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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)
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 | <U+2581><U+2582><U+2582><U+2585><U+2587> |
AKT_pS473 | 0 | 1 | -0.63 | 0.71 | -2.76 | -1.02 | -0.41 | -0.15 | 0.96 | <U+2581><U+2582><U+2583><U+2587><U+2581> |
AKT_pT308 | 0 | 1 | 0.07 | 0.42 | -1.16 | -0.07 | 0.09 | 0.22 | 1.72 | <U+2581><U+2583><U+2587><U+2581><U+2581> |
BETACATENIN | 0 | 1 | 1.50 | 0.72 | -1.30 | 1.08 | 1.51 | 1.87 | 3.70 | <U+2581><U+2581><U+2587><U+2585><U+2581> |
JNK_pT183Y185 | 0 | 1 | -0.21 | 0.29 | -1.14 | -0.36 | -0.17 | -0.04 | 0.48 | <U+2581><U+2582><U+2587><U+2587><U+2582> |
MEK1_pS217S221 | 0 | 1 | -0.17 | 0.36 | -0.82 | -0.35 | -0.23 | -0.10 | 2.58 | <U+2587><U+2583><U+2581><U+2581><U+2581> |
P38_pT180Y182 | 0 | 1 | 0.49 | 0.57 | -1.45 | 0.31 | 0.50 | 0.72 | 2.95 | <U+2581><U+2582><U+2587><U+2581><U+2581> |
P70S6K_pT389 | 0 | 1 | -1.21 | 0.75 | -3.22 | -1.58 | -0.95 | -0.65 | 1.09 | <U+2582><U+2583><U+2587><U+2583><U+2581> |
PDK1_pS241 | 0 | 1 | 0.40 | 0.31 | -0.56 | 0.23 | 0.37 | 0.55 | 1.62 | <U+2581><U+2587><U+2587><U+2582><U+2581> |
S6_pS235S236 | 0 | 1 | -0.72 | 0.75 | -3.53 | -1.06 | -0.67 | -0.36 | 1.12 | <U+2581><U+2581><U+2585><U+2587><U+2582> |
YAP_pS127 | 0 | 1 | 2.21 | 0.61 | 0.68 | 1.78 | 2.10 | 2.54 | 4.05 | <U+2581><U+2587><U+2587><U+2582><U+2581> |
TRANSGLUTAMINASE | 0 | 1 | -0.42 | 0.56 | -1.24 | -0.83 | -0.59 | -0.20 | 2.72 | <U+2587><U+2583><U+2581><U+2581><U+2581> |
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 | 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 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
AKT_pS473 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
AKT_pT308 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
BETACATENIN | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
JNK_pT183Y185 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
MEK1_pS217S221 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
P38_pT180Y182 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
P70S6K_pT389 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
PDK1_pS241 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
S6_pS235S236 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
YAP_pS127 | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
TRANSGLUTAMINASE | 0 | 1 | 0.01 | 0.52 | -1.59 | -0.27 | 0.05 | 0.3 | 1.87 | <U+2581><U+2583><U+2587><U+2582><U+2581> |
tcga.rank <- mutate_all(tcga.raw, rank)
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)
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$gap_stat)
fviz_dend(tcga.hclust, rect = TRUE)
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.
