Analysis of TCGA RPPA LIHC samples

Last updated: 2021-11-11

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Knit directory: Multispectral HCC/

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html	515f7e6	Jovan Tanevski	2021-10-28	Build site.
Rmd	182f85e	Jovan Tanevski	2021-10-28	duplicate tcga

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[["3"]]

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	AveExpr	F
MAPK_pT202Y204	-0.5178925	0.2500126	-0.2086544	-0.0112531	31.00327
STAT3_pY705	-0.3933026	0.2281801	-0.2135669	-0.0112531	24.36117
PKCALPHA	-0.2718865	-0.1924176	0.2849000	-0.0112531	23.64137
PKCALPHA_pS657	-0.5129190	-0.1103646	0.2442252	-0.0112531	23.05584
P38_pT180Y182	-0.5967727	0.1659339	-0.0822090	-0.0112719	21.54107
JNK_pT183Y185	-0.1161683	0.2132073	-0.2668280	-0.0112531	21.19132
PKCDELTA_pS664	-0.5337640	0.1687629	-0.1021265	-0.0112531	19.32253
BETACATENIN	-0.2041675	-0.1799670	0.2515834	-0.0112531	18.42928
JNK2	-0.2369005	-0.1708064	0.2485788	-0.0112448	18.13879
P38MAPK	-0.3746121	-0.1156606	0.2149019	-0.0112531	15.94046

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

tests.nmf@.Data

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

summary(tests.nmf)

       Cluster1 Cluster2 Cluster3
Down          9        6        6
NotSig        2        1        2
Up            2        6        5

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   19          0.07
2       2   92          0.24
3       3   73          0.07

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
MAPK_pT202Y204	0.0000000	2	0.6103957	0.0000000
STAT3_pY705	0.0000000	2	0.5884322	0.0000000
PKCALPHA	0.0000000	3	0.5560225	0.0000000
JNK_pT183Y185	0.0000000	2	0.5179596	0.0000000
BETACATENIN	0.0000000	3	0.4836079	0.0000000
PKCDELTA_pS664	0.0000000	2	0.4526043	0.0000000
JNK_pT183Y185	0.0000000	3	-0.4927967	0.0000000
JNK2	0.0000000	3	0.5316402	0.0000000
P38MAPK	0.0000000	3	0.4500168	0.0000000
MAPK_pT202Y204	0.0000000	3	-0.4295631	0.0000000
S6_pS235S236	0.0000000	3	-0.4651793	0.0000000
P38_pT180Y182	0.0000000	1	-0.7831030	0.0000000
PKCALPHA_pS657	0.0000000	3	0.4532088	0.0000001
STAT3_pY705	0.0000001	3	-0.4079326	0.0000002
BETACATENIN	0.0000001	2	-0.3795628	0.0000003
P38_pT180Y182	0.0000001	2	0.4135443	0.0000003
STAT3_pY705	0.0000003	1	-0.6238685	0.0000007
PKCDELTA_pS664	0.0000004	1	-0.6362928	0.0000008
PKCALPHA	0.0000004	2	-0.3849414	0.0000008
S6_pS235S236	0.0000013	2	0.3576065	0.0000025
MAPK_pT202Y204	0.0000015	1	-0.6309006	0.0000027
JNK2	0.0000043	2	-0.3636325	0.0000077
TRANSGLUTAMINASE	0.0000075	1	0.5521899	0.0000127
PKCALPHA_pS657	0.0000386	1	-0.6176111	0.0000627
PKCDELTA_pS664	0.0001984	3	-0.2521342	0.0003094
P38MAPK	0.0024458	2	-0.2314684	0.0036687
P38MAPK	0.0035793	1	-0.4894797	0.0051700
TRANSGLUTAMINASE	0.0097212	2	-0.1864079	0.0135402
PKCALPHA_pS657	0.0169716	2	-0.1889002	0.0228239
PKCALPHA	0.0292345	1	-0.3257747	0.0367788
CKIT	0.0290147	3	-0.1749210	0.0367788
JNK2	0.0400623	1	-0.3239765	0.0488260

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