Last updated: 2019-07-29

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Knit directory: scRNA-seq-workshop-Fall-2019/

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Downstream analysis of scRNAseq data

Gene Set enrichment (GSEA) analysis

library(Seurat)
Warning: package 'Seurat' was built under R version 3.5.2
pbmc<- readRDS("data/pbmc5k/pbmc_5k_v3.rds")

# for GSEA, we need the information of all genes, Seurat is just too slow if we test
# all 20,000 genes. instead let's try presto which performs a fast Wilcoxon rank sum test 

#library(devtools)
#install_github('immunogenomics/presto')
library(presto)
Loading required package: Rcpp
pbmc.genes <- wilcoxauc(pbmc, 'seurat_clusters')

head(pbmc.genes)
     feature group     avgExpr         logFC statistic       auc
1 AL627309.1     0 0.005441269 -0.0039398743   2111934 0.4972455
2 AL627309.3     0 0.000000000 -0.0004917936   2122351 0.4996982
3 AL669831.5     0 0.058406758 -0.0098809068   2078179 0.4892981
4     FAM87B     0 0.000000000 -0.0017569714   2117864 0.4986417
5  LINC00115     0 0.033673117 -0.0053392639   2086692 0.4913023
6     FAM41C     0 0.015977393 -0.0161927551   2074044 0.4883245
          pval         padj   pct_in    pct_out
1 0.0960505617 0.1578387500 0.624025 1.17718080
2 0.3791408832 0.4752158709 0.000000 0.06036825
3 0.0201543483 0.0399747053 6.864275 9.20615756
4 0.0618111058 0.1073170552 0.000000 0.27165711
5 0.0159900126 0.0323571319 3.744150 5.58406278
6 0.0001773787 0.0004981502 2.028081 4.37669786
# we have all the genes for each cluster
dplyr::count(pbmc.genes, group)
# A tibble: 14 x 2
   group     n
   <chr> <int>
 1 0     18791
 2 1     18791
 3 10    18791
 4 11    18791
 5 12    18791
 6 13    18791
 7 2     18791
 8 3     18791
 9 4     18791
10 5     18791
11 6     18791
12 7     18791
13 8     18791
14 9     18791

To do Gene set enrichment analysis, we need to have the annotated gene set first. One popular source is the MsigDB from Broad Institute.

Gene Set Enrichment with fgsea

library(msigdbr)
Loading required package: dplyr
Warning: package 'dplyr' was built under R version 3.5.2

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
Loading required package: tibble
Warning: package 'tibble' was built under R version 3.5.2
library(fgsea)
library(dplyr)
library(ggplot2)

msigdbr_show_species()
 [1] "Bos taurus"               "Caenorhabditis elegans"  
 [3] "Canis lupus familiaris"   "Danio rerio"             
 [5] "Drosophila melanogaster"  "Gallus gallus"           
 [7] "Homo sapiens"             "Mus musculus"            
 [9] "Rattus norvegicus"        "Saccharomyces cerevisiae"
[11] "Sus scrofa"              
m_df<- msigdbr(species = "Homo sapiens", category = "C7")

head(m_df)
# A tibble: 6 x 9
  gs_name gs_id gs_cat gs_subcat human_gene_symb… species_name entrez_gene
  <chr>   <chr> <chr>  <chr>     <chr>            <chr>              <int>
1 GOLDRA… M3044 C7     ""        ABCA2            Homo sapiens          20
2 GOLDRA… M3044 C7     ""        ABCC5            Homo sapiens       10057
3 GOLDRA… M3044 C7     ""        ABHD14A          Homo sapiens       25864
4 GOLDRA… M3044 C7     ""        ACADM            Homo sapiens          34
5 GOLDRA… M3044 C7     ""        ACP5             Homo sapiens          54
6 GOLDRA… M3044 C7     ""        ACP6             Homo sapiens       51205
# … with 2 more variables: gene_symbol <chr>, sources <chr>
fgsea_sets<- m_df %>% split(x = .$gene_symbol, f = .$gs_name)

