Main Steps
Hook dataset
Citation: Hook, Paul W., Sarah A. McClymont, Gabrielle H. Cannon, William D. Law, A. Jennifer Morton, Loyal A. Goff, and Andrew S. McCallion. 2018. “Single-Cell RNA-Seq of Mouse Dopaminergic Neurons Informs Candidate Gene Selection for Sporadic Parkinson Disease.” American Journal of Human Genetics 102 (3): 427–46.
1. Obtain the data
473 single cell RNA-Seq samples from sorted mouse Th-eGFP+ dopaminergic neurons collected at two timepoints from three distinct brain regions.
#wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE108nnn/GSE108020/suppl/GSE108020_fpkm_table.txt.gz
#unzip GSE108020_fpkm_table.txt.gz
After downloading the data, unzip the file of FPKM matrix for further analysis.
Download raw fastq files from SRA
2. Filtration
- Filter out cells expressing less than 500 genes (min.genes = 500, Seurat)
Genome alignment
A. Get reference data from Gencode
Reference mouse genome: ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M20/GRCm38.primary_assembly.genome.fa.gz
Comprehensive gene annotation: ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M20/gencode.vM20.annotation.gtf.gz
For the processing details, please follow Code
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sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin17.5.0 (64-bit)
Running under: macOS 10.14.3
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.4.0 stringr_1.4.0 purrr_0.3.1 readr_1.3.1
[5] tidyr_0.8.3 tibble_2.0.1 tidyverse_1.2.1 dplyr_0.8.0.1
[9] Seurat_2.3.4 Matrix_1.2-14 cowplot_0.9.4 here_0.1
[13] DT_0.5 plotly_4.8.0 ggplot2_3.1.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 snow_0.4-3 backports_1.1.2
[4] Hmisc_4.2-0 workflowr_1.2.0 plyr_1.8.4
[7] igraph_1.2.4 lazyeval_0.2.1 splines_3.5.0
[10] digest_0.6.18 foreach_1.4.4 htmltools_0.3.6
[13] lars_1.2 gdata_2.18.0 magrittr_1.5
[16] checkmate_1.9.1 cluster_2.0.7-1 mixtools_1.1.0
[19] ROCR_1.0-7 modelr_0.1.4 R.utils_2.8.0
[22] colorspace_1.4-0 rvest_0.3.2 haven_2.1.0
[25] crayon_1.3.4 jsonlite_1.6 survival_2.42-6
[28] zoo_1.8-4 iterators_1.0.10 ape_5.2
[31] glue_1.3.1 gtable_0.2.0 kernlab_0.9-27
[34] prabclus_2.2-7 DEoptimR_1.0-8 scales_1.0.0
[37] mvtnorm_1.0-10 bibtex_0.4.2 Rcpp_1.0.0
[40] metap_1.1 dtw_1.20-1 viridisLite_0.3.0
[43] htmlTable_1.13.1 reticulate_1.11.1 foreign_0.8-70
[46] bit_1.1-14 proxy_0.4-23 mclust_5.4.3
[49] SDMTools_1.1-221 Formula_1.2-3 stats4_3.5.0
[52] tsne_0.1-3 htmlwidgets_1.3 httr_1.4.0
[55] gplots_3.0.1.1 RColorBrewer_1.1-2 fpc_2.1-11.1
[58] acepack_1.4.1 modeltools_0.2-22 ica_1.0-2
[61] pkgconfig_2.0.2 R.methodsS3_1.7.1 flexmix_2.3-15
[64] nnet_7.3-12 tidyselect_0.2.5 rlang_0.3.1
[67] reshape2_1.4.3 munsell_0.5.0 cellranger_1.1.0
[70] tools_3.5.0 cli_1.0.1 generics_0.0.2
[73] broom_0.5.1 ggridges_0.5.1 evaluate_0.10.1
[76] yaml_2.2.0 npsurv_0.4-0 knitr_1.20
[79] bit64_0.9-7 fs_1.2.6 fitdistrplus_1.0-14
[82] robustbase_0.93-3 caTools_1.17.1.2 RANN_2.6.1
[85] pbapply_1.4-0 nlme_3.1-137 whisker_0.3-2
[88] R.oo_1.22.0 xml2_1.2.0 hdf5r_1.0.1
[91] compiler_3.5.0 rstudioapi_0.9.0 png_0.1-7
[94] lsei_1.2-0 stringi_1.2.4 lattice_0.20-35
[97] trimcluster_0.1-2.1 pillar_1.3.1 Rdpack_0.10-1
[100] lmtest_0.9-36 data.table_1.12.0 bitops_1.0-6
[103] irlba_2.3.3 gbRd_0.4-11 R6_2.4.0
[106] latticeExtra_0.6-28 KernSmooth_2.23-15 gridExtra_2.3
[109] codetools_0.2-15 MASS_7.3-50 gtools_3.8.1
[112] assertthat_0.2.0 rprojroot_1.3-2 withr_2.1.2
[115] diptest_0.75-7 parallel_3.5.0 doSNOW_1.0.16
[118] hms_0.4.2 grid_3.5.0 rpart_4.1-13
[121] class_7.3-14 rmarkdown_1.10 segmented_0.5-3.0
[124] Rtsne_0.15 git2r_0.24.0 lubridate_1.7.4
[127] base64enc_0.1-3