fviz_silhouette(tcga.hclust) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
cluster size ave.sil.width
1 1 65 0.09
2 2 98 0.31
3 3 21 -0.01
Plot in 2D PCA and UMAP
fviz_pca_ind(tcga.pca, geom = "point", col.ind = as.factor(tcga.hclust$cluster)) +
theme_classic()
tcga.umap.clus <-
tcga.umap %>%
cbind(tcga.hclust$cluster) %>%
`colnames<-`(c("U1", "U2", "Cluster")) %>%
as_tibble() %>%
mutate_at("Cluster", as.factor)
ggplot(tcga.umap.clus, aes(x = U1, y = U2, color = Cluster)) +
geom_point() +
theme_classic()
Expression profiles per cluster
tcga.clustered <- tcga.norm %>%
mutate(Cluster = as.factor(tcga.hclust$cluster)) %>%
pivot_longer(names_to = "Marker", values_to = "Norm.value", -Cluster)
profiles <- seq_len(tcga.hclust$nbclust) %>% map(~
ggplot(
tcga.clustered %>% filter(Cluster == .x),
aes(x = Marker, y = Norm.value, color = Marker)
) +
stat_summary(fun.data = mean_sdl, show.legend = FALSE) +
ylim(-3, 3) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)))
plot_grid(plotlist = profiles, labels = paste("Cluster", seq_len(tcga.hclust$nbclust)))
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.norm), design = design)
tcga.hclust.eb <- eBayes(tcga.hclust.limma)
topTable(tcga.hclust.eb)
Cluster1 | Cluster2 | Cluster3 | AveExpr | F | P.Value | adj.P.Val | |
---|---|---|---|---|---|---|---|
AKT_pS473 | -0.3923946 | 0.3388212 | -0.2436093 | 0.0140382 | 45.89570 | 0e+00 | 0e+00 |
P53 | -0.3749402 | 0.3330240 | -0.2705809 | 0.0140382 | 42.93496 | 0e+00 | 0e+00 |
S6_pS235S236 | -0.3945736 | 0.1484539 | 0.6515164 | 0.0140382 | 41.88864 | 0e+00 | 0e+00 |
P70S6K_pT389 | -0.3440403 | 0.3195119 | -0.3031673 | 0.0140382 | 37.41850 | 0e+00 | 0e+00 |
AKT_pT308 | -0.3538640 | 0.2450292 | 0.0748255 | 0.0140382 | 23.97404 | 0e+00 | 0e+00 |
PDK1_pS241 | 0.3746244 | -0.2098970 | -0.0566096 | 0.0140863 | 22.61683 | 0e+00 | 0e+00 |
JNK_pT183Y185 | -0.3138481 | 0.2263673 | 0.0380556 | 0.0140382 | 18.42235 | 0e+00 | 0e+00 |
BETACATENIN | 0.2608163 | -0.0429501 | -0.4838529 | 0.0140382 | 14.76223 | 0e+00 | 0e+00 |
TRANSGLUTAMINASE | -0.0122248 | -0.0965749 | 0.6110503 | 0.0139842 | 13.40254 | 0e+00 | 1e-07 |
P38_pT180Y182 | -0.1809971 | 0.2029575 | -0.2638881 | 0.0140401 | 11.43223 | 5e-07 | 6e-07 |
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 0 -1
JNK_pT183Y185 -1 1 0
MEK1_pS217S221 0 0 0
P38_pT180Y182 -1 1 -1
P70S6K_pT389 -1 1 -1
PDK1_pS241 1 -1 0
S6_pS235S236 -1 1 1
YAP_pS127 1 0 0
TRANSGLUTAMINASE 0 0 1
summary(tests.hclust)
Cluster1 Cluster2 Cluster3
Down 7 1 5
NotSig 2 4 5
Up 3 7 2
Differential expression analysis (silhouette)
Select only the samples with positive silhouette scores as “core samples”
core.samples <- which(tcga.hclust$silinfo$widths[,3] > 0)
tcga.core.samples <- tcga.norm %>% add_column(Cluster = tcga.hclust$cluster) %>%
