Analysis of TCGA RPPA LIHC samples

Last updated: 2021-07-28

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Knit directory: Multispectral HCC/

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File	Version	Author	Date	Message
Rmd	dcab1bf	Jovan Tanevski	2021-07-28	set figure output to svg
html	33e71a0	Jovan Tanevski	2021-07-28	Build site.
Rmd	bb998b8	Jovan Tanevski	2021-07-28	hclust with factoextra
html	1cb80af	Jovan Tanevski	2021-07-28	Build site.
Rmd	d0b6e0f	Jovan Tanevski	2021-07-28	add pca to tcga analysis
html	d73f586	Jovan Tanevski	2021-07-28	Build site.
Rmd	c4324f5	Jovan Tanevski	2021-07-28	add umap
html	0bfae68	Jovan Tanevski	2021-07-28	Build site.
Rmd	82e3712	Jovan Tanevski	2021-07-28	add basic hclust

Setup

Load required libraries.

library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.3     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   2.0.0     ✓ forcats 0.5.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(skimr)
library(uwot)

Loading required package: Matrix


Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

library(factoextra)

Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

Read filtered TCGA RRPA data and display summary statistics.

tcga <- read_csv("data/TCGA-RPPA-LIHC_selected.csv", col_types = cols()) %>%
  select(-TumorType) %>%
  column_to_rownames("SampleID")
skim(tcga)

Data summary
Name	tcga
Number of rows	184
Number of columns	12
_______________________
Column type frequency:
numeric	12
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
P53	1	-1.13	0.46	-2.54	-1.40	-0.96	-0.76	-0.51	▁▂▂▅▇
AKT_pS473	1	-0.63	0.71	-2.76	-1.02	-0.41	-0.15	0.96	▁▂▃▇▁
AKT_pT308	1	0.07	0.42	-1.16	-0.07	0.09	0.22	1.72	▁▃▇▁▁
BETACATENIN	1	1.50	0.72	-1.30	1.08	1.51	1.87	3.70	▁▁▇▅▁
JNK_pT183Y185	1	-0.21	0.29	-1.14	-0.36	-0.17	-0.04	0.48	▁▂▇▇▂
MEK1_pS217S221	1	-0.17	0.36	-0.82	-0.35	-0.23	-0.10	2.58	▇▃▁▁▁
P38_pT180Y182	1	0.49	0.57	-1.45	0.31	0.50	0.72	2.95	▁▂▇▁▁
P70S6K_pT389	1	-1.21	0.75	-3.22	-1.58	-0.95	-0.65	1.09	▂▃▇▃▁
PDK1_pS241	1	0.40	0.31	-0.56	0.23	0.37	0.55	1.62	▁▇▇▂▁
S6_pS235S236	1	-0.72	0.75	-3.53	-1.06	-0.67	-0.36	1.12	▁▁▅▇▂
YAP_pS127	1	2.21	0.61	0.68	1.78	2.10	2.54	4.05	▁▇▇▂▁
TRANSGLUTAMINASE	1	-0.42	0.56	-1.24	-0.83	-0.59	-0.20	2.72	▇▃▁▁▁

Unsupervised analysis

Perform hierarchical clustering of the data and plot the resulting dendrogram

tcga.hclust <- eclust(tcga, "hclust", k = 4)

Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.

fviz_dend(tcga.hclust, rect = TRUE)

Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.

fviz_silhouette(tcga.hclust) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

  cluster size ave.sil.width
1       1   72          0.05
2       2   15          0.23
3       3   29         -0.04
4       4   68          0.25

Project to 2D with PCA and UMAP and plot

tcga.pca <- prcomp(tcga)
summary(tcga.pca)

