Analysis of TCGA RPPA LIHC samples

Last updated: 2021-11-11

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Knit directory: Multispectral HCC/

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Setup

Load required libraries.

library(tidyverse)
library(skimr)
library(uwot)
library(factoextra)
library(cowplot)
library(limma)
library(NMF)
library(pheatmap)

Read filtered TCGA RRPA data and display summary statistics.

tcga.lihc <- read_tsv("data/TCGA-RPPA-pancan-clean.txt") %>%
  filter(TumorType == "LIHC")

Rows: 7790 Columns: 200

── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr   (2): SampleID, TumorType
dbl (198): X1433EPSILON, X4EBP1, X4EBP1_pS65, X4EBP1_pT37T46, X53BP1, ACC_pS...


ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

tcga.raw <- tcga.lihc %>%
  select(
    SampleID, BETACATENIN, CKIT, JNK_pT183Y185, JNK2, MAPK_pT202Y204,
    P38MAPK, P38_pT180Y182, PKCALPHA, PKCALPHA_pS657, PKCDELTA_pS664,
    S6_pS235S236, STAT3_pY705, TRANSGLUTAMINASE
  ) %>%
  column_to_rownames("SampleID")

skim(tcga.raw)

Data summary
Name	tcga.raw
Number of rows	184
Number of columns	13
_______________________
Column type frequency:
numeric	13
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
BETACATENIN	1	1.50	0.72	-1.30	1.08	1.51	1.87	3.70	▁▁▇▅▁
CKIT	1	0.12	0.39	-0.80	-0.06	0.15	0.31	2.75	▂▇▁▁▁
JNK_pT183Y185	1	-0.21	0.29	-1.14	-0.36	-0.17	-0.04	0.48	▁▂▇▇▂
JNK2	1	0.19	0.32	-1.52	0.03	0.15	0.30	1.48	▁▁▇▃▁
MAPK_pT202Y204	1	-0.22	0.74	-2.03	-0.62	-0.06	0.25	4.12	▃▇▁▁▁
P38MAPK	1	0.11	0.29	-0.88	-0.08	0.07	0.27	0.96	▁▂▇▃▁
P38_pT180Y182	1	0.49	0.57	-1.45	0.31	0.50	0.72	2.95	▁▂▇▁▁
PKCALPHA	1	-0.30	0.40	-1.36	-0.59	-0.36	-0.02	0.82	▁▆▇▅▁
PKCALPHA_pS657	1	-0.46	0.37	-1.74	-0.62	-0.47	-0.22	0.46	▁▁▇▆▁
PKCDELTA_pS664	1	-0.36	0.15	-0.88	-0.42	-0.33	-0.26	-0.14	▁▁▃▇▇
S6_pS235S236	1	-0.72	0.75	-3.53	-1.06	-0.67	-0.36	1.12	▁▁▅▇▂
STAT3_pY705	1	0.14	0.40	-1.27	-0.05	0.20	0.36	1.20	▁▂▇▇▁
TRANSGLUTAMINASE	1	-0.42	0.56	-1.24	-0.83	-0.59	-0.20	2.72	▇▃▁▁▁

Quantile normalization and rank normalization for NMF as suggested in https://gdac.broadinstitute.org/runs/analyses__2016_01_28/reports/cancer/LIHC/RPPA_Clustering_CNMF/nozzle.html

tcga.norm <- normalizeQuantiles(tcga.raw)

skim(tcga.norm)

Data summary
Name	tcga.norm
Number of rows	184
Number of columns	13
_______________________
Column type frequency:
numeric	13
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
BETACATENIN	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
CKIT	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
JNK_pT183Y185	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
JNK2	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
MAPK_pT202Y204	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
P38MAPK	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
P38_pT180Y182	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
PKCALPHA	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
PKCALPHA_pS657	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
PKCDELTA_pS664	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
S6_pS235S236	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
STAT3_pY705	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁
TRANSGLUTAMINASE	1	-0.01	0.44	-1.47	-0.25	-0.01	0.23	1.74	▁▃▇▂▁

tcga.rank <- mutate_all(tcga.raw, rank)

Dimensionality reduction

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

Importance of components:
                          PC1    PC2    PC3     PC4     PC5     PC6     PC7
Standard deviation     0.8463 0.6793 0.5538 0.46534 0.42315 0.39088 0.35254
Proportion of Variance 0.2797 0.1802 0.1198 0.08456 0.06992 0.05966 0.04853
Cumulative Proportion  0.2797 0.4599 0.5797 0.66423 0.73415 0.79381 0.84235
                           PC8     PC9   PC10    PC11    PC12    PC13
Standard deviation     0.32919 0.30663 0.2716 0.24954 0.23050 0.11040
Proportion of Variance 0.04232 0.03672 0.0288 0.02432 0.02075 0.00476
Cumulative Proportion  0.88466 0.92138 0.9502 0.97449 0.99524 1.00000

set.seed(42)
tcga.umap <- umap(tcga.norm, n_neighbors = 10, n_epochs = 1000)