slice(core.samples)
Calculate difference in means (mean(cluster) - mean(other)), one-vs-all t-test per marker and correct for FDR. Filter q <= 0.05
unique(tcga.core.samples$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 | 0.7230948 | 0.0000000 |
P53 | 0.0000000 | 2 | 0.6910253 | 0.0000000 |
P70S6K_pT389 | 0.0000000 | 2 | 0.6814648 | 0.0000000 |
P53 | 0.0000000 | 1 | -0.6242319 | 0.0000000 |
AKT_pS473 | 0.0000000 | 1 | -0.6051380 | 0.0000000 |
S6_pS235S236 | 0.0000000 | 1 | -0.6115548 | 0.0000000 |
PDK1_pS241 | 0.0000000 | 1 | 0.5346180 | 0.0000000 |
P70S6K_pT389 | 0.0000000 | 1 | -0.5955775 | 0.0000000 |
JNK_pT183Y185 | 0.0000000 | 1 | -0.5571427 | 0.0000000 |
AKT_pT308 | 0.0000000 | 2 | 0.5115924 | 0.0000000 |
PDK1_pS241 | 0.0000000 | 2 | -0.4901475 | 0.0000000 |
AKT_pT308 | 0.0000000 | 1 | -0.5342625 | 0.0000000 |
JNK_pT183Y185 | 0.0000000 | 2 | 0.4812649 | 0.0000000 |
P38_pT180Y182 | 0.0000000 | 2 | 0.4413414 | 0.0000001 |
BETACATENIN | 0.0000003 | 1 | 0.4567438 | 0.0000007 |
TRANSGLUTAMINASE | 0.0000017 | 3 | 0.6685184 | 0.0000039 |
P38_pT180Y182 | 0.0000105 | 1 | -0.3895088 | 0.0000222 |
BETACATENIN | 0.0000149 | 3 | -0.6160989 | 0.0000298 |
S6_pS235S236 | 0.0000670 | 3 | 0.6776690 | 0.0001269 |
S6_pS235S236 | 0.0004215 | 2 | 0.2909752 | 0.0007588 |
AKT_pS473 | 0.0004449 | 3 | -0.3937226 | 0.0007627 |
P70S6K_pT389 | 0.0008687 | 3 | -0.3136291 | 0.0014215 |
YAP_pS127 | 0.0021548 | 1 | 0.3000306 | 0.0033727 |
TRANSGLUTAMINASE | 0.0051001 | 2 | -0.2383929 | 0.0076501 |
P53 | 0.0056982 | 3 | -0.2719157 | 0.0082054 |
YAP_pS127 | 0.0245292 | 2 | -0.1964199 | 0.0339635 |
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()
ggplot(tcga.umap.clus %>% mutate(Cluster = as.factor(leiden.clusters)),
aes(x = U1, y = U2, color = Cluster, shape = Cluster)) +
geom_point() +
theme_classic()
Expression profiles per cluster
tcga.clustered <- tcga.norm %>%
mutate(Cluster = as.factor(leiden.clusters)) %>%
pivot_longer(names_to = "Marker", values_to = "Norm.value", -Cluster)
profiles <- seq_len(max(leiden.clusters)) %>% map(~
ggplot(
tcga.clustered %>% 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(leiden.clusters))))
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.norm), design = design)
tcga.leiden.eb <- eBayes(tcga.leiden.limma)
topTable(tcga.hclust.eb)
Cluster1 | Cluster2 | Cluster3 | AveExpr | F | P.Value | adj.P.Val | |
---|---|---|---|---|---|---|---|
AKT_pS473 | -0.3923946 | 0.3388212 | -0.2436093 | 0.0140382 | 45.89570 | 0e+00 | 0e+00 |
P53 | -0.3749402 | 0.3330240 | -0.2705809 | 0.0140382 | 42.93496 | 0e+00 | 0e+00 |
S6_pS235S236 | -0.3945736 | 0.1484539 | 0.6515164 | 0.0140382 | 41.88864 | 0e+00 | 0e+00 |
P70S6K_pT389 | -0.3440403 | 0.3195119 | -0.3031673 | 0.0140382 | 37.41850 | 0e+00 | 0e+00 |
