Analysis of TCGA RPPA LIHC samples
Jovan Tanevski
2021-10-28
Last updated: 2021-10-28
Checks: 7 0
Knit directory: Multispectral HCC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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)| Name | tcga.raw |
| Number of rows | 184 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| BETACATENIN | 0 | 1 | 1.50 | 0.72 | -1.30 | 1.08 | 1.51 | 1.87 | 3.70 | ▁▁▇▅▁ |
| CKIT | 0 | 1 | 0.12 | 0.39 | -0.80 | -0.06 | 0.15 | 0.31 | 2.75 | ▂▇▁▁▁ |
| JNK_pT183Y185 | 0 | 1 | -0.21 | 0.29 | -1.14 | -0.36 | -0.17 | -0.04 | 0.48 | ▁▂▇▇▂ |
| JNK2 | 0 | 1 | 0.19 | 0.32 | -1.52 | 0.03 | 0.15 | 0.30 | 1.48 | ▁▁▇▃▁ |
| MAPK_pT202Y204 | 0 | 1 | -0.22 | 0.74 | -2.03 | -0.62 | -0.06 | 0.25 | 4.12 | ▃▇▁▁▁ |
| P38MAPK | 0 | 1 | 0.11 | 0.29 | -0.88 | -0.08 | 0.07 | 0.27 | 0.96 | ▁▂▇▃▁ |
| P38_pT180Y182 | 0 | 1 | 0.49 | 0.57 | -1.45 | 0.31 | 0.50 | 0.72 | 2.95 | ▁▂▇▁▁ |
| PKCALPHA | 0 | 1 | -0.30 | 0.40 | -1.36 | -0.59 | -0.36 | -0.02 | 0.82 | ▁▆▇▅▁ |
| PKCALPHA_pS657 | 0 | 1 | -0.46 | 0.37 | -1.74 | -0.62 | -0.47 | -0.22 | 0.46 | ▁▁▇▆▁ |
| PKCDELTA_pS664 | 0 | 1 | -0.36 | 0.15 | -0.88 | -0.42 | -0.33 | -0.26 | -0.14 | ▁▁▃▇▇ |
| S6_pS235S236 | 0 | 1 | -0.72 | 0.75 | -3.53 | -1.06 | -0.67 | -0.36 | 1.12 | ▁▁▅▇▂ |
| STAT3_pY705 | 0 | 1 | 0.14 | 0.40 | -1.27 | -0.05 | 0.20 | 0.36 | 1.20 | ▁▂▇▇▁ |
| TRANSGLUTAMINASE | 0 | 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)| Name | tcga.norm |
| Number of rows | 184 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| BETACATENIN | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| CKIT | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| JNK_pT183Y185 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| JNK2 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| MAPK_pT202Y204 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| P38MAPK | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| P38_pT180Y182 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| PKCALPHA | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| PKCALPHA_pS657 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| PKCDELTA_pS664 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| S6_pS235S236 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| STAT3_pY705 | 0 | 1 | -0.01 | 0.44 | -1.47 | -0.25 | -0.01 | 0.23 | 1.74 | ▁▃▇▂▁ |
| TRANSGLUTAMINASE | 0 | 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 1.00000set.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)
tcga.nmf <- tcga.nmf <- tcga.nmf.rank$fit[["3"]]Extract basis of NMF (signature of cluster)
basismap(tcga.nmf)
Extract coefficients of NMF (soft clustering of samples)
coefmap(tcga.nmf)
Check for signs of overfitting
consensusmap(tcga.nmf)
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()
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()
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))))
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 | P.Value | adj.P.Val | |
