells where < 200 genes had non-zero counts were filtered out and excluded from subsequent analysis. Low quality .....
    Document: Single-cell decoding was performed using available computational framework. Raw scRNA-seq data was pre-processed, demultiplexed, aligned to human GRCh38 reference and feature-barcodes generated using CellRanger (10X Genomics, version 3.0.2). Detailed quality control metrics were generated and evaluated. Genes detected in < 3 cells and cells where < 200 genes had non-zero counts were filtered out and excluded from subsequent analysis. Low quality cells where >15% of the read counts originated from the mitochondrial genome were also discarded. In addition, cells with >6,000 detected genes were discarded to remove likely doublet or multiplet captures. Possible batch effects were evaluated and corrected using Harmony 17 . Raw unique molecular identifier (UMI) counts were log normalized and used for principal component analysis (PCA). We applied Seurat 18 for clustering and UMAP 19 for dimensional reduction to perform unsupervised analysis of the highdimensional data into distinct cell clusters. EPCAM+ cells were partitioned into major airway lineage clusters, followed by subclustering within each compartment/lineage to identify subpopulations.

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