Importance of components:
                          PC1    PC2    PC3     PC4     PC5     PC6     PC7
Standard deviation     1.2123 0.8248 0.6607 0.56514 0.52805 0.47568 0.43431
Proportion of Variance 0.3831 0.1773 0.1138 0.08325 0.07268 0.05898 0.04917
Cumulative Proportion  0.3831 0.5604 0.6742 0.75746 0.83014 0.88912 0.93829
                           PC8     PC9   PC10    PC11    PC12
Standard deviation     0.31055 0.24392 0.2216 0.14622 0.10156
Proportion of Variance 0.02514 0.01551 0.0128 0.00557 0.00269
Cumulative Proportion  0.96343 0.97894 0.9917 0.99731 1.00000

fviz_pca_biplot(tcga.pca, label = "var", col.ind = as.factor(tcga.hclust$cluster)) +
  theme_classic()

tcga.umap <- umap(tcga, n_neighbors = 5, n_epochs = 1000) %>%
  cbind(tcga.hclust$cluster) %>%
  `colnames<-`(c("U1", "U2", "Cluster")) %>%
  as_tibble() %>%
  mutate_at("Cluster", as.factor)

ggplot(tcga.umap, aes(x = U1, y = U2, color = Cluster, shape = Cluster)) +
  geom_point() +
  theme_classic()

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] factoextra_1.0.7 uwot_0.1.10      Matrix_1.3-4     skimr_2.1.3     
 [5] forcats_0.5.1    stringr_1.4.0    dplyr_1.0.7      purrr_0.3.4     
 [9] readr_2.0.0      tidyr_1.1.3      tibble_3.1.3     ggplot2_3.3.5   
[13] tidyverse_1.3.1  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0          lubridate_1.7.10  bit64_4.0.5       httr_1.4.2       
 [5] rprojroot_2.0.2   repr_1.1.3        tools_4.1.0       backports_1.2.1  
 [9] bslib_0.2.5.1     utf8_1.2.2        R6_2.5.0          DBI_1.1.1        
[13] colorspace_2.0-2  withr_2.4.2       tidyselect_1.1.1  gridExtra_2.3    
[17] curl_4.3.2        bit_4.0.4         compiler_4.1.0    git2r_0.28.0     
[21] cli_3.0.1         rvest_1.0.0       xml2_1.3.2        labeling_0.4.2   
[25] sass_0.4.0        scales_1.1.1      digest_0.6.27     foreign_0.8-81   
[29] rmarkdown_2.9     rio_0.5.27        base64enc_0.1-3   pkgconfig_2.0.3  
[33] htmltools_0.5.1.1 dbplyr_2.1.1      highr_0.9         rlang_0.4.11     
[37] readxl_1.3.1      rstudioapi_0.13   FNN_1.1.3         farver_2.1.0     
[41] jquerylib_0.1.4   generics_0.1.0    jsonlite_1.7.2    vroom_1.5.3      
[45] zip_2.2.0         dendextend_1.15.1 car_3.0-11        magrittr_2.0.1   
[49] Rcpp_1.0.7        munsell_0.5.0     fansi_0.5.0       abind_1.4-5      
[53] viridis_0.6.1     lifecycle_1.0.0   stringi_1.7.3     whisker_0.4      
[57] yaml_2.2.1        carData_3.0-4     grid_4.1.0        parallel_4.1.0   
[61] promises_1.2.0.1  ggrepel_0.9.1     crayon_1.4.1      lattice_0.20-44  
[65] haven_2.4.1       hms_1.1.0         knitr_1.33        pillar_1.6.1     
[69] ggpubr_0.4.0      ggsignif_0.6.2    reprex_2.0.0      glue_1.4.2       
[73] evaluate_0.14     data.table_1.14.0 modelr_0.1.8      vctrs_0.3.8      
[77] tzdb_0.1.2        httpuv_1.6.1      cellranger_1.1.0  gtable_0.3.0     
[81] assertthat_0.2.1  openxlsx_4.2.4    xfun_0.24         broom_0.7.8      
[85] RSpectra_0.16-0   rstatix_0.7.0     later_1.2.0       viridisLite_0.4.0
[89] cluster_2.1.2     ellipsis_0.3.2

Analysis of TCGA RPPA LIHC samples

Jovan Tanevski

2021-07-28

Setup

Unsupervised analysis