Consensus NMF clustering

For this analysis 3 clusters were selected based on the consensus cophenetic correlation coefficient.

tcga.nmf.rank <- nmfEstimateRank(as.matrix(t(tcga.rank)), seq(2, 10), nrun = 20, seed = 42)

plot(tcga.nmf.rank)

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

tcga.nmf <- tcga.nmf <- tcga.nmf.rank$fit[["6"]]

Extract basis of NMF (signature of cluster)

basismap(tcga.nmf)

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

Extract coefficients of NMF (soft clustering of samples)

coefmap(tcga.nmf)

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

Check for signs of overfitting

consensusmap(tcga.nmf)

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

Assign clusters

nmf.clusters <- apply(tcga.nmf@fit@H, 2, which.max)

Plot in 2D PCA and UMAP

fviz_pca_ind(tcga.pca, geom = "point", col.ind = as.factor(nmf.clusters)) +
  theme_classic()

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

tcga.umap.clus <-
  tcga.umap %>%
  cbind(nmf.clusters) %>%
  `colnames<-`(c("U1", "U2", "Cluster")) %>%
  as_tibble() %>%
  mutate_at("Cluster", as.factor)

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

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

Expression profiles per cluster

tcga.clustered.nmf <- tcga.norm %>%
  mutate(Cluster = as.factor(nmf.clusters)) %>%
  pivot_longer(names_to = "Marker", values_to = "Norm.value", -Cluster)

profiles <- seq_len(max(nmf.clusters)) %>% map(~
ggplot(
  tcga.clustered.nmf %>% filter(Cluster == .x),
  aes(x = Marker, y = Norm.value, color = Marker)
) +
  stat_summary(fun.data = mean_sdl, show.legend = FALSE) +
  ylim(-3, 3) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)))

plot_grid(plotlist = profiles, labels = paste("Cluster", seq_len(max(nmf.clusters))))

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

Differential expression analysis (limma)

design <- model.matrix(~ 0 + as.factor(nmf.clusters))
colnames(design) <- paste0("Cluster", seq_len(max(nmf.clusters)))

tcga.nmf.limma <- lmFit(t(tcga.norm), design = design)

tcga.nmf.eb <- eBayes(tcga.nmf.limma)

topTable(tcga.nmf.eb)

	Cluster1	Cluster2	Cluster3	Cluster4	Cluster5	Cluster6	AveExpr	F
PKCALPHA_pS657	-0.3730278	-0.2937306	0.3724889	-0.0850154	-0.2231801	-0.6994625	-0.0112531	22.11160
PKCALPHA	-0.1560999	-0.1697372	0.3853539	-0.2080164	-0.1104291	-0.4628339	-0.0112531	18.85941
PKCDELTA_pS664	-0.4300651	-0.4760229	0.0415293	0.2406422	-0.3974601	-0.3695432	-0.0112531	16.82459
STAT3_pY705	-0.2554661	-0.4818671	-0.1820745	0.2073898	0.3576408	-0.3717927	-0.0112531	15.68875
JNK_pT183Y185	-0.3404028	-0.4054327	-0.1714750	0.2661254	-0.2508033	0.3296977	-0.0112531	15.25773
P38_pT180Y182	-0.4345396	-0.4041800	-0.0950428	0.1413069	0.4209966	-0.5078815	-0.0112719	14.17915
MAPK_pT202Y204	-0.5001174	-0.3442448	-0.1283186	0.2725479	-0.1019825	-0.2658334	-0.0112531	13.28206
P38MAPK	-0.1589381	0.3190424	0.0674792	-0.1949200	0.4337038	-0.4089599	-0.0112531	11.38894
S6_pS235S236	0.6378278	-0.3856756	-0.1232475	0.1429577	-0.2631252	0.0083774	-0.0112531	11.04938
BETACATENIN	-0.3392457	0.4892022	0.1698493	-0.1622843	-0.1459475	-0.2320966	-0.0112531	10.44787

tests.nmf <- decideTests(tcga.nmf.eb)

tests.nmf@.Data

                 Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6
BETACATENIN            -1        1        1       -1        0        0
CKIT                    0        0        0        0        0        1
JNK_pT183Y185          -1       -1       -1        1       -1        1
JNK2                   -1        1        1       -1        0        0
MAPK_pT202Y204         -1       -1       -1        1        0       -1
P38MAPK                 0        1        0       -1        1       -1
P38_pT180Y182          -1       -1        0        1        1       -1
PKCALPHA                0        0        1       -1        0       -1
PKCALPHA_pS657         -1       -1        1       -1       -1       -1
PKCDELTA_pS664         -1       -1        0        1       -1       -1
S6_pS235S236            1       -1       -1        1       -1        0
STAT3_pY705             0       -1       -1        1        1       -1
TRANSGLUTAMINASE        1        0        0       -1        0        1

summary(tests.nmf)

       Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6
Down          7        7        4        6        4        7
NotSig        4        3        5        1        6        3
Up            2        3        4        6        3        3

Differential expression analysis (silhouette)

Calculate the similarity of samples using the expression and the silhouette scores based on the assigned clusters.

silhouette.nmf <- silhouette(nmf.clusters, dist(tcga.norm))
fviz_silhouette(silhouette.nmf)

  cluster size ave.sil.width
1       1    9         -0.01
2       2   15          0.08
3       3   60         -0.01
4       4   72          0.24
5       5   19          0.00
6       6    9          0.01

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

Select only the samples with positive silhouette scores as “core samples”

core.samples <- which(silhouette.nmf[, 3] > 0)
tcga.core.samples <- tcga.norm %>%
  add_column(Cluster = nmf.clusters) %>%
  slice(core.samples)

Calculate difference in means (mean(cluster) - mean(other)), one-vs-all t-test per marker and correct for FDR. Filter q <= 0.05. Plot the differences.

de.table <- unique(tcga.core.samples$Cluster) %>%
  map_dfr(\(c){
    tcga.core.samples %>%
      summarize(across(-Cluster, ~ t.test(.x ~ (Cluster == c))$p.value)) %>%
      pivot_longer(names_to = "Marker", values_to = "p", everything()) %>%
      mutate(Cluster = c, Difference = tcga.core.samples %>%
        group_by(Cluster == c) %>%
        select(-Cluster) %>%
        group_split(.keep = FALSE) %>% map(colMeans) %>% reduce(`-`))
  }) %>%
  mutate(q = p.adjust(p, method = "fdr"), Difference = -Difference)

de.table %>%
  filter(q <= 0.05) %>%
  arrange(q)

Marker	p	Cluster	Difference	q
PKCALPHA	0.0000000	3	0.7701627	0.0000000
PKCALPHA_pS657	0.0000000	3	0.7431880	0.0000000
MAPK_pT202Y204	0.0000000	4	0.5537569	0.0000000
JNK_pT183Y185	0.0000000	4	0.5116234	0.0000000
PKCDELTA_pS664	0.0000000	4	0.4766631	0.0000000
S6_pS235S236	0.0000015	4	0.3604600	0.0000194
STAT3_pY705	0.0000031	4	0.3840488	0.0000342
PKCALPHA	0.0000055	4	-0.3757780	0.0000538
P38MAPK	0.0000079	4	-0.3644667	0.0000686
JNK2	0.0000153	4	-0.2946430	0.0001197
BETACATENIN	0.0000288	2	0.6929014	0.0002045
P38_pT180Y182	0.0000331	4	0.3229844	0.0002150
PKCDELTA_pS664	0.0000401	2	-0.5608850	0.0002404
JNK2	0.0000896	3	0.3296572	0.0004660
STAT3_pY705	0.0000867	3	-0.2972949	0.0004660
P38_pT180Y182	0.0001063	6	-0.6298227	0.0005184
P38_pT180Y182	0.0001293	2	-0.4511885	0.0005931
JNK2	0.0001502	2	0.5217964	0.0006347
BETACATENIN	0.0001546	4	-0.3013091	0.0006347
JNK_pT183Y185	0.0002328	3	-0.3574638	0.0009079
P38MAPK	0.0002997	5	0.6237278	0.0011133
JNK_pT183Y185	0.0004760	1	-0.6043500	0.0016875
JNK_pT183Y185	0.0005527	2	-0.5475734	0.0017962
P38MAPK	0.0005522	2	0.4367812	0.0017962
TRANSGLUTAMINASE	0.0007879	4	-0.2732536	0.0024582
MAPK_pT202Y204	0.0009888	2	-0.4639665	0.0029665
PKCDELTA_pS664	0.0021256	6	-0.4919496	0.0061405
S6_pS235S236	0.0027499	2	-0.4689384	0.0076605
PKCDELTA_pS664	0.0030533	5	-0.5668973	0.0082123
STAT3_pY705	0.0036598	2	-0.6449699	0.0095156
BETACATENIN	0.0041324	3	0.2508449	0.0103977
MAPK_pT202Y204	0.0052705	5	-0.4149137	0.0128469
STAT3_pY705	0.0059010	5	0.5901075	0.0139478
PKCDELTA_pS664	0.0068256	1	-0.6073484	0.0156587
PKCALPHA_pS657	0.0085792	6	-0.8492541	0.0191194
MAPK_pT202Y204	0.0091498	3	-0.2274984	0.0198246
S6_pS235S236	0.0095179	5	-0.4778042	0.0200648
P38_pT180Y182	0.0122621	5	0.3881879	0.0251695
PKCALPHA	0.0225922	6	-0.6475438	0.0451843
TRANSGLUTAMINASE	0.0234684	6	0.4467719	0.0457633
P38MAPK	0.0253916	3	0.2127752	0.0483061

de.table %>%
  pivot_wider(names_from = "Cluster", values_from = "Difference", -c(p, q)) %>%
  column_to_rownames("Marker") %>%
  as.matrix() %>%
  pheatmap(scale = "none")

