Analysis of TCGA RPPA LIHC samples

Last updated: 2021-09-17

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Knit directory: Multispectral HCC/

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File	Version	Author	Date	Message
Rmd	410dc90	Jovan Tanevski	2021-09-17	fix issue with low number of examples
Rmd	201876f	Jovan Tanevski	2021-09-17	work with normalized data for profiles and de
html	c85e3e6	Jovan Tanevski	2021-09-17	Build site.
Rmd	b8745a8	Jovan Tanevski	2021-09-17	fix typos
html	ae61865	Jovan Tanevski	2021-09-16	Build site.
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
html	e7709fe	Jovan Tanevski	2021-08-03	Build site.
html	f791e00	Jovan Tanevski	2021-08-03	Build site.
Rmd	e464694	Jovan Tanevski	2021-08-03	fix seed, stabilize nmf
html	51e2651	Jovan Tanevski	2021-08-03	Build site.
Rmd	7d62e48	Jovan Tanevski	2021-08-03	hclust and leiden on quantile normalized data
html	55fb7fc	Jovan Tanevski	2021-08-02	Build site.
Rmd	a152862	Jovan Tanevski	2021-08-02	add NMF based analysis
html	c25dcca	Jovan Tanevski	2021-08-02	Build site.
Rmd	21cf743	Jovan Tanevski	2021-08-02	rank normalization, leiden analysis, diff exp
html	a386eaa	Jovan Tanevski	2021-07-28	Build site.
Rmd	9ab4acc	Jovan Tanevski	2021-07-28	Build site.
Rmd	bd05dbf	Jovan Tanevski	2021-07-28	add expression profiles
html	e45bf1d	Jovan Tanevski	2021-07-28	Build site.
Rmd	dcab1bf	Jovan Tanevski	2021-07-28	set figure output to svg
html	33e71a0	Jovan Tanevski	2021-07-28	Build site.
Rmd	bb998b8	Jovan Tanevski	2021-07-28	hclust with factoextra
html	1cb80af	Jovan Tanevski	2021-07-28	Build site.
Rmd	d0b6e0f	Jovan Tanevski	2021-07-28	add pca to tcga analysis
html	d73f586	Jovan Tanevski	2021-07-28	Build site.
Rmd	c4324f5	Jovan Tanevski	2021-07-28	add umap
html	0bfae68	Jovan Tanevski	2021-07-28	Build site.
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	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
P53	1	-1.13	0.46	-2.54	-1.40	-0.96	-0.76	-0.51	▁▂▂▅▇
AKT_pS473	1	-0.63	0.71	-2.76	-1.02	-0.41	-0.15	0.96	▁▂▃▇▁
AKT_pT308	1	0.07	0.42	-1.16	-0.07	0.09	0.22	1.72	▁▃▇▁▁
BETACATENIN	1	1.50	0.72	-1.30	1.08	1.51	1.87	3.70	▁▁▇▅▁
JNK_pT183Y185	1	-0.21	0.29	-1.14	-0.36	-0.17	-0.04	0.48	▁▂▇▇▂
MEK1_pS217S221	1	-0.17	0.36	-0.82	-0.35	-0.23	-0.10	2.58	▇▃▁▁▁
P38_pT180Y182	1	0.49	0.57	-1.45	0.31	0.50	0.72	2.95	▁▂▇▁▁
P70S6K_pT389	1	-1.21	0.75	-3.22	-1.58	-0.95	-0.65	1.09	▂▃▇▃▁
PDK1_pS241	1	0.40	0.31	-0.56	0.23	0.37	0.55	1.62	▁▇▇▂▁
S6_pS235S236	1	-0.72	0.75	-3.53	-1.06	-0.67	-0.36	1.12	▁▁▅▇▂
YAP_pS127	1	2.21	0.61	0.68	1.78	2.10	2.54	4.05	▁▇▇▂▁
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	12
_______________________
Column type frequency:
numeric	12
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
P53	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
AKT_pS473	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
AKT_pT308	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
BETACATENIN	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
JNK_pT183Y185	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
MEK1_pS217S221	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
P38_pT180Y182	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
P70S6K_pT389	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
PDK1_pS241	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
S6_pS235S236	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
YAP_pS127	1	0.01	0.52	-1.59	-0.27	0.05	0.3	1.87	▁▃▇▂▁
TRANSGLUTAMINASE	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.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)))

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.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

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
ae61865	Jovan Tanevski	2021-09-16
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
ae61865	Jovan Tanevski	2021-09-16
9a18eda	Jovan Tanevski	2021-09-16

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))))

Version	Author	Date
ae61865	Jovan Tanevski	2021-09-16
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.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
P53                    -1        1        1
AKT_pS473              -1        1        1
AKT_pT308              -1        0        1
BETACATENIN             0        1       -1
JNK_pT183Y185          -1        0        1
MEK1_pS217S221          0        0        0
P38_pT180Y182          -1        0        1
P70S6K_pT389           -1        1        1
PDK1_pS241              1        0       -1
S6_pS235S236           -1        0        1
YAP_pS127               1        0       -1
TRANSGLUTAMINASE        1        0       -1

summary(tests.leiden)

       Cluster1 Cluster2 Cluster3
Down          7        0        4
NotSig        2        8        1
Up            3        4        7

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   75         -0.08
2       2   60          0.19
3       3   49          0.02

Version	Author	Date
ae61865	Jovan Tanevski	2021-09-16
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.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
P70S6K_pT389	0.0000000	1	-1.0874717	0.0000000
AKT_pS473	0.0000000	1	-0.9967265	0.0000000
P53	0.0000000	1	-1.0804359	0.0000000
P53	0.0000000	3	0.7685440	0.0000000
P70S6K_pT389	0.0000000	3	0.7063237	0.0000000
BETACATENIN	0.0000000	3	-0.6289288	0.0000000
AKT_pS473	0.0000000	3	0.5438691	0.0000002
JNK_pT183Y185	0.0000002	3	0.6394211	0.0000007
PDK1_pS241	0.0000002	3	-0.6011500	0.0000007
P38_pT180Y182	0.0000006	3	0.5057055	0.0000021
JNK_pT183Y185	0.0000007	1	-0.8366643	0.0000024
PDK1_pS241	0.0000014	1	0.6549129	0.0000042
AKT_pT308	0.0000019	1	-0.7738529	0.0000053
AKT_pT308	0.0000039	3	0.3788258	0.0000099
P38_pT180Y182	0.0000400	1	-0.6335631	0.0000960
S6_pS235S236	0.0000735	3	0.3195876	0.0001653
TRANSGLUTAMINASE	0.0003745	3	-0.4615017	0.0007932
S6_pS235S236	0.0019327	1	-0.4625113	0.0038654
YAP_pS127	0.0039729	3	-0.2848829	0.0075277
YAP_pS127	0.0076809	1	0.4923553	0.0138256
AKT_pS473	0.0095635	2	0.2786417	0.0163946
TRANSGLUTAMINASE	0.0104905	1	0.3641892	0.0171662
BETACATENIN	0.0147928	2	0.2588295	0.0231540
AKT_pT308	0.0182048	2	0.2486818	0.0273072

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.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
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.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

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

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

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.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
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.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

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

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] 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

Analysis of TCGA RPPA LIHC samples

Jovan Tanevski

2021-09-17

Setup

Dimensionality reduction

Hierarchical clustering

Similarity and graph based clustering

Consensus NMF (9 clusters)

Consensus NMF (3 clusters)