AKT_pT308 | -0.3538640 | 0.2450292 | 0.0748255 | 0.0140382 | 23.97404 | 0e+00 | 0e+00 |
PDK1_pS241 | 0.3746244 | -0.2098970 | -0.0566096 | 0.0140863 | 22.61683 | 0e+00 | 0e+00 |
JNK_pT183Y185 | -0.3138481 | 0.2263673 | 0.0380556 | 0.0140382 | 18.42235 | 0e+00 | 0e+00 |
BETACATENIN | 0.2608163 | -0.0429501 | -0.4838529 | 0.0140382 | 14.76223 | 0e+00 | 0e+00 |
TRANSGLUTAMINASE | -0.0122248 | -0.0965749 | 0.6110503 | 0.0139842 | 13.40254 | 0e+00 | 1e-07 |
P38_pT180Y182 | -0.1809971 | 0.2029575 | -0.2638881 | 0.0140401 | 11.43223 | 5e-07 | 6e-07 |
tests.leiden <- decideTests(tcga.leiden.eb)
tests.leiden@.Data
Cluster1 Cluster2 Cluster3 Cluster4
P53 -1 1 1 0
AKT_pS473 -1 1 1 0
AKT_pT308 -1 0 1 0
BETACATENIN 1 1 -1 -1
JNK_pT183Y185 -1 0 1 0
MEK1_pS217S221 0 0 0 0
P38_pT180Y182 -1 0 1 0
P70S6K_pT389 -1 1 1 -1
PDK1_pS241 1 0 -1 0
S6_pS235S236 -1 0 1 1
YAP_pS127 1 0 -1 0
TRANSGLUTAMINASE 0 0 -1 1
summary(tests.leiden)
Cluster1 Cluster2 Cluster3 Cluster4
Down 7 0 4 2
NotSig 2 8 1 8
Up 3 4 7 2
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.norm))
fviz_silhouette(silhouette.leiden)
cluster size ave.sil.width
1 1 65 -0.06
2 2 60 0.18
3 3 47 0.04
4 4 12 -0.05
Select only the samples with positive silhouette scores as “core samples”
core.samples <- which(silhouette.leiden[,3] > 0)
tcga.core.samples <- tcga.norm %>% add_column(Cluster = leiden.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
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 | 1 | -0.9515080 | 0.0000000 |
P53 | 0.0000000 | 1 | -1.0430459 | 0.0000000 |
P70S6K_pT389 | 0.0000000 | 1 | -1.0392005 | 0.0000000 |
P70S6K_pT389 | 0.0000000 | 3 | 0.7451213 | 0.0000000 |
P53 | 0.0000000 | 3 | 0.7888113 | 0.0000000 |
AKT_pS473 | 0.0000000 | 3 | 0.5951014 | 0.0000000 |
JNK_pT183Y185 | 0.0000000 | 1 | -0.9124860 | 0.0000000 |
JNK_pT183Y185 | 0.0000000 | 3 | 0.5929591 | 0.0000001 |
PDK1_pS241 | 0.0000003 | 3 | -0.5886677 | 0.0000013 |
AKT_pT308 | 0.0000003 | 1 | -0.8127502 | 0.0000014 |
BETACATENIN | 0.0000003 | 3 | -0.5613748 | 0.0000015 |
AKT_pT308 | 0.0000008 | 3 | 0.3897925 | 0.0000030 |
P38_pT180Y182 | 0.0000011 | 3 | 0.4830546 | 0.0000039 |
PDK1_pS241 | 0.0000069 | 1 | 0.6190042 | 0.0000237 |
TRANSGLUTAMINASE | 0.0000647 | 4 | 0.6687992 | 0.0002069 |
MEK1_pS217S221 | 0.0001052 | 4 | 0.7620182 | 0.0003156 |
P70S6K_pT389 | 0.0001118 | 4 | -0.5501574 | 0.0003157 |
S6_pS235S236 | 0.0001459 | 1 | -0.6186327 | 0.0003891 |
TRANSGLUTAMINASE | 0.0001960 | 3 | -0.4678118 | 0.0004950 |
YAP_pS127 | 0.0004880 | 3 | -0.3415873 | 0.0011711 |
S6_pS235S236 | 0.0011507 | 3 | 0.2745295 | 0.0026301 |
P38_pT180Y182 | 0.0019148 | 1 | -0.4929049 | 0.0041777 |
YAP_pS127 | 0.0022614 | 1 | 0.5293986 | 0.0047195 |
BETACATENIN | 0.0025053 | 1 | 0.4534593 | 0.0050107 |
BETACATENIN | 0.0027574 | 4 | -0.8034237 | 0.0052942 |
