Last updated: 2021-09-16

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Rmd 9c11427 Jovan Tanevski 2021-09-16 fix difference calculation
html 9a18eda Jovan Tanevski 2021-09-16 Build site.
Rmd 17fc915 Jovan Tanevski 2021-09-16 update hclust
Rmd 47928d9 Jovan Tanevski 2021-09-16 add silhouette based differential expression analysis, repeat nmf
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Rmd a152862 Jovan Tanevski 2021-08-02 add NMF based analysis
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Rmd dcab1bf Jovan Tanevski 2021-07-28 set figure output to svg
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Rmd bb998b8 Jovan Tanevski 2021-07-28 hclust with factoextra
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Rmd d0b6e0f Jovan Tanevski 2021-07-28 add pca to tcga analysis
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Rmd c4324f5 Jovan Tanevski 2021-07-28 add umap
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Rmd 82e3712 Jovan Tanevski 2021-07-28 add basic hclust

Setup

Load required libraries.

library(tidyverse)
library(skimr)
library(uwot)
library(factoextra)
library(cowplot)
library(FNN)
library(igraph)
library(leiden)
library(limma)
library(NMF)

Read filtered TCGA RRPA data and display summary statistics.

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

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

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
P53 0 1 -1.13 0.46 -2.54 -1.40 -0.96 -0.76 -0.51 ▁▂▂▅▇
AKT_pS473 0 1 -0.63 0.71 -2.76 -1.02 -0.41 -0.15 0.96 ▁▂▃▇▁
AKT_pT308 0 1 0.07 0.42 -1.16 -0.07 0.09 0.22 1.72 ▁▃▇▁▁
BETACATENIN 0 1 1.50 0.72 -1.30 1.08 1.51 1.87 3.70 ▁▁▇▅▁
JNK_pT183Y185 0 1 -0.21 0.29 -1.14 -0.36 -0.17 -0.04 0.48 ▁▂▇▇▂
MEK1_pS217S221 0 1 -0.17 0.36 -0.82 -0.35 -0.23 -0.10 2.58 ▇▃▁▁▁
P38_pT180Y182 0 1 0.49 0.57 -1.45 0.31 0.50 0.72 2.95 ▁▂▇▁▁
P70S6K_pT389 0 1 -1.21 0.75 -3.22 -1.58 -0.95 -0.65 1.09 ▂▃▇▃▁
PDK1_pS241 0 1 0.40 0.31 -0.56 0.23 0.37 0.55 1.62 ▁▇▇▂▁
S6_pS235S236 0 1 -0.72 0.75 -3.53 -1.06 -0.67 -0.36 1.12 ▁▁▅▇▂
YAP_pS127 0 1 2.21 0.61 0.68 1.78 2.10 2.54 4.05 ▁▇▇▂▁
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)
Data summary
Name tcga.norm
Number of rows 184
Number of columns 12
_______________________
Column type frequency:
numeric 12
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
P53 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
AKT_pS473 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
AKT_pT308 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
BETACATENIN 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
JNK_pT183Y185 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
MEK1_pS217S221 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
P38_pT180Y182 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
P70S6K_pT389 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
PDK1_pS241 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
S6_pS235S236 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
YAP_pS127 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁
TRANSGLUTAMINASE 0 1 0.01 0.52 -1.59 -0.27 0.05 0.3 1.87 ▁▃▇▂▁ 
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     1.052 0.6800 0.5864 0.53531 0.50548 0.47429 0.40905
Proportion of Variance 0.340 0.1421 0.1057 0.08809 0.07855 0.06915 0.05144
Cumulative Proportion  0.340 0.4821 0.5878 0.67594 0.75449 0.82364 0.87508
                           PC8     PC9    PC10    PC11    PC12
Standard deviation     0.35923 0.34052 0.28251 0.23710 0.15913
Proportion of Variance 0.03967 0.03565 0.02454 0.01728 0.00778
Cumulative Proportion  0.91475 0.95040 0.97493 0.99222 1.00000
set.seed(42)
tcga.umap <- umap(tcga.norm, n_neighbors = 10, n_epochs = 1000)

Hierarchical clustering

Perform hierarchical clustering of the data and plot the resulting dendrogram. Number of clusters to use (3) is based on gap statistic.

tcga.hclust <- eclust(tcga.norm, "hclust", 3)
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.
#fviz_gap_stat(tcga.hclust$gap_stat)

fviz_dend(tcga.hclust, rect = TRUE)
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.