|---|---|---|---|---|---|---|---|
| MAPK_pT202Y204 | -0.5178925 | 0.2500126 | -0.2086544 | -0.0112531 | 31.00327 | 0 | 0 |
| STAT3_pY705 | -0.3933026 | 0.2281801 | -0.2135669 | -0.0112531 | 24.36117 | 0 | 0 |
| PKCALPHA | -0.2718865 | -0.1924176 | 0.2849000 | -0.0112531 | 23.64137 | 0 | 0 |
| PKCALPHA_pS657 | -0.5129190 | -0.1103646 | 0.2442252 | -0.0112531 | 23.05584 | 0 | 0 |
| P38_pT180Y182 | -0.5967727 | 0.1659339 | -0.0822090 | -0.0112719 | 21.54107 | 0 | 0 |
| JNK_pT183Y185 | -0.1161683 | 0.2132073 | -0.2668280 | -0.0112531 | 21.19132 | 0 | 0 |
| PKCDELTA_pS664 | -0.5337640 | 0.1687629 | -0.1021265 | -0.0112531 | 19.32253 | 0 | 0 |
| BETACATENIN | -0.2041675 | -0.1799670 | 0.2515834 | -0.0112531 | 18.42928 | 0 | 0 |
| JNK2 | -0.2369005 | -0.1708064 | 0.2485788 | -0.0112448 | 18.13879 | 0 | 0 |
| P38MAPK | -0.3746121 | -0.1156606 | 0.2149019 | -0.0112531 | 15.94046 | 0 | 0 |
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 0summary(tests.nmf) Cluster1 Cluster2 Cluster3
Down 9 6 6
NotSig 2 1 2
Up 2 6 5Differential 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
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")
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_2.53.0
[10] BiocGenerics_0.39.2 cluster_2.1.2 rngtools_1.5.2
[13] pkgmaker_0.32.2 registry_0.5-1 limma_3.49.5
[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_3.1.5
[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] rio_0.5.27 ellipsis_0.3.2 rprojroot_2.0.2
[7] htmlTable_2.3.0 base64enc_0.1-3 fs_1.5.0
[10] rstudioapi_0.13 ggpubr_0.4.0 farver_2.1.0
[13] ggrepel_0.9.1 bit64_4.0.5 RSpectra_0.16-0
[16] fansi_0.5.0 lubridate_1.8.0 xml2_1.3.2
[19] splines_4.1.1 codetools_0.2-18 knitr_1.36
[22] Formula_1.2-4 jsonlite_1.7.2 broom_0.7.9
[25] gridBase_0.4-7 dbplyr_2.1.1 png_0.1-7
[28] compiler_4.1.1 httr_1.4.2 backports_1.3.0
[31] assertthat_0.2.1 fastmap_1.1.0 cli_3.0.1
[34] later_1.3.0 htmltools_0.5.2 tools_4.1.1
[37] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
[40] Rcpp_1.0.7 carData_3.0-4 cellranger_1.1.0
[43] jquerylib_0.1.4 vctrs_0.3.8 xfun_0.27
[46] openxlsx_4.2.4 rvest_1.0.2 lifecycle_1.0.1
[49] rstatix_0.7.0 scales_1.1.1 vroom_1.5.5
[52] hms_1.1.1 promises_1.2.0.1 curl_4.3.2
[55] yaml_2.2.1 gridExtra_2.3 sass_0.4.0
[58] rpart_4.1-15 latticeExtra_0.6-29 stringi_1.7.5
[61] highr_0.9 checkmate_2.0.0 zip_2.2.0
[64] repr_1.1.3 rlang_0.4.12 pkgconfig_2.0.3
[67] evaluate_0.14 lattice_0.20-45 htmlwidgets_1.5.4
[70] labeling_0.4.2 bit_4.0.4 tidyselect_1.1.1
[73] plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
[76] Hmisc_4.6-0 generics_0.1.1 DBI_1.1.1
[79] foreign_0.8-81 pillar_1.6.4 haven_2.4.3
[82] whisker_0.4 withr_2.4.2 nnet_7.3-16
[85] survival_3.2-13 abind_1.4-5 modelr_0.1.8
[88] crayon_1.4.1 car_3.0-11 uuid_0.1-4
[91] utf8_1.2.2 tzdb_0.1.2 rmarkdown_2.11
[94] jpeg_0.1-9 grid_4.1.1 readxl_1.3.1
[97] data.table_1.14.2 FNN_1.1.3 git2r_0.28.0
[100] reprex_2.0.1 digest_0.6.28 xtable_1.8-4
[103] httpuv_1.6.3 munsell_0.5.0 bslib_0.3.1