Analysis of TCGA RPPA LIHC samples
Jovan Tanevski
2021-08-03
Last updated: 2021-08-03
Checks: 7 0
Knit directory: Multispectral HCC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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)| 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
sorted.means <- apply(tcga.raw, 1, sort) %>% colMeans()
names(sorted.means) <- rank(sorted.means)
tcga.norm <- mutate_all(tcga.raw, ~ sorted.means[as.character(rank(.))])
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 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| AKT_pS473 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| AKT_pT308 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| BETACATENIN | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| JNK_pT183Y185 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| MEK1_pS217S221 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| P38_pT180Y182 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| P70S6K_pT389 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| PDK1_pS241 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| S6_pS235S236 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| YAP_pS127 | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
| TRANSGLUTAMINASE | 0 | 1 | 0.01 | 0.2 | -0.68 | -0.1 | 0.06 | 0.13 | 0.63 | ▁▃▇▆▁ |
tcga.rank <- mutate_all(tcga.raw, rank)Dimensionality reduction
tcga.pca <- prcomp(tcga.norm)
summary(tcga.pca)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 0.4038 0.2522 0.2157 0.19762 0.19025 0.16939 0.15692
Proportion of Variance 0.3550 0.1385 0.1013 0.08504 0.07882 0.06248 0.05362
Cumulative Proportion 0.3550 0.4935 0.5948 0.67980 0.75862 0.82110 0.87472
PC8 PC9 PC10 PC11 PC12
Standard deviation 0.13833 0.13333 0.09990 0.08501 0.05844
Proportion of Variance 0.04166 0.03871 0.02173 0.01574 0.00744
Cumulative Proportion 0.91638 0.95509 0.97683 0.99256 1.00000tcga.umap <- umap(tcga.norm, n_neighbors = 10, n_epochs = 1000)Hierarchical clustering
Perform hierarchical clustering of the data and plot the resulting dendrogram
tcga.hclust <- eclust(tcga.norm, "hclust", k = 6)Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.fviz_dend(tcga.hclust, rect = TRUE)Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.
#fviz_gap_stat(tcga.hclust$gap_stat)
fviz_silhouette(tcga.hclust) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) cluster size ave.sil.width
1 1 82 0.18
2 2 21 0.00
3 3 20 0.12
4 4 21 0.14
5 5 33 0.00
6 6 7 -0.01
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Plot in 2D PCA and UMAP
fviz_pca_ind(tcga.pca, geom = "point", col.ind = as.factor(tcga.hclust$cluster)) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
tcga.umap.clus <-
tcga.umap %>%
cbind(tcga.hclust$cluster) %>%
`colnames<-`(c("U1", "U2", "Cluster")) %>%
as_tibble() %>%
mutate_at("Cluster", as.factor)
ggplot(tcga.umap.clus, aes(x = U1, y = U2, color = Cluster)) +
geom_point() +
theme_classic()
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Expression profiles per cluster
tcga.clustered <- tcga.raw %>%
mutate(Cluster = as.factor(tcga.hclust$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 2 rows containing non-finite values (stat_summary).Warning: Removed 23 rows containing non-finite values (stat_summary).Warning: Removed 4 rows containing missing values (geom_segment).Warning: Removed 3 rows containing non-finite values (stat_summary).Warning: Removed 2 rows containing missing values (geom_segment).Warning: Removed 1 rows containing non-finite values (stat_summary).Warning: Removed 2 rows containing non-finite values (stat_summary).Warning: Removed 1 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).