fgsea_sets$GSE11057_NAIVE_VS_MEMORY_CD4_TCELL_UP
  [1] "ABLIM1"       "ACER1"        "ACPL2"        "AEBP1"       
  [5] "AGRN"         "AIF1"         "ALG10B"       "AMN1"        
  [9] "ANKRD36BP2"   "APBA2"        "APBB1"        "ARMCX2"      
 [13] "BACH2"        "BEND5"        "BNIP3L"       "BTBD3"       
 [17] "C10orf58"     "C12orf23"     "C1orf145"     "C6orf170"    
 [21] "C6orf48"      "CA6"          "CADPS2"       "CAMK4"       
 [25] "CD248"        "CD55"         "CENPV"        "CEP41"       
 [29] "CHI3L2"       "CHML"         "CHMP7"        "CIAPIN1"     
 [33] "CLCN5"        "COL5A2"       "CRLF3"        "CYHR1"       
 [37] "DDR1"         "DFNB59"       "DNHD1"        "DNTT"        
 [41] "DSC1"         "EDAR"         "EEA1"         "EFNA1"       
 [45] "ENGASE"       "EXPH5"        "FAM113B"      "FAM134B"     
 [49] "FCGRT"        "FLJ13197"     "GAL3ST4"      "GNAI1"       
 [53] "GP5"          "GPR125"       "GPR160"       "GPRASP1"     
 [57] "GPRASP2"      "GPRC5B"       "HEMGN"        "HIPK2"       
 [61] "HSF2"         "IGF1R"        "IGIP"         "ITGA6"       
 [65] "KCNQ5"        "KCTD3"        "KLHDC1"       "KLHL13"      
 [69] "KLHL24"       "KRT18"        "KRT2"         "KRT72"       
 [73] "KRT73"        "LINC00282"    "LMLN"         "LOC100286937"
 [77] "LOC100287237" "LOC100289019" "LOC100507218" "LOC282997"   
 [81] "LOC283887"    "LOC284023"    "LOC346887"    "LOC439949"   
 [85] "LOC440104"    "LOC641518"    "LOC644794"    "LOC646762"   
 [89] "LOC646808"    "LPHN1"        "LRRN3"        "MALL"        
 [93] "MAML2"        "MANSC1"       "ME3"          "MEF2A"       
 [97] "MEST"         "METAP1D"      "MIR101-1"     "MIR600HG"    
[101] "MLXIP"        "MPP1"         "MPP7"         "MRPL45P2"    
[105] "MYB"          "MZF1"         "NAA16"        "NBEA"        
[109] "NDFIP1"       "NET1"         "NPAS2"        "NPM3"        
[113] "NSUN5"        "NUCB2"        "NUDT12"       "NUDT17"      
[117] "PADI4"        "PCSK5"        "PDE3B"        "PDE7A"       
[121] "PDE7B"        "PDE9A"        "PDK1"         "PECAM1"      
[125] "PHGDH"        "PIGL"         "PIK3CD"       "PIK3IP1"     
[129] "PITPNM2"      "PKIG"         "PLA2G12A"     "PLAG1"       
[133] "PLLP"         "PRRT1"        "PTPRK"        "RAPGEF6"     
[137] "REG4"         "RGS10"        "RHPN2"        "RIN3"        
[141] "RNF175"       "ROBO3"        "SATB1"        "SCAI"        
[145] "SCARB1"       "SCML1"        "SCML2"        "SERPINE2"    
[149] "SERTAD2"      "SERTM1"       "SETD1B"       "SFMBT2"      
[153] "SFXN2"        "SGK223"       "SH3RF3"       "SIAH1"       
[157] "SLC11A2"      "SLC25A37"     "SLC29A2"      "SLC2A11"     
[161] "SMPD1"        "SNORD104"     "SNPH"         "SNRPN"       
[165] "SNX9"         "SORCS3"       "SPPL2B"       "SREBF1"      
[169] "STAP1"        "STK17A"       "TAF4B"        "TARBP1"      
[173] "TGFBR2"       "TIMP2"        "TLE2"         "TMEM170B"    
[177] "TMEM220"      "TMEM41B"      "TMEM48"       "TMIGD2"      
[181] "TOM1L2"       "TSPAN3"       "TTC28"        "TUG1"        
[185] "UBE2E2"       "USP44"        "VPS52"        "ZC4H2"       
[189] "ZMYND8"       "ZNF182"       "ZNF229"       "ZNF238"      
[193] "ZNF496"       "ZNF506"       "ZNF516"       "ZNF546"      
[197] "ZNF563"       "ZNF662"       "ZNF780B"      "ZNRD1-AS1"