Version	Author	Date
515f7e6	Jovan Tanevski	2021-10-28

sessionInfo()

R version 4.1.1 (2021-08-10)
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.0.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] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] RColorBrewer_1.1-2  doParallel_1.0.16   iterators_1.0.13   
 [4] foreach_1.5.1       pheatmap_1.0.12     NMF_0.23.0         
 [7] synchronicity_1.3.5 bigmemory_4.5.36    Biobase_2.54.0     
[10] BiocGenerics_0.40.0 cluster_2.1.2       rngtools_1.5.2     
[13] pkgmaker_0.32.2     registry_0.5-1      limma_3.50.0       
[16] cowplot_1.1.1       factoextra_1.0.7    uwot_0.1.10        
[19] Matrix_1.3-4        skimr_2.1.3         forcats_0.5.1      
[22] stringr_1.4.0       dplyr_1.0.7         purrr_0.3.4        
[25] readr_2.0.2         tidyr_1.1.4         tibble_3.1.6       
[28] ggplot2_3.3.5       tidyverse_1.3.1     workflowr_1.6.2    

loaded via a namespace (and not attached):
  [1] bigmemory.sri_0.1.3 colorspace_2.0-2    ggsignif_0.6.3     
  [4] ellipsis_0.3.2      rprojroot_2.0.2     htmlTable_2.3.0    
  [7] base64enc_0.1-3     fs_1.5.0            rstudioapi_0.13    
 [10] ggpubr_0.4.0        farver_2.1.0        ggrepel_0.9.1      
 [13] bit64_4.0.5         RSpectra_0.16-0     fansi_0.5.0        
 [16] lubridate_1.8.0     xml2_1.3.2          splines_4.1.1      
 [19] codetools_0.2-18    knitr_1.36          Formula_1.2-4      
 [22] jsonlite_1.7.2      broom_0.7.10        gridBase_0.4-7     
 [25] dbplyr_2.1.1        png_0.1-7           compiler_4.1.1     
 [28] httr_1.4.2          backports_1.3.0     assertthat_0.2.1   
 [31] fastmap_1.1.0       cli_3.1.0           later_1.3.0        
 [34] htmltools_0.5.2     tools_4.1.1         gtable_0.3.0       
 [37] glue_1.5.0          reshape2_1.4.4      Rcpp_1.0.7         
 [40] carData_3.0-4       cellranger_1.1.0    jquerylib_0.1.4    
 [43] vctrs_0.3.8         xfun_0.28           rvest_1.0.2        
 [46] lifecycle_1.0.1     rstatix_0.7.0       scales_1.1.1       
 [49] vroom_1.5.6         hms_1.1.1           promises_1.2.0.1   
 [52] yaml_2.2.1          gridExtra_2.3       sass_0.4.0         
 [55] rpart_4.1-15        latticeExtra_0.6-29 stringi_1.7.5      
 [58] highr_0.9           checkmate_2.0.0     repr_1.1.3         
 [61] rlang_0.4.12        pkgconfig_2.0.3     evaluate_0.14      
 [64] lattice_0.20-45     htmlwidgets_1.5.4   labeling_0.4.2     
 [67] bit_4.0.4           tidyselect_1.1.1    plyr_1.8.6         
 [70] magrittr_2.0.1      R6_2.5.1            Hmisc_4.6-0        
 [73] generics_0.1.1      DBI_1.1.1           foreign_0.8-81     
 [76] pillar_1.6.4        haven_2.4.3         whisker_0.4        
 [79] withr_2.4.2         nnet_7.3-16         survival_3.2-13    
 [82] abind_1.4-5         modelr_0.1.8        crayon_1.4.2       
 [85] car_3.0-12          uuid_1.0-3          utf8_1.2.2         
 [88] tzdb_0.2.0          rmarkdown_2.11      jpeg_0.1-9         
 [91] grid_4.1.1          readxl_1.3.1        data.table_1.14.2  
 [94] FNN_1.1.3           git2r_0.28.0        reprex_2.0.1       
 [97] digest_0.6.28       xtable_1.8-4        httpuv_1.6.3       
[100] munsell_0.5.0       bslib_0.3.1

Analysis of TCGA RPPA LIHC samples

Jovan Tanevski

2021-11-11

Setup

Dimensionality reduction

Consensus NMF clustering