Version Author Date
9a18eda Jovan Tanevski 2021-09-16
51e2651 Jovan Tanevski 2021-08-03
c25dcca Jovan Tanevski 2021-08-02
a386eaa Jovan Tanevski 2021-07-28 
fviz_silhouette(tcga.hclust) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
  cluster size ave.sil.width
1       1   65          0.09
2       2   98          0.31
3       3   21         -0.01

Version Author Date
9a18eda Jovan Tanevski 2021-09-16
51e2651 Jovan Tanevski 2021-08-03
c25dcca Jovan Tanevski 2021-08-02

Plot in 2D PCA and UMAP

fviz_pca_ind(tcga.pca, geom = "point", col.ind = as.factor(tcga.hclust$cluster)) +
  theme_classic()

Version Author Date
9a18eda Jovan Tanevski 2021-09-16
51e2651 Jovan Tanevski 2021-08-03
c25dcca Jovan Tanevski 2021-08-02 
tcga.umap.clus <-
  tcga.umap %>%
  cbind(tcga.hclust$cluster) %>%
  `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
9a18eda Jovan Tanevski 2021-09-16
f791e00 Jovan Tanevski 2021-08-03
51e2651 Jovan Tanevski 2021-08-03
c25dcca Jovan Tanevski 2021-08-02

Expression profiles per cluster

tcga.clustered <- tcga.raw %>%
  mutate(Cluster = as.factor(tcga.hclust$cluster)) %>%
  pivot_longer(names_to = "Marker", values_to = "Z", -Cluster)

profiles <- seq_len(tcga.hclust$nbclust) %>% map(~
ggplot(
  tcga.clustered %>% filter(Cluster == .x),
  aes(x = Marker, y = Z, 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(tcga.hclust$nbclust)))
Warning: Removed 27 rows containing non-finite values (stat_summary).
Warning: Removed 3 rows containing missing values (geom_segment).
Warning: Removed 3 rows containing non-finite values (stat_summary).

Warning: Removed 3 rows containing non-finite values (stat_summary).
Warning: Removed 1 rows containing missing values (geom_segment).

Version Author Date
9a18eda Jovan Tanevski 2021-09-16
51e2651 Jovan Tanevski 2021-08-03
c25dcca Jovan Tanevski 2021-08-02

Differential expression analysis (limma)

design <- model.matrix(~0 + as.factor(tcga.hclust$cluster))
colnames(design) <- paste0("Cluster", seq_len(tcga.hclust$nbclust))

tcga.hclust.limma <- lmFit(t(tcga.raw), design = design)

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

topTable(tcga.hclust.eb)
Cluster1 Cluster2 Cluster3 AveExpr F P.Value adj.P.Val
YAP_pS127 2.4877540 2.0607184 2.0439790 2.2096629 913.18442 0 0
P53 -1.4915298 -0.8366292 -1.3823670 -1.1302653 729.46397 0 0
BETACATENIN 1.8632840 1.3984936 0.8682628 1.5021704 349.87656 0 0
P70S6K_pT389 -1.7528388 -0.7550050 -1.6956402 -1.2148558 335.71231 0 0
PDK1_pS241 0.6114473 0.2713946 0.3504661 0.4005464 156.42129 0 0
AKT_pS473 -1.1958630 -0.1776438 -0.9913925 -0.6302143 148.88894 0 0
S6_pS235S236 -1.2954581 -0.5261595 0.1387379 -0.7220376 137.29853 0 0
TRANSGLUTAMINASE -0.4989926 -0.5342255 0.3389867 -0.4221190 64.77636 0 0
JNK_pT183Y185 -0.4004955 -0.0856420 -0.2233252 -0.2125813 62.21809 0 0
P38_pT180Y182 0.2950672 0.6794689 0.1684548 0.4853525 62.08894 0 0 
tests.hclust <- decideTests(tcga.hclust.eb)

tests.hclust@.Data
                 Cluster1 Cluster2 Cluster3
P53                    -1       -1       -1
AKT_pS473              -1       -1       -1
AKT_pT308              -1        1        0
BETACATENIN             1        1        1
JNK_pT183Y185          -1       -1       -1
MEK1_pS217S221         -1       -1        0
P38_pT180Y182           1        1        0
P70S6K_pT389           -1       -1       -1
PDK1_pS241              1        1        1
S6_pS235S236           -1       -1        0
YAP_pS127               1        1        1
TRANSGLUTAMINASE       -1       -1        1
summary(tests.hclust)
       Cluster1 Cluster2 Cluster3
Down          8        7        4
NotSig        0        0        4
Up            4        5        4

Differential expression analysis (silhouette)

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

core.samples <- which(tcga.hclust$silinfo$widths[,3] > 0)
tcga.core.samples <- tcga.raw %>% add_column(Cluster = tcga.hclust$cluster) %>% 
  slice(core.samples)

