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Tutorial

Load the dataset

A subset of invasive breast carcinoma data from primary tumor tissue. See ?tcga for more information on loading the full dataset or metadata.

library(tcgaViz)
library(ggplot2)
data(tcga)
head(tcga$genes)
#> # A tibble: 6 x 2
#>   sample           ICOS
#>   <chr>           <dbl>
#> 1 TCGA-3C-AAAU-01  1.25
#> 2 TCGA-A2-A04Q-01  7.79
#> 3 TCGA-A2-A0T4-01  4.97
#> 4 TCGA-A8-A08S-01  3.69
#> 5 TCGA-A8-A09B-01  2.55
#> 6 TCGA-A8-A0AD-01  3.72
head(tcga$cells$Cibersort_ABS)
#> # A tibble: 6 x 24
#>   sample          study B_cell_naive B_cell_memory B_cell_plasma T_cell_CD8.
#>   <chr>           <fct>        <dbl>         <dbl>         <dbl>       <dbl>
#> 1 TCGA-3C-AAAU-01 BRCA      0             0.0221         0.0192       0.0129
#> 2 TCGA-A2-A04Q-01 BRCA      0.0274        0.0249         0.0236       0.118 
#> 3 TCGA-A2-A0T4-01 BRCA      0.0167        0              0.0159       0.0432
#> 4 TCGA-A8-A08S-01 BRCA      0             0.00425        0            0.0217
#> 5 TCGA-A8-A09B-01 BRCA      0.0146        0              0.00612      0.0256
#> 6 TCGA-A8-A0AD-01 BRCA      0.000919      0.000797       0.00290      0     
#> # … with 18 more variables: T_cell_CD4._naive <dbl>,
#> #   T_cell_CD4._memory_resting <dbl>, T_cell_CD4._memory_activated <dbl>,
#> #   T_cell_follicular_helper <dbl>, T_cell_regulatory_.Tregs. <dbl>,
#> #   T_cell_gamma_delta <dbl>, NK_cell_resting <dbl>, NK_cell_activated <dbl>,
#> #   Monocyte <dbl>, Macrophage_M0 <dbl>, Macrophage_M1 <dbl>,
#> #   Macrophage_M2 <dbl>, Myeloid_dendritic_cell_resting <dbl>,
#> #   Myeloid_dendritic_cell_activated <dbl>, Mast_cell_activated <dbl>,
#> #   Mast_cell_resting <dbl>, Eosinophil <dbl>, Neutrophil <dbl>

Violin plot of cell subtypes

And perform a significance of a Wilcoxon adjusted test according to the expression level (high or low) of a selected gene.

df <- convert2biodata(
  algorithm = "Cibersort_ABS",
  disease = "breast invasive carcinoma",
  tissue = "Primary Tumor",
  gene_x = "ICOS"
)
(stats <- calculate_pvalue(df))
#> Breast Invasive Carcinoma (BRCA; Primary Tumor)
#> Wilcoxon-Mann-Whitney test with Benjamini & Hochberg correction (n_low = 16; n_high = 14).
#> # A tibble: 6 x 9
#>   `Cell type`                `Average(High)` `Average(Low)` `SD(High)` `SD(Low)`
#>   <fct>                                <dbl>          <dbl>      <dbl>     <dbl>
#> 1 Macrophage_M1                      0.0454        0.00943      0.0328   0.0116 
#> 2 Macrophage_M2                      0.109         0.0697       0.0321   0.0368 
#> 3 T_cell_CD4._memory_resting         0.0504        0.0122       0.0377   0.0124 
#> 4 T_cell_CD8.                        0.0498        0.0127       0.0387   0.00934
#> 5 T_cell_follicular_helper           0.0352        0.0119       0.0259   0.00691
#> 6 T_cell_gamma_delta                 0.00823       0.000956     0.0101   0.00258
#> # … with 4 more variables: Average(High - Low) <dbl>, P-value <dbl>,
#> #   P-value adjusted <dbl>, Significance <chr>
plot(df, stats = stats)

Advanced parameters

With ggplot2::theme() expressions.