AKT_pS473 | 0.0031525 | 2 | 0.3042622 | 0.0058201 |
BETACATENIN | 0.0082524 | 2 | 0.2740756 | 0.0145708 |
AKT_pS473 | 0.0084996 | 4 | -0.6089976 | 0.0145708 |
P53 | 0.0093318 | 4 | -0.4443350 | 0.0151240 |
S6_pS235S236 | 0.0094525 | 4 | 0.9038836 | 0.0151240 |
AKT_pT308 | 0.0225756 | 2 | 0.2259513 | 0.0346134 |
MEK1_pS217S221 | 0.0230756 | 2 | -0.2115988 | 0.0346134 |
P70S6K_pT389 | 0.0259921 | 2 | 0.2405353 | 0.0378067 |
tcga.nmf.rank <- nmfEstimateRank(as.matrix(t(tcga.rank)), seq(2,10), nrun = 20, seed = 42)
plot(tcga.nmf.rank)
tcga.nmf <- tcga.nmf.rank$fit[["9"]]
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.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 | Cluster4 | Cluster5 | Cluster6 | Cluster7 | Cluster8 | Cluster9 | AveExpr | F | P.Value | adj.P.Val | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AKT_pS473 | -0.3220759 | -0.3904936 | -0.3826254 | -0.4339399 | -0.6736179 | 0.6093029 | -0.4700041 | -0.2894932 | 0.2936120 | 0.0140382 | 25.188323 | 0.00e+00 | 0.00e+00 |
P53 | -0.2922492 | -0.4584418 | -0.4579182 | -0.5605988 | -0.5959401 | -0.3025761 | -0.2814704 | -0.2834671 | 0.4090562 | 0.0140382 | 22.400792 | 0.00e+00 | 0.00e+00 |
AKT_pT308 | -0.1724002 | -0.5425656 | -0.4053844 | -0.4893439 | -0.5940615 | 0.8693894 | -0.2443673 | -0.2725625 | 0.1529242 | 0.0140382 | 18.763284 | 0.00e+00 | 0.00e+00 |
JNK_pT183Y185 | -0.3826415 | -0.5191906 | -0.7144001 | -0.5982929 | -0.6215868 | -0.2385082 | 0.2673725 | -0.5131761 | 0.2663160 | 0.0140382 | 16.497419 | 0.00e+00 | 0.00e+00 |
P70S6K_pT389 | -0.1999527 | -0.4521321 | -0.4838440 | -0.4582817 | -0.5780100 | -0.2954114 | -0.2225336 | -0.3191828 | 0.3716099 | 0.0140382 | 16.311622 | 0.00e+00 | 0.00e+00 |
MEK1_pS217S221 | -0.1179460 | -0.1925672 | -0.1397102 | -0.4602432 | 0.0803592 | -0.3126890 | 0.6715251 | -0.5460719 | -0.0376882 | 0.0140382 | 12.560822 | 0.00e+00 | 0.00e+00 |
PDK1_pS241 | 0.2770698 | 0.6646545 | 0.0811439 | 0.1709092 | 0.2719001 | 0.3062457 | 0.1806304 | 0.1605935 | -0.2605341 | 0.0140863 | 8.539487 | 0.00e+00 | 0.00e+00 |
S6_pS235S236 | -0.0405195 | -0.6419346 | -0.4534233 | -0.4050666 | 0.6443719 | -0.0373028 | -0.0203922 | -0.4853911 | 0.1607298 | 0.0140382 | 7.085680 | 0.00e+00 | 0.00e+00 |
TRANSGLUTAMINASE | 0.6994046 | 0.0061055 | -0.2990139 | -0.7092480 | -0.0038815 | 0.0366546 | 0.1183758 | -0.0953301 | -0.0779632 | 0.0139842 | 5.084693 | 3.10e-06 | 4.20e-06 |
BETACATENIN | 0.0175635 | 0.3555484 | 0.2202937 | 0.1732127 | -0.1770933 | 0.1497450 | -0.2942520 | 0.7390227 | -0.0131377 | 0.0140382 | 4.694458 | 1.08e-05 | 1.29e-05 |
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 0 -1 1 -1
AKT_pT308 0 -1 -1 0 -1 1 -1
BETACATENIN 0 1 0 0 0 0 -1
JNK_pT183Y185 -1 -1 -1 -1 -1 -1 1
MEK1_pS217S221 0 0 0 0 0 -1 1
P38_pT180Y182 0 -1 1 0 0 0 0
P70S6K_pT389 0 -1 -1 0 -1 -1 -1