Calculate difference in means (mean(cluster) - mean()), one-vs-all t-test per marker and correct for FDR. Filter q <= 0.5

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) %>% 
  filter(q <= 0.05) %>% arrange(q)
Marker p Cluster Difference q
AKT_pS473 0.0000000 2 0.9977784 0.0000000
P53 0.0000000 2 0.6244634 0.0000000
P70S6K_pT389 0.0000000 2 1.0173218 0.0000000
P53 0.0000000 1 -0.5674498 0.0000000
AKT_pS473 0.0000000 1 -0.8376645 0.0000000
P70S6K_pT389 0.0000000 1 -0.8881928 0.0000000
S6_pS235S236 0.0000000 1 -0.8359746 0.0000000
PDK1_pS241 0.0000000 1 0.3113291 0.0000000
JNK_pT183Y185 0.0000000 2 0.2863957 0.0000000
JNK_pT183Y185 0.0000000 1 -0.3166869 0.0000000
AKT_pT308 0.0000000 2 0.3713197 0.0000000
PDK1_pS241 0.0000000 2 -0.2801783 0.0000000
AKT_pT308 0.0000000 1 -0.3832204 0.0000000
BETACATENIN 0.0000001 1 0.6651368 0.0000002
P38_pT180Y182 0.0000003 2 0.4524412 0.0000006
BETACATENIN 0.0000186 3 -0.7821302 0.0000418
S6_pS235S236 0.0000201 3 0.9014354 0.0000425
P38_pT180Y182 0.0000634 1 -0.3851268 0.0001269
TRANSGLUTAMINASE 0.0001356 3 0.8388293 0.0002569
YAP_pS127 0.0001584 1 0.4404121 0.0002851
S6_pS235S236 0.0003887 2 0.4079406 0.0006664
YAP_pS127 0.0012609 2 -0.3303099 0.0020633
P70S6K_pT389 0.0038452 3 -0.4702731 0.0060186
AKT_pS473 0.0043921 3 -0.5372638 0.0065882
BETACATENIN 0.0122357 2 -0.2980339 0.0176194
TRANSGLUTAMINASE 0.0157029 2 -0.2208274 0.0217425
P53 0.0278707 3 -0.2381224 0.0371609

Similarity and graph based clustering

Calculate leiden clustering based on weighted shared nearest neighbor graph

tcga.knn <- knn.index(tcga.norm, 10)
jaccard.cuttoff <- 0.1

snn <- seq_len(nrow(tcga.knn) - 1) %>% map_dfr(\(id){
  to <- seq(id + 1, nrow(tcga.knn))
  jaccard <- to %>%
    map_dbl(~ length(intersect(tcga.knn[id, ], tcga.knn[.x, ])) /
      length(union(tcga.knn[id, ], tcga.knn[.x, ])))
  
  tibble(from = id, to = to, weight = jaccard) %>% filter(jaccard >= jaccard.cuttoff)
})

leiden.clusters <- graph_from_data_frame(snn, directed = FALSE) %>% leiden()

Plot in 2D PCA and UMAP

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

Version Author Date
9a18eda Jovan Tanevski 2021-09-16 
ggplot(tcga.umap.clus %>% mutate(Cluster = as.factor(leiden.clusters)), aes(x = U1, y = U2, color = Cluster, shape = Cluster)) +
  geom_point() +
  theme_classic()

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

Expression profiles per cluster

tcga.clustered <- tcga.raw %>%
  mutate(Cluster = as.factor(leiden.clusters)) %>%
  pivot_longer(names_to = "Marker", values_to = "Z", -Cluster)

profiles <- seq_len(max(leiden.clusters)) %>% map(~
ggplot(
  tcga.clustered %>% filter(Cluster == .x),
  aes(x = Marker, y = Z, 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(leiden.clusters))))
Warning: Removed 29 rows containing non-finite values (stat_summary).
Warning: Removed 3 rows containing missing values (geom_segment).
Warning: Removed 3 rows containing non-finite values (stat_summary).
Warning: Removed 1 rows containing non-finite values (stat_summary).