The fgsea() function requires a list of gene sets to check, and a named vector of gene-level statistics, where the names should be the same as the gene names in the pathways list. First, let’s create our named vector of test statistics. See ?tibble::deframe for help here - deframe() converts two-column data frames to a named vector or list, using the first column as name and the second column as value. I copied some code from https://stephenturner.github.io/deseq-to-fgsea/

# Naive CD4+ T cells
pbmc.genes %>%
  dplyr::filter(group == "0") %>%
  arrange(desc(logFC), desc(auc)) %>%
  head(n = 10)
   feature group  avgExpr     logFC statistic       auc          pval
1     LDHB     0 2.237079 1.1313172   3720734 0.8760304  0.000000e+00
2     TRAC     0 1.998718 1.1093173   3280268 0.7723246 2.917922e-193
3     CD3E     0 1.875625 1.0529526   3311352 0.7796431 9.625101e-204
4     TCF7     0 1.587238 1.0255404   3586950 0.8445316  0.000000e+00
5      LTB     0 2.393230 1.0036746   3028868 0.7131337 2.244504e-113
6     CD3D     0 1.730219 0.9411404   3208290 0.7553776 6.267795e-172
7     CCR7     0 1.180417 0.9300371   3603946 0.8485333  0.000000e+00
8     IL7R     0 1.649311 0.8914322   3104348 0.7309051 3.329368e-148
9    SARAF     0 2.226600 0.8481803   3713555 0.8743401  0.000000e+00
10 TRABD2A     0 1.043007 0.8444002   3548526 0.8354848  0.000000e+00
            padj   pct_in  pct_out
1   0.000000e+00 98.75195 74.97736
2  5.030337e-191 97.42590 45.27618
3  1.826922e-201 97.97192 44.97434
4   0.000000e+00 94.69579 44.00845
5  1.794743e-111 98.75195 65.34863
6  9.273869e-170 96.80187 42.92182
7   0.000000e+00 85.56942 22.69846
8  3.959630e-146 88.92356 35.79837
9   0.000000e+00 99.14197 89.37519
10  0.000000e+00 80.18721 19.52913
# we feel assured to see IL7R and CCR7 which are marker genes for naive CD4+ T cells
  
# select only the feature and auc columns for fgsea, which statistics to use is an open question
cluster0.genes<- pbmc.genes %>%
  dplyr::filter(group == "0") %>%
  arrange(desc(auc)) %>% 
  dplyr::select(feature, auc)


ranks<- deframe(cluster0.genes)

head(ranks)
    RPL30     RPS3A     RPS13    RPL35A     RPL32     RPL11 
0.9372499 0.9352810 0.9240511 0.9192642 0.9176527 0.9133020 
fgseaRes<- fgsea(fgsea_sets, stats = ranks, nperm = 1000)
Warning in fgsea(fgsea_sets, stats = ranks, nperm = 1000): There are ties in the preranked stats (21% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.

tidy the data a bit

fgseaResTidy <- fgseaRes %>%
  as_tibble() %>%
  arrange(desc(NES))


fgseaResTidy %>% 
  dplyr::select(-leadingEdge, -ES, -nMoreExtreme) %>% 
  arrange(padj) %>% 
  head()
# A tibble: 6 x 5
  pathway                                         pval    padj   NES  size
  <chr>                                          <dbl>   <dbl> <dbl> <int>
1 GSE10325_CD4_TCELL_VS_MYELOID_UP             0.00121 0.00699 10.0    190
2 GSE10325_LUPUS_CD4_TCELL_VS_LUPUS_MYELOID_UP 0.00121 0.00699  9.88   188
3 GSE22886_NAIVE_TCELL_VS_MONOCYTE_UP          0.00122 0.00699  9.39   184
4 GSE22886_NAIVE_CD4_TCELL_VS_MONOCYTE_UP      0.00121 0.00699  9.26   188
5 GSE22886_NAIVE_TCELL_VS_DC_UP                0.00122 0.00699  8.81   183
6 GSE22886_NAIVE_TCELL_VS_NKCELL_UP            0.00126 0.00699  8.34   176

plot a barplot for with the normalized Enrichment score

# only plot the top 20 pathways
ggplot(fgseaResTidy %>% filter(padj < 0.008) %>% head(n= 20), aes(reorder(pathway, NES), NES)) +
  geom_col(aes(fill= NES < 7.5)) +
  coord_flip() +
  labs(x="Pathway", y="Normalized Enrichment Score",
       title="Hallmark pathways NES from GSEA") + 
  theme_minimal()

