Last updated: 2019-02-21

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File	Version	Author	Date	Message
html	6fa2bc6	Yasin Kaymaz	2019-02-16	Build site.
Rmd	e4ef6cf	Yasin Kaymaz	2019-02-16	Publishing contrast results
html	99374a6	Yasin Kaymaz	2019-02-16	Build site.
Rmd	0f4a96c	Yasin Kaymaz	2019-02-16	second Commit to repo

Summary

Main Steps

Lake 2018 dataset

1. Obtain the data

#CerebellarHem data:
#wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97930/suppl/GSE97930_CerebellarHem_snDrop-seq_UMI_Count_Matrix_08-01-2017.txt.gz
#unzip GSE97930_CerebellarHem_snDrop-seq_UMI_Count_Matrix_08-01-2017.txt.gz 

#FrontalCortex data:
#wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97930/suppl/GSE97930_FrontalCortex_snDrop-seq_UMI_Count_Matrix_08-01-2017.txt.gz
#unzip GSE97930_FrontalCortex_snDrop-seq_UMI_Count_Matrix_08-01-2017.txt.gz 

#VisualCortex data:
#wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97930/suppl/GSE97930_VisualCortex_snDrop-seq_UMI_Count_Matrix_08-01-2017.txt.gz
#unzip GSE97930_VisualCortex_snDrop-seq_UMI_Count_Matrix_08-01-2017.txt.gz

After downloading the data, unzip the file of count matrix for further analysis.

3. I also filtered cells based on their expressed gene quantity.

Filter out cells expressing less than 500 genes (min.genes = 500, Seurat)

For the processing details, please follow Code

Session information

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_0.3.0   stringr_1.4.0   purrr_0.2.5     readr_1.1.1    
 [5] tidyr_0.8.1     tibble_2.0.1    tidyverse_1.2.1 dplyr_0.7.6    
 [9] Seurat_2.3.3    Matrix_1.2-14   cowplot_0.9.3   here_0.1       
[13] DT_0.4          plotly_4.8.0    ggplot2_3.1.0  

loaded via a namespace (and not attached):
  [1] diffusionMap_1.1-0   Rtsne_0.15           colorspace_1.4-0    
  [4] class_7.3-14         modeltools_0.2-22    ggridges_0.5.0      
  [7] mclust_5.4.1         rprojroot_1.3-2      htmlTable_1.12      
 [10] base64enc_0.1-3      rstudioapi_0.8       proxy_0.4-22        
 [13] flexmix_2.3-14       bit64_0.9-7          lubridate_1.7.4     
 [16] mvtnorm_1.0-8        xml2_1.2.0           codetools_0.2-15    
 [19] splines_3.5.0        R.methodsS3_1.7.1    robustbase_0.93-1   
 [22] knitr_1.20           Formula_1.2-3        jsonlite_1.6        
 [25] workflowr_1.1.1      broom_0.5.0          ica_1.0-2           
 [28] cluster_2.0.7-1      kernlab_0.9-26       png_0.1-7           
 [31] R.oo_1.22.0          compiler_3.5.0       httr_1.3.1          
 [34] backports_1.1.2      assertthat_0.2.0     lazyeval_0.2.1      
 [37] cli_1.0.1            lars_1.2             acepack_1.4.1       
 [40] htmltools_0.3.6      tools_3.5.0          bindrcpp_0.2.2      
 [43] igraph_1.2.1         gtable_0.2.0         glue_1.3.0          
 [46] reshape2_1.4.3       RANN_2.6             Rcpp_1.0.0          
 [49] cellranger_1.1.0     trimcluster_0.1-2    gdata_2.18.0        
 [52] ape_5.1              nlme_3.1-137         iterators_1.0.10    
 [55] fpc_2.1-11           lmtest_0.9-36        rvest_0.3.2         
 [58] irlba_2.3.2          gtools_3.8.1         DEoptimR_1.0-8      
 [61] zoo_1.8-3            MASS_7.3-50          scales_1.0.0        
 [64] hms_0.4.2            doSNOW_1.0.16        parallel_3.5.0      
 [67] RColorBrewer_1.1-2   yaml_2.2.0           reticulate_1.9      
 [70] pbapply_1.3-4        gridExtra_2.3        segmented_0.5-3.0   
 [73] rpart_4.1-13         latticeExtra_0.6-28  stringi_1.2.4       
 [76] foreach_1.4.4        checkmate_1.8.5      caTools_1.17.1      
 [79] SDMTools_1.1-221     rlang_0.3.1          pkgconfig_2.0.2     
 [82] dtw_1.20-1           prabclus_2.2-6       bitops_1.0-6        
 [85] evaluate_0.10.1      lattice_0.20-35      ROCR_1.0-7          
 [88] bindr_0.1.1          htmlwidgets_1.2      bit_1.1-14          
 [91] tidyselect_0.2.4     plyr_1.8.4           magrittr_1.5        
 [94] R6_2.3.0             snow_0.4-3           gplots_3.0.1        
 [97] Hmisc_4.1-1          haven_1.1.2          pillar_1.3.1        
[100] whisker_0.3-2        foreign_0.8-70       withr_2.1.2         
[103] mixtools_1.1.0       fitdistrplus_1.0-9   survival_2.42-6     
[106] scatterplot3d_0.3-41 nnet_7.3-12          tsne_0.1-3          
[109] modelr_0.1.2         crayon_1.3.4         hdf5r_1.0.1         
[112] KernSmooth_2.23-15   rmarkdown_1.10       readxl_1.1.0        
[115] grid_3.5.0           data.table_1.11.4    git2r_0.23.0        
[118] metap_0.9            digest_0.6.18        diptest_0.75-7      
[121] R.utils_2.6.0        stats4_3.5.0         munsell_0.5.0       
[124] viridisLite_0.3.0

This reproducible R Markdown analysis was created with workflowr 1.1.1

Data Analysis

Yasin Kaymaz