Version Author Date
9a18eda Jovan Tanevski 2021-09-16
f791e00 Jovan Tanevski 2021-08-03
51e2651 Jovan Tanevski 2021-08-03
55fb7fc Jovan Tanevski 2021-08-02

Differential expression analysis (limma)

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

tcga.leiden.limma <- lmFit(t(tcga.raw), design = design)

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

topTable(tcga.hclust.eb)
Cluster1 Cluster2 Cluster3 AveExpr F P.Value adj.P.Val
YAP_pS127 2.4877540 2.0607184 2.0439790 2.2096629 913.18442 0 0
P53 -1.4915298 -0.8366292 -1.3823670 -1.1302653 729.46397 0 0
BETACATENIN 1.8632840 1.3984936 0.8682628 1.5021704 349.87656 0 0
P70S6K_pT389 -1.7528388 -0.7550050 -1.6956402 -1.2148558 335.71231 0 0
PDK1_pS241 0.6114473 0.2713946 0.3504661 0.4005464 156.42129 0 0
AKT_pS473 -1.1958630 -0.1776438 -0.9913925 -0.6302143 148.88894 0 0
S6_pS235S236 -1.2954581 -0.5261595 0.1387379 -0.7220376 137.29853 0 0
TRANSGLUTAMINASE -0.4989926 -0.5342255 0.3389867 -0.4221190 64.77636 0 0
JNK_pT183Y185 -0.4004955 -0.0856420 -0.2233252 -0.2125813 62.21809 0 0
P38_pT180Y182 0.2950672 0.6794689 0.1684548 0.4853525 62.08894 0 0 
tests.leiden <- decideTests(tcga.leiden.eb)

tests.leiden@.Data
                 Cluster1 Cluster2 Cluster3
P53                    -1       -1       -1
AKT_pS473              -1       -1       -1
AKT_pT308              -1        1        1
BETACATENIN             1        1        1
JNK_pT183Y185          -1       -1        0
MEK1_pS217S221         -1       -1       -1
P38_pT180Y182           1        1        1
P70S6K_pT389           -1       -1       -1
PDK1_pS241              1        1        1
S6_pS235S236           -1       -1       -1
YAP_pS127               1        1        1
TRANSGLUTAMINASE       -1       -1       -1
summary(tests.leiden)
       Cluster1 Cluster2 Cluster3
Down          8        7        6
NotSig        0        0        1
Up            4        5        5

Differential expression analysis (silhouette)

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

silhouette.leiden <- silhouette(leiden.clusters, dist(tcga.raw))
fviz_silhouette(silhouette.leiden)
  cluster size ave.sil.width
1       1   75         -0.08
2       2   60          0.14
3       3   49          0.04

Version Author Date
9a18eda Jovan Tanevski 2021-09-16
f791e00 Jovan Tanevski 2021-08-03
51e2651 Jovan Tanevski 2021-08-03
55fb7fc Jovan Tanevski 2021-08-02

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

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

Calculate difference in means (mean(cluster) - mean()), one-vs-all t-test per marker and correct for FDR. Filter q <= 0.5

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) %>% 
  filter(q <= 0.05) %>% arrange(q)
Marker p Cluster Difference q
P70S6K_pT389 0.0000000 1 -1.7217698 0.0000000
P53 0.0000000 1 -1.0758468 0.0000000
AKT_pS473 0.0000000 1 -1.5412559 0.0000000
P53 0.0000000 3 0.5124872 0.0000000
P70S6K_pT389 0.0000000 3 0.8783924 0.0000000
BETACATENIN 0.0000000 3 -0.8666396 0.0000000
AKT_pS473 0.0000000 3 0.7042814 0.0000000
JNK_pT183Y185 0.0000000 3 0.3040051 0.0000000
PDK1_pS241 0.0000001 3 -0.2830544 0.0000005
JNK_pT183Y185 0.0000008 1 -0.4645504 0.0000029
AKT_pT308 0.0000011 1 -0.5160337 0.0000036
P38_pT180Y182 0.0000026 3 0.4947317 0.0000079
AKT_pT308 0.0000061 3 0.2417146 0.0000168
P53 0.0000247 2 0.3711768 0.0000528
AKT_pS473 0.0000249 2 0.5569786 0.0000528
P38_pT180Y182 0.0000210 1 -0.7042719 0.0000528
YAP_pS127 0.0000244 3 -0.4641142 0.0000528
S6_pS235S236 0.0000388 3 0.4884534 0.0000775
P70S6K_pT389 0.0001566 2 0.5449114 0.0002966
YAP_pS127 0.0001853 1 0.7514012 0.0003335
BETACATENIN 0.0004519 2 0.4873364 0.0007747
PDK1_pS241 0.0022423 1 0.2940508 0.0036693
AKT_pT308 0.0070042 2 0.1814968 0.0109630
S6_pS235S236 0.0107877 1 -0.6036238 0.0157459
TRANSGLUTAMINASE 0.0109346 1 0.4139345 0.0157459
MEK1_pS217S221 0.0123095 2 -0.1622516 0.0170440
TRANSGLUTAMINASE 0.0179813 3 -0.2528470 0.0239750

Consensus NMF (9 clusters)

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

plot(tcga.nmf.rank)

Version Author Date
9a18eda Jovan Tanevski 2021-09-16
f791e00 Jovan Tanevski 2021-08-03
51e2651 Jovan Tanevski 2021-08-03
55fb7fc Jovan Tanevski 2021-08-02 
tcga.nmf <- nmf(as.matrix(t(tcga.rank)), 9, nrun = 20, seed = 42)

For this analysis 9 clusters were selected based on consensus cophenetic correlation coefficient The analysis can also be repeated with 3 clusters as the cophenetic correlation coefficient for 3 clusters is higher (in continuation).