GSEA style plot

plotEnrichment(fgsea_sets[["GSE10325_CD4_TCELL_VS_MYELOID_UP"]],
               ranks) + labs(title="GSE10325 CD4 TCELL VS MYELOID UP")

How to read the figure ?

The X-axis is all your genes in the expriment (~ 20,000 in this case) pre-ranked by your metric. each black bar is the gene in this gene set(pathway). You have an idea where are the genes located in the pre-ranked list.

Enrichement Score (ES) is calcuated by some metric that ES is positive if the gene set is located in the top of the pre-ranked gene list. ES is negative if the gene set is located in the bottom of the pre-ranked gene list.

More readings and other tools to try


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.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] ggplot2_3.1.0 fgsea_1.8.0   msigdbr_6.2.1 tibble_2.0.1  dplyr_0.8.0.1
[6] presto_1.0.0  Rcpp_1.0.0    Seurat_3.0.2 

loaded via a namespace (and not attached):
 [1] tsne_0.1-3          nlme_3.1-137        bitops_1.0-6       
 [4] fs_1.2.6            RColorBrewer_1.1-2  httr_1.4.0         
 [7] rprojroot_1.3-2     sctransform_0.2.0   tools_3.5.1        
[10] backports_1.1.3     utf8_1.1.4          R6_2.3.0           
[13] irlba_2.3.2         KernSmooth_2.23-15  lazyeval_0.2.1     
[16] colorspace_1.4-1    withr_2.1.2         npsurv_0.4-0       
[19] tidyselect_0.2.5    gridExtra_2.3       compiler_3.5.1     
[22] git2r_0.23.0        cli_1.0.1           plotly_4.8.0       
[25] labeling_0.3        caTools_1.17.1.1    scales_1.0.0       
[28] lmtest_0.9-36       ggridges_0.5.1      pbapply_1.3-4      
[31] stringr_1.3.1       digest_0.6.18       rmarkdown_1.11     
[34] R.utils_2.7.0       pkgconfig_2.0.2     htmltools_0.3.6    
[37] bibtex_0.4.2        htmlwidgets_1.3     rlang_0.3.1        
[40] zoo_1.8-4           jsonlite_1.6        BiocParallel_1.16.2
[43] ica_1.0-2           gtools_3.8.1        R.oo_1.22.0        
[46] magrittr_1.5        Matrix_1.2-15       fansi_0.4.0        
[49] munsell_0.5.0       ape_5.2             reticulate_1.10    
[52] R.methodsS3_1.7.1   stringi_1.2.4       yaml_2.2.0         
[55] gbRd_0.4-11         MASS_7.3-51.1       gplots_3.0.1       
[58] Rtsne_0.15          plyr_1.8.4          grid_3.5.1         
[61] parallel_3.5.1      gdata_2.18.0        listenv_0.7.0      
[64] ggrepel_0.8.0       crayon_1.3.4        lattice_0.20-38    
[67] cowplot_0.9.3       splines_3.5.1       SDMTools_1.1-221   
[70] knitr_1.21          pillar_1.3.1        igraph_1.2.2       
[73] future.apply_1.0.1  reshape2_1.4.3      codetools_0.2-16   
[76] fastmatch_1.1-0     glue_1.3.0          evaluate_0.12      
[79] lsei_1.2-0          metap_1.0           data.table_1.11.8  
[82] png_0.1-7           Rdpack_0.10-1       gtable_0.2.0       
[85] RANN_2.6            purrr_0.2.5         tidyr_0.8.2        
[88] future_1.10.0       assertthat_0.2.0    xfun_0.4           
[91] rsvd_1.0.0          survival_2.43-3     viridisLite_0.3.0  
[94] workflowr_1.4.0     cluster_2.0.7-1     globals_0.12.4     
[97] fitdistrplus_1.0-11 ROCR_1.0-7