PDK1_pS241 1 1 0 0 0 1 1
S6_pS235S236 0 -1 -1 0 1 0 0
YAP_pS127 0 0 0 0 0 0 0
TRANSGLUTAMINASE 1 0 0 -1 0 0 0
Cluster8 Cluster9
P53 -1 1
AKT_pS473 -1 1
AKT_pT308 0 1
BETACATENIN 1 0
JNK_pT183Y185 -1 1
MEK1_pS217S221 -1 0
P38_pT180Y182 0 0
P70S6K_pT389 -1 1
PDK1_pS241 0 -1
S6_pS235S236 -1 1
YAP_pS127 1 -1
TRANSGLUTAMINASE 0 0
summary(tests.nmf)
Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Cluster8
Down 3 7 6 3 5 4 5 6
NotSig 7 3 5 9 6 5 4 4
Up 2 2 1 0 1 3 3 2
Cluster9
Down 2
NotSig 4
Up 6
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 14 -0.02
2 2 10 -0.10
3 3 6 -0.05
4 4 3 0.04
5 5 7 -0.11
6 6 17 -0.09
7 7 31 -0.11
8 8 8 0.06
9 9 88 0.30
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
names(which((tcga.core.samples$Cluster %>% table)>1)) %>% 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 | 9 | 0.7317530 | 0.0000000 |
P53 | 0.0000000 | 9 | 0.8032341 | 0.0000000 |
PDK1_pS241 | 0.0000000 | 9 | -0.6334421 | 0.0000000 |
TRANSGLUTAMINASE | 0.0000000 | 4 | -1.0562255 | 0.0000000 |
AKT_pS473 | 0.0000021 | 8 | -0.4277011 | 0.0000329 |
P70S6K_pT389 | 0.0000024 | 8 | -0.4217032 | 0.0000329 |
P53 | 0.0000157 | 8 | -0.4666429 | 0.0001650 |
S6_pS235S236 | 0.0000144 | 8 | -0.7272244 | 0.0001650 |
P70S6K_pT389 | 0.0000193 | 1 | -0.5192767 | 0.0001800 |
BETACATENIN | 0.0000517 | 8 | 0.7021493 | 0.0004344 |
AKT_pS473 | 0.0001435 | 1 | -0.4818315 | 0.0010568 |
YAP_pS127 | 0.0001510 | 4 | 0.6516795 | 0.0010568 |
JNK_pT183Y185 | 0.0002143 | 9 | 0.5482949 | 0.0013844 |
BETACATENIN | 0.0002869 | 7 | -0.8814987 | 0.0017214 |
AKT_pS473 | 0.0004402 | 6 | 1.2277923 | 0.0023110 |
AKT_pT308 | 0.0004387 | 6 | 1.2880988 | 0.0023110 |
MEK1_pS217S221 | 0.0007882 | 7 | 1.0517941 | 0.0038949 |
JNK_pT183Y185 | 0.0015404 | 8 | -0.5703655 | 0.0071884 |
JNK_pT183Y185 | 0.0018237 | 1 | -0.6179520 | 0.0080627 |
BETACATENIN | 0.0028594 | 4 | 0.1984077 | 0.0120095 |
TRANSGLUTAMINASE | 0.0031592 | 7 | 0.6179096 | 0.0126369 |
P53 | 0.0034732 | 6 | -0.7257314 | 0.0132613 |
PDK1_pS241 | 0.0038260 | 1 | 0.3887911 | 0.0133911 |
PDK1_pS241 | 0.0037113 | 8 | 0.3227224 | 0.0133911 |
P53 | 0.0049660 | 7 | -0.6153044 | 0.0166858 |
TRANSGLUTAMINASE | 0.0056889 | 1 | 0.9545623 | 0.0183796 |
AKT_pS473 | 0.0067895 | 7 | -1.0844259 | 0.0211229 |
P70S6K_pT389 | 0.0070736 | 7 | -0.5899212 | 0.0212208 |
AKT_pS473 | 0.0088958 | 9 | 0.4364604 | 0.0257670 |
P53 | 0.0094836 | 1 | -0.5418611 | 0.0265541 |
AKT_pT308 | 0.0098094 | 3 | -0.6195756 | 0.0265802 |
PDK1_pS241 | 0.0103135 | 7 | 0.7220394 | 0.0270730 |
P70S6K_pT389 | 0.0122957 | 6 | -0.6869836 | 0.0312980 |
S6_pS235S236 | 0.0151100 | 9 | 0.2843164 | 0.0373307 |
PDK1_pS241 | 0.0163368 | 4 | 0.3319152 | 0.0392083 |
MEK1_pS217S221 | 0.0185698 | 8 | -0.4822460 | 0.0433295 |
Repeat the analysis, but using 3 clusters.