Extract basis of NMF (signature of cluster)

basismap(tcga.nmf)

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

Extract coefficients of NMF (soft clustering of samples)

coefmap(tcga.nmf)

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

Check for signs of overfitting

consensusmap(tcga.nmf)

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

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
9a18eda Jovan Tanevski 2021-09-16 
ggplot(tcga.umap.clus %>% mutate(Cluster = as.factor(nmf.clusters)), aes(x = U1, y = U2, color = Cluster)) +
  geom_point() +
  theme_classic()

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

Expression profiles per cluster

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

profiles <- seq_len(max(nmf.clusters)) %>% map(~
ggplot(
  tcga.clustered.nmf %>% filter(Cluster == .x),
  aes(x = Marker, y = Z, 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))))
Warning: Removed 2 rows containing non-finite values (stat_summary).
Warning: Removed 1 rows containing missing values (geom_segment).
Warning: Removed 8 rows containing non-finite values (stat_summary).
Warning: Removed 1 rows containing missing values (geom_segment).
Warning: Removed 4 rows containing non-finite values (stat_summary).
Warning: Removed 2 rows containing non-finite values (stat_summary).
Warning: Removed 2 rows containing missing values (geom_segment).
Warning: Removed 2 rows containing non-finite values (stat_summary).
Warning: Removed 3 rows containing missing values (geom_segment).
Warning: Removed 4 rows containing non-finite values (stat_summary).
Warning: Removed 1 rows containing missing values (geom_segment).
Warning: Removed 5 rows containing non-finite values (stat_summary).
Warning: Removed 3 rows containing missing values (geom_segment).
Warning: Removed 4 rows containing non-finite values (stat_summary).
Warning: Removed 2 rows containing missing values (geom_segment).
Warning: Removed 2 rows containing non-finite values (stat_summary).

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

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.raw), design = design)

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

topTable(tcga.nmf.eb)
Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Cluster8 Cluster9 AveExpr F P.Value adj.P.Val
YAP_pS127 2.3978952 2.6128531 2.3201659 3.1420813 2.2238593 2.3185180 2.2843542 2.7963107 1.9927770 2.2096629 317.40603 0 0
P53 -1.3898400 -1.5713606 -1.5791340 -1.6966091 -1.6871837 -1.3987109 -1.4106263 -1.4182143 -0.7678330 -1.1302653 313.60412 0 0
P70S6K_pT389 -1.5005166 -1.8855909 -1.8771042 -1.9916481 -2.1545847 -1.6546863 -1.6219767 -1.7235497 -0.6721740 -1.2148558 129.29253 0 0
BETACATENIN 1.5219449 1.9416915 1.7378802 1.6725415 1.2337905 1.6827628 1.1085864 2.5889554 1.4535112 1.5021704 112.27214 0 0
AKT_pS473 -1.0718449 -1.1911002 -1.1885023 -1.2880118 -1.6214364 0.0276857 -1.3219127 -1.0160392 -0.2052334 -0.6302143 69.34864 0 0
PDK1_pS241 0.5337273 0.8230705 0.4057842 0.4516760 0.5444971 0.5639792 0.5094236 0.4634277 0.2421504 0.4005464 55.29196 0 0
JNK_pT183Y185 -0.4204487 -0.5393783 -0.6318378 -0.5722916 -0.5929950 -0.3430688 -0.0887012 -0.5234238 -0.0614400 -0.2125813 39.72208 0 0
S6_pS235S236 -0.7697932 -1.7578225 -1.4877032 -1.2771461 0.1585755 -0.7772765 -0.7770087 -1.3703878 -0.5066805 -0.7220376 33.38019 0 0
TRANSGLUTAMINASE 0.4737129 -0.4879732 -0.7383488 -0.8834281 -0.4936744 -0.3968575 -0.2877142 -0.6573294 -0.5450193 -0.4221190 21.94286 0 0
P38_pT180Y182 0.1586131 -0.0629108 1.1912479 0.1479636 0.6498547 0.6538162 0.4270264 0.1811323 0.5655827 0.4853525 21.42125 0 0 
tests.nmf <- decideTests(tcga.nmf.eb)