(df <- convert2biodata(
  algorithm = "Cibersort_ABS",
  disease = "breast invasive carcinoma",
  tissue = "Primary Tumor",
  gene_x = "ICOS",
  stat = "quantile"
))
#> # A tibble: 352 x 3
#>    high  cell_type      value
#>  * <fct> <fct>          <dbl>
#>  1 25%   B_cell_naive 0.00001
#>  2 75%   B_cell_naive 0.0274 
#>  3 25%   B_cell_naive 0.0146 
#>  4 75%   B_cell_naive 0.0112 
#>  5 25%   B_cell_naive 0.0141 
#>  6 25%   B_cell_naive 0.00546
#>  7 75%   B_cell_naive 0.0289 
#>  8 75%   B_cell_naive 0.00376
#>  9 25%   B_cell_naive 0.00001
#> 10 75%   B_cell_naive 0.00118
#> # … with 342 more rows
(stats <- calculate_pvalue(
  df,
  method_test = "t_test",
  method_adjust = "bonferroni",
  p_threshold = 0.01
))
#> Breast Invasive Carcinoma (BRCA; Primary Tumor)
#> Student's t-test with bonferroni correction (n_low = 8; n_high = 8).
#> # A tibble: 1 x 9
#>   `Cell type` `Average(75%)` `Average(25%)` `SD(75%)` `SD(25%)` `Average(75% - …
#>   <fct>                <dbl>          <dbl>     <dbl>     <dbl>            <dbl>
#> 1 Macrophage…          0.117         0.0456    0.0274    0.0216           0.0719
#> # … with 3 more variables: P-value <dbl>, P-value adjusted <dbl>,
#> #   Significance <chr>
plot(
  df,
  stats = stats,
  type = "boxplot",
  dots = TRUE,
  xlab = "Expression level of the 'ICOS' gene by cell type",
  ylab = "Percent of relative abundance\n(from the Cibersort_ABS algorithm)",
  title = toupper("Differential analysis of immune cell type abundance
    based on RNASeq gene-level expression from The Cancer Genome Atlas"),
  axis.text.y = element_text(size = 8, hjust = 0.5),
  plot.title =  element_text(face = "bold", hjust = 0.5),
  plot.subtitle =  element_text(size = , face = "italic", hjust = 0.5),
  draw = FALSE
) + labs(
  subtitle = paste("Breast Invasive Carcinoma (BRCA; Primary Tumor):",
                   "Student's t-test with Bonferroni (P < 0.01)")
)

Session information

#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggplot2_3.3.3 tcgaViz_1.0.2
#> 
#> loaded via a namespace (and not attached):
#>  [1] fs_1.5.0            usethis_2.0.1       httr_1.4.2         
#>  [4] rprojroot_2.0.2     tools_4.0.5         backports_1.2.1    
#>  [7] bslib_0.2.5         utf8_1.2.1          R6_2.5.0           
#> [10] DT_0.18             lazyeval_0.2.2      colorspace_2.0-1   
#> [13] withr_2.4.2         tidyselect_1.1.1    curl_4.3.1         
#> [16] compiler_4.0.5      cli_2.5.0           xml2_1.3.2         
#> [19] shinyjs_2.0.0       desc_1.3.0          plotly_4.9.3       
#> [22] sass_0.4.0          scales_1.1.1        readr_1.4.0        
#> [25] stringr_1.4.0       digest_0.6.27       shinyFeedback_0.3.0
#> [28] foreign_0.8-81      rmarkdown_2.8       rio_0.5.26         
#> [31] pkgconfig_2.0.3     htmltools_0.5.1.1   attempt_0.3.1      
#> [34] highr_0.9           fastmap_1.1.0       htmlwidgets_1.5.3  
#> [37] rlang_0.4.11        readxl_1.3.1        rstudioapi_0.13    
#> [40] shiny_1.6.0         farver_2.1.0        jquerylib_0.1.4    
#> [43] generics_0.1.0      jsonlite_1.7.2      dplyr_1.0.6        
#> [46] zip_2.1.1           car_3.0-10          config_0.3.1       
#> [49] magrittr_2.0.1      Rcpp_1.0.6          munsell_0.5.0      
#> [52] fansi_0.4.2         abind_1.4-5         lifecycle_1.0.0    
#> [55] stringi_1.6.1       yaml_2.2.1          carData_3.0-4      
#> [58] plyr_1.8.6          grid_4.0.5          promises_1.2.0.1   
#> [61] forcats_0.5.1       crayon_1.4.1        haven_2.4.1        
#> [64] hms_1.0.0           knitr_1.33          pillar_1.6.1       
#> [67] ggpubr_0.4.0        ggsignif_0.6.1      reshape2_1.4.4     
#> [70] pkgload_1.2.1       glue_1.4.2          evaluate_0.14      
#> [73] golem_0.3.1         data.table_1.14.0   remotes_2.3.0      
#> [76] vctrs_0.3.8         httpuv_1.6.1        testthat_3.0.2     
#> [79] cellranger_1.1.0    gtable_0.3.0        purrr_0.3.4        
#> [82] tidyr_1.1.3         xfun_0.23           openxlsx_4.2.3     
#> [85] mime_0.10           xtable_1.8-4        broom_0.7.6        
#> [88] roxygen2_7.1.1      rstatix_0.7.0       later_1.2.0        
#> [91] viridisLite_0.4.0   dockerfiler_0.1.3   tibble_3.1.2       
#> [94] ellipsis_0.3.2

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.