tcga.nmf <- tcga.nmf <- tcga.nmf.rank$fit[["3"]]
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.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 | |
---|---|---|---|---|---|---|---|
P53 | -0.4414233 | -0.4485783 | 0.2956533 | 0.0140382 | 49.62915 | 0e+00 | 0e+00 |
AKT_pS473 | -0.5334368 | -0.3138352 | 0.2904909 | 0.0140382 | 49.52718 | 0e+00 | 0e+00 |
P70S6K_pT389 | -0.3765563 | -0.4171258 | 0.2649090 | 0.0140382 | 35.36002 | 0e+00 | 0e+00 |
MEK1_pS217S221 | 0.5545074 | -0.3643842 | -0.0679547 | 0.0140382 | 29.63664 | 0e+00 | 0e+00 |
JNK_pT183Y185 | -0.0595382 | -0.5230228 | 0.1852520 | 0.0140382 | 20.60883 | 0e+00 | 0e+00 |
BETACATENIN | -0.3085174 | 0.5078866 | -0.0099059 | 0.0140382 | 18.98657 | 0e+00 | 0e+00 |
PDK1_pS241 | 0.2734628 | 0.4012811 | -0.1799376 | 0.0140863 | 18.75428 | 0e+00 | 0e+00 |
S6_pS235S236 | 0.0790752 | -0.5132934 | 0.1351859 | 0.0140382 | 16.62621 | 0e+00 | 0e+00 |
AKT_pT308 | -0.2971225 | -0.2369574 | 0.1887412 | 0.0140382 | 14.32245 | 0e+00 | 0e+00 |
YAP_pS127 | 0.0589849 | 0.4566955 | -0.1217100 | 0.0140382 | 12.63077 | 1e-07 | 1e-07 |
tests.nmf <- decideTests(tcga.nmf.eb)
tests.nmf@.Data
Cluster1 Cluster2 Cluster3
P53 -1 -1 1
AKT_pS473 -1 -1 1
AKT_pT308 -1 -1 1
BETACATENIN -1 1 0
JNK_pT183Y185 0 -1 1
MEK1_pS217S221 1 -1 0
P38_pT180Y182 0 -1 1
P70S6K_pT389 -1 -1 1
PDK1_pS241 1 1 -1
S6_pS235S236 0 -1 1
YAP_pS127 0 1 -1
TRANSGLUTAMINASE 1 0 0
summary(tests.nmf)
Cluster1 Cluster2 Cluster3
Down 5 8 2
NotSig 4 1 3
Up 3 3 7
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 39 -0.04
2 2 31 0.15
3 3 114 0.33
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
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 | 0.8261853 | 0.0000000 |
P70S6K_pT389 | 0.0000000 | 3 | 0.7636210 | 0.0000000 |
AKT_pS473 | 0.0000000 | 3 | 0.8341263 | 0.0000000 |
JNK_pT183Y185 | 0.0000000 | 2 | -0.7426361 | 0.0000000 |
P53 | 0.0000000 | 2 | -0.7245959 | 0.0000000 |
AKT_pS473 | 0.0000000 | 2 | -0.6138768 | 0.0000000 |
P70S6K_pT389 | 0.0000000 | 2 | -0.6583089 | 0.0000000 |
S6_pS235S236 | 0.0000000 | 2 | -0.7278831 | 0.0000000 |
BETACATENIN | 0.0000000 | 2 | 0.6267117 | 0.0000000 |
PDK1_pS241 | 0.0000001 | 2 | 0.5861173 | 0.0000004 |
MEK1_pS217S221 | 0.0000007 | 1 | 0.9512471 | 0.0000022 |
P38_pT180Y182 | 0.0000011 | 3 | 0.5284230 | 0.0000032 |
AKT_pT308 | 0.0000020 | 3 | 0.5371256 | 0.0000054 |
P38_pT180Y182 | 0.0000027 | 2 | -0.5664680 | 0.0000069 |
JNK_pT183Y185 | 0.0000049 | 3 | 0.5255499 | 0.0000118 |
PDK1_pS241 | 0.0000081 | 3 | -0.5210833 | 0.0000181 |
AKT_pS473 | 0.0000109 | 1 | -1.0376493 | 0.0000230 |
P70S6K_pT389 | 0.0000241 | 1 | -0.7212306 | 0.0000482 |
YAP_pS127 | 0.0000988 | 3 | -0.5056444 | 0.0001872 |
P53 | 0.0001748 | 1 | -0.7509946 | 0.0003146 |
YAP_pS127 | 0.0002097 | 2 | 0.5573314 | 0.0003595 |
AKT_pT308 | 0.0003973 | 2 | -0.4833504 | 0.0006247 |
BETACATENIN | 0.0003991 | 1 | -0.8469248 | 0.0006247 |
S6_pS235S236 | 0.0004766 | 3 | 0.4551491 | 0.0007149 |
MEK1_pS217S221 | 0.0007290 | 2 | -0.3592819 | 0.0010497 |
TRANSGLUTAMINASE | 0.0072461 | 1 | 0.6227038 | 0.0100330 |
AKT_pT308 | 0.0122702 | 1 | -0.4591054 | 0.0163602 |
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] C
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
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[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
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[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
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