tests.nmf@.Data
                 Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7
P53                    -1       -1       -1       -1       -1       -1       -1
AKT_pS473              -1       -1       -1       -1       -1        0       -1
AKT_pT308               0       -1        0        0       -1        1       -1
BETACATENIN             1        1        1        1        1        1        1
JNK_pT183Y185          -1       -1       -1       -1       -1       -1       -1
MEK1_pS217S221         -1       -1       -1       -1        0       -1        1
P38_pT180Y182           0        0        1        0        1        1        1
P70S6K_pT389           -1       -1       -1       -1       -1       -1       -1
PDK1_pS241              1        1        1        1        1        1        1
S6_pS235S236           -1       -1       -1       -1        0       -1       -1
YAP_pS127               1        1        1        1        1        1        1
TRANSGLUTAMINASE        1       -1       -1       -1       -1       -1       -1
                 Cluster8 Cluster9
P53                    -1       -1
AKT_pS473              -1       -1
AKT_pT308               0        1
BETACATENIN             1        1
JNK_pT183Y185          -1       -1
MEK1_pS217S221         -1       -1
P38_pT180Y182           0        1
P70S6K_pT389           -1       -1
PDK1_pS241              1        1
S6_pS235S236           -1       -1
YAP_pS127               1        1
TRANSGLUTAMINASE       -1       -1
summary(tests.nmf)
       Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Cluster8
Down          6        8        7        7        6        6        7        7
NotSig        2        1        1        2        2        1        0        2
Up            4        3        4        3        4        5        5        3
       Cluster9
Down          7
NotSig        0
Up            5

Differential expression analysis (silhouette)

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

silhouette.nmf <- silhouette(nmf.clusters, dist(tcga.raw))
fviz_silhouette(silhouette.nmf)
  cluster size ave.sil.width
1       1   14         -0.09
2       2   10         -0.20
3       3    6         -0.22
4       4    3          0.01
5       5    7         -0.09
6       6   17         -0.20
7       7   31         -0.21
8       8    8          0.10
9       9   88          0.43

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

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

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

Calculate difference in means (mean(cluster) - mean()), one-vs-all t-test per marker and correct for FDR. Filter q <= 0.5

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) %>% 
  filter(q <= 0.05) %>% arrange(q)
Marker p Cluster Difference q
TRANSGLUTAMINASE 0.0000000 7 0.9957910 0.0000000
P70S6K_pT389 0.0000000 9 1.2611914 0.0000001
P53 0.0000000 9 0.8911042 0.0000002
PDK1_pS241 0.0000174 9 -0.3434557 0.0003535
AKT_pT308 0.0000210 6 1.5135159 0.0003535
BETACATENIN 0.0000333 8 1.0382497 0.0004661
PDK1_pS241 0.0000579 7 0.6671920 0.0006952
JNK_pT183Y185 0.0001002 9 0.3905673 0.0010520
S6_pS235S236 0.0001729 8 -0.9595315 0.0016137
P70S6K_pT389 0.0002544 8 -0.6218769 0.0021373
AKT_pS473 0.0003231 8 -0.5407695 0.0024673
P53 0.0003875 8 -0.4337668 0.0027126
BETACATENIN 0.0006708 4 0.2519954 0.0043344
P70S6K_pT389 0.0008152 1 -0.9801315 0.0048903
AKT_pS473 0.0008733 6 1.0744155 0.0048903
TRANSGLUTAMINASE 0.0013948 5 -0.4224691 0.0073228
AKT_pS473 0.0017123 5 -2.2164129 0.0084609
JNK_pT183Y185 0.0023667 8 -0.3278548 0.0110446
AKT_pT308 0.0032882 5 -0.9700170 0.0145372
AKT_pS473 0.0040119 9 0.7960121 0.0160475
MEK1_pS217S221 0.0038236 8 -0.1997002 0.0160475
AKT_pS473 0.0053995 1 -0.7913235 0.0206161
S6_pS235S236 0.0069532 9 0.5536300 0.0253943
TRANSGLUTAMINASE 0.0103733 8 -0.2580312 0.0363067

Consensus NMF (3 clusters)

Repeat the analysis, but using 3 clusters.

tcga.nmf <- nmf(as.matrix(t(tcga.rank)), 3, nrun = 20, seed = 42)

Extract basis of NMF (signature of cluster)

basismap(tcga.nmf)

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

Extract coefficients of NMF (soft clustering of samples)

coefmap(tcga.nmf)

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

Check for signs of overfitting

consensusmap(tcga.nmf)

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

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
9a18eda Jovan Tanevski 2021-09-16 
ggplot(tcga.umap.clus %>% mutate(Cluster = as.factor(nmf.clusters)), aes(x = U1, y = U2, color = Cluster)) +
  geom_point() +
  theme_classic()

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

Expression profiles per cluster

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

profiles <- seq_len(max(nmf.clusters)) %>% map(~
ggplot(
  tcga.clustered.nmf %>% filter(Cluster == .x),
  aes(x = Marker, y = Z, 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))))
Warning: Removed 9 rows containing non-finite values (stat_summary).
Warning: Removed 2 rows containing missing values (geom_segment).
Warning: Removed 21 rows containing non-finite values (stat_summary).
Warning: Removed 2 rows containing missing values (geom_segment).
Warning: Removed 3 rows containing non-finite values (stat_summary).

