Last updated: 2020-11-25

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Knit directory: bios_2020_single-cell-workshop-svi/

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Rmd eab0b17 rlyu 2020-11-16 finalising analysis workflow and sctransform
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html 4f6f985 rlyu 2020-11-05 Workshop material
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Introduction

In this workshop, we will introduce the data infrastructure for scRNAseq analysis in R and practice a workflow of scRNAseq analysis; from pre-processing, quality control to dimensionality reduction aand clustering. We will then demonstrate the usage of marker genes for cell type annotation and an automatic approach for matching query cells to a reference altas with labels.

  1. single-cell RNAseq analysis workflow for cell type identification

devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.3 (2020-10-10)
 os       Red Hat Enterprise Linux    
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_AU.UTF-8                 
 ctype    en_AU.UTF-8                 
 tz       Australia/Melbourne         
 date     2020-11-25                  

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version date       lib source        
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 Rcpp          1.0.5   2020-07-06 [1] CRAN (R 4.0.2)
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 rstudioapi    0.11    2020-02-07 [1] CRAN (R 4.0.2)
 sessioninfo   1.1.1   2018-11-05 [1] CRAN (R 4.0.2)
 stringi       1.5.3   2020-09-09 [1] CRAN (R 4.0.2)
 stringr       1.4.0   2019-02-10 [1] CRAN (R 4.0.2)
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 whisker       0.4     2019-08-28 [1] CRAN (R 4.0.2)
 withr         2.3.0   2020-09-22 [1] CRAN (R 4.0.2)
 workflowr     1.6.2   2020-04-30 [1] CRAN (R 4.0.2)
 xfun          0.19    2020-10-30 [1] CRAN (R 4.0.2)
 yaml          2.2.1   2020-02-01 [1] CRAN (R 4.0.2)

[1] /mnt/mcscratch/rlyu/Software/R/4.0/library
[2] /opt/R/4.0.3/lib/R/library