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

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.raw), design = design)

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

topTable(tcga.nmf.eb)
Cluster1 Cluster2 Cluster3 AveExpr F P.Value adj.P.Val
YAP_pS127 2.3056243 2.8307973 2.0079290 2.2096629 1082.86252 0 0
P53 -1.5692867 -1.5852183 -0.8563584 -1.1302653 926.61353 0 0
BETACATENIN 1.0743656 2.1963955 1.4597442 1.5021704 373.80077 0 0
P70S6K_pT389 -1.8678252 -1.8760045 -0.8116854 -1.2148558 367.15157 0 0
AKT_pS473 -1.4512149 -1.1160172 -0.2172413 -0.6302143 199.61468 0 0
PDK1_pS241 0.5507008 0.6284452 0.2872053 0.4005464 147.94848 0 0
S6_pS235S236 -0.6603990 -1.4782462 -0.5374888 -0.7220376 90.44153 0 0
JNK_pT183Y185 -0.2750007 -0.5252041 -0.1062158 -0.2125813 69.30905 0 0
P38_pT180Y182 0.4358148 0.0701750 0.6151988 0.4853525 60.17513 0 0
MEK1_pS217S221 0.2105159 -0.3666276 -0.2427199 -0.1675294 44.84511 0 0 
tests.nmf <- decideTests(tcga.nmf.eb)

tests.nmf@.Data
                 Cluster1 Cluster2 Cluster3
P53                    -1       -1       -1
AKT_pS473              -1       -1       -1
AKT_pT308              -1        0        1
BETACATENIN             1        1        1
JNK_pT183Y185          -1       -1       -1
MEK1_pS217S221          1       -1       -1
P38_pT180Y182           1        0        1
P70S6K_pT389           -1       -1       -1
PDK1_pS241              1        1        1
S6_pS235S236           -1       -1       -1
YAP_pS127               1        1        1
TRANSGLUTAMINASE        0       -1       -1
summary(tests.nmf)
       Cluster1 Cluster2 Cluster3
Down          6        7        7
NotSig        1        2        0
Up            5        3        5

Differential expression analysis (silhouette)

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

silhouette.nmf <- silhouette(nmf.clusters, dist(tcga.raw))
fviz_silhouette(silhouette.nmf)
  cluster size ave.sil.width
1       1   39         -0.08
2       2   31          0.11
3       3  114          0.46

Version Author Date
9a18eda Jovan Tanevski 2021-09-16

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

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

Calculate difference in means (mean(cluster) - mean()), one-vs-all t-test per marker and correct for FDR. Filter q <= 0.5

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) %>% 
  filter(q <= 0.05) %>% arrange(q)
Marker p Cluster Difference q
P53 0.0000000 3 0.9276232 0.0000000
P70S6K_pT389 0.0000000 3 1.3233875 0.0000000
AKT_pS473 0.0000000 3 1.3386013 0.0000000
P53 0.0000000 2 -0.7862438 0.0000000
P70S6K_pT389 0.0000000 2 -1.1617138 0.0000000
BETACATENIN 0.0000000 2 0.9884260 0.0000000
JNK_pT183Y185 0.0000000 2 -0.4408868 0.0000001
AKT_pS473 0.0000001 2 -0.9498340 0.0000003
S6_pS235S236 0.0000003 2 -1.1646209 0.0000012
AKT_pS473 0.0000006 1 -1.6201703 0.0000021
P70S6K_pT389 0.0000019 1 -1.1924270 0.0000064
JNK_pT183Y185 0.0000021 3 0.3864052 0.0000064
YAP_pS127 0.0000024 2 0.9972987 0.0000065
P53 0.0000052 1 -0.8873985 0.0000134
AKT_pT308 0.0000062 3 0.4279157 0.0000148
P38_pT180Y182 0.0000123 3 0.6090580 0.0000278
PDK1_pS241 0.0000171 2 0.3768913 0.0000362
YAP_pS127 0.0000189 3 -0.7565865 0.0000378
P38_pT180Y182 0.0001395 2 -0.6169658 0.0002642
PDK1_pS241 0.0003042 3 -0.2904325 0.0005476
AKT_pT308 0.0018944 2 -0.3545662 0.0031801
MEK1_pS217S221 0.0019434 1 0.6063847 0.0031801
S6_pS235S236 0.0020345 3 0.6409746 0.0031845
BETACATENIN 0.0021520 1 -0.9504341 0.0032281
MEK1_pS217S221 0.0024026 2 -0.1463695 0.0034597
AKT_pT308 0.0085914 1 -0.4243061 0.0118958
TRANSGLUTAMINASE 0.0270956 1 0.7888172 0.0361275 

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       NMF_0.23.0          synchronicity_1.3.5
 [7] bigmemory_4.5.36    Biobase_2.52.0      BiocGenerics_0.38.0
[10] cluster_2.1.2       rngtools_1.5        pkgmaker_0.32.2    
[13] registry_0.5-1      limma_3.48.3        leiden_0.3.9       
[16] igraph_1.2.6        FNN_1.1.3           cowplot_1.1.1      
[19] factoextra_1.0.7    uwot_0.1.10         Matrix_1.3-4       
[22] skimr_2.1.3         forcats_0.5.1       stringr_1.4.0      
[25] dplyr_1.0.7         purrr_0.3.4         readr_2.0.1        
[28] tidyr_1.1.3         tibble_3.1.4        ggplot2_3.3.5      
[31] tidyverse_1.3.1     workflowr_1.6.2    

loaded via a namespace (and not attached):
  [1] readxl_1.3.1        uuid_0.1-4          backports_1.2.1    
  [4] Hmisc_4.5-0         plyr_1.8.6          repr_1.1.3         
  [7] splines_4.1.1       gridBase_0.4-7      digest_0.6.27      
 [10] htmltools_0.5.2     viridis_0.6.1       fansi_0.5.0        
 [13] checkmate_2.0.0     magrittr_2.0.1      tzdb_0.1.2         
 [16] openxlsx_4.2.4      modelr_0.1.8        vroom_1.5.5        
 [19] jpeg_0.1-9          colorspace_2.0-2    rvest_1.0.1        
 [22] ggrepel_0.9.1       haven_2.4.3         xfun_0.26          
 [25] crayon_1.4.1        jsonlite_1.7.2      bigmemory.sri_0.1.3
 [28] survival_3.2-13     glue_1.4.2          gtable_0.3.0       
 [31] car_3.0-11          abind_1.4-5         scales_1.1.1       
 [34] DBI_1.1.1           rstatix_0.7.0       Rcpp_1.0.7         
 [37] htmlTable_2.2.1     viridisLite_0.4.0   xtable_1.8-4       
 [40] reticulate_1.21     foreign_0.8-81      bit_4.0.4          
 [43] Formula_1.2-4       htmlwidgets_1.5.4   httr_1.4.2         
 [46] ellipsis_0.3.2      pkgconfig_2.0.3     farver_2.1.0       
 [49] nnet_7.3-16         sass_0.4.0          dbplyr_2.1.1       
 [52] utf8_1.2.2          tidyselect_1.1.1    labeling_0.4.2     
 [55] rlang_0.4.11        reshape2_1.4.4      later_1.3.0        
 [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.1        
 [61] cli_3.0.1           generics_0.1.0      broom_0.7.9        
 [64] evaluate_0.14       fastmap_1.1.0       yaml_2.2.1         
 [67] knitr_1.34          bit64_4.0.5         fs_1.5.0           
 [70] zip_2.2.0           dendextend_1.15.1   whisker_0.4        
 [73] xml2_1.3.2          compiler_4.1.1      rstudioapi_0.13    
 [76] curl_4.3.2          png_0.1-7           ggsignif_0.6.3     
 [79] reprex_2.0.1        bslib_0.3.0         stringi_1.7.4      
 [82] highr_0.9           RSpectra_0.16-0     lattice_0.20-44    
 [85] vctrs_0.3.8         pillar_1.6.2        lifecycle_1.0.0    
 [88] jquerylib_0.1.4     data.table_1.14.0   httpuv_1.6.3       
 [91] latticeExtra_0.6-29 R6_2.5.1            promises_1.2.0.1   
 [94] gridExtra_2.3       rio_0.5.27          codetools_0.2-18   
 [97] assertthat_0.2.1    rprojroot_2.0.2     withr_2.4.2        
[100] hms_1.1.0           rpart_4.1-15        grid_4.1.1         
[103] rmarkdown_2.11      carData_3.0-4       git2r_0.28.0       
[106] ggpubr_0.4.0        lubridate_1.7.10    base64enc_0.1-3