| Version | Author | Date |
|---|---|---|
| c25dcca | Jovan Tanevski | 2021-08-02 |
Differential expression analysis with 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 Cluster4 Cluster5
P53 -0.81853456 -1.9728291 -1.34957363 -0.74815702 -1.35906356
YAP_pS127 2.02647828 3.0339519 2.09697253 1.93563539 2.41272242
P70S6K_pT389 -0.76752424 -2.4629435 -1.57688878 -0.55839403 -1.53985816
BETACATENIN 1.52521648 1.9768320 1.95573186 0.99194015 1.17977279
AKT_pS473 -0.27169830 -1.8269418 -1.02160833 -0.17498966 -1.05301660
PDK1_pS241 0.27956272 0.6802625 0.61401976 0.19940906 0.47342995
S6_pS235S236 -0.49873009 -1.6585656 -1.28105258 -0.34080340 -0.63892664
JNK_pT183Y185 -0.08655255 -0.6563026 -0.34991835 -0.05875654 -0.19999888
AKT_pT308 0.14372226 -0.4067163 -0.13355154 0.19638602 -0.04363602
P38_pT180Y182 0.57219116 0.1158460 0.01588039 0.88070826 0.47681020
Cluster6 AveExpr F P.Value adj.P.Val
P53 -1.6953849 -1.13026525 942.06423 3.292798e-134 3.951358e-133
YAP_pS127 2.0694465 2.20966288 571.85237 2.802263e-115 1.681358e-114
P70S6K_pT389 -2.1136125 -1.21485575 317.89567 1.172629e-93 4.690517e-93
BETACATENIN 1.5628928 1.50217041 171.13854 4.393233e-72 1.317970e-71
AKT_pS473 0.5060157 -0.63021426 142.44966 4.903972e-66 1.176953e-65
PDK1_pS241 0.6285290 0.40054637 83.80991 9.596449e-50 1.919290e-49
S6_pS235S236 -0.4666669 -0.72203758 55.91764 8.970505e-39 1.537801e-38
JNK_pT183Y185 -0.4861532 -0.21258126 52.37781 3.885665e-37 5.828498e-37
AKT_pT308 1.3833306 0.07032927 43.95006 6.024275e-33 8.032366e-33
P38_pT180Y182 0.7721723 0.48535253 35.13690 4.890317e-28 5.868380e-28tests.hclust <- decideTests(tcga.hclust.eb)
tests.hclust@.Data Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6
P53 -1 -1 -1 -1 -1 -1
AKT_pS473 -1 -1 -1 0 -1 1
AKT_pT308 1 -1 -1 1 0 1
BETACATENIN 1 1 1 1 1 1
JNK_pT183Y185 -1 -1 -1 0 -1 -1
MEK1_pS217S221 -1 0 -1 0 0 -1
P38_pT180Y182 1 0 0 1 1 1
P70S6K_pT389 -1 -1 -1 -1 -1 -1
PDK1_pS241 1 1 1 1 1 1
S6_pS235S236 -1 -1 -1 -1 -1 0
YAP_pS127 1 1 1 1 1 1
TRANSGLUTAMINASE -1 -1 -1 -1 0 0summary(tests.hclust) Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6
Down 7 7 8 4 5 4
NotSig 0 2 1 3 3 2
Up 5 3 3 5 4 6Similarity 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()
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.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 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 2 rows containing non-finite values (stat_summary).Warning: Removed 1 rows containing non-finite values (stat_summary).
Differential expression analysis
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 Cluster4 Cluster5
P53 -0.81853456 -1.9728291 -1.34957363 -0.74815702 -1.35906356
YAP_pS127 2.02647828 3.0339519 2.09697253 1.93563539 2.41272242
P70S6K_pT389 -0.76752424 -2.4629435 -1.57688878 -0.55839403 -1.53985816
BETACATENIN 1.52521648 1.9768320 1.95573186 0.99194015 1.17977279
AKT_pS473 -0.27169830 -1.8269418 -1.02160833 -0.17498966 -1.05301660
PDK1_pS241 0.27956272 0.6802625 0.61401976 0.19940906 0.47342995
S6_pS235S236 -0.49873009 -1.6585656 -1.28105258 -0.34080340 -0.63892664
JNK_pT183Y185 -0.08655255 -0.6563026 -0.34991835 -0.05875654 -0.19999888
AKT_pT308 0.14372226 -0.4067163 -0.13355154 0.19638602 -0.04363602
P38_pT180Y182 0.57219116 0.1158460 0.01588039 0.88070826 0.47681020
Cluster6 AveExpr F P.Value adj.P.Val
P53 -1.6953849 -1.13026525 942.06423 3.292798e-134 3.951358e-133
YAP_pS127 2.0694465 2.20966288 571.85237 2.802263e-115 1.681358e-114
P70S6K_pT389 -2.1136125 -1.21485575 317.89567 1.172629e-93 4.690517e-93
BETACATENIN 1.5628928 1.50217041 171.13854 4.393233e-72 1.317970e-71
AKT_pS473 0.5060157 -0.63021426 142.44966 4.903972e-66 1.176953e-65
PDK1_pS241 0.6285290 0.40054637 83.80991 9.596449e-50 1.919290e-49
S6_pS235S236 -0.4666669 -0.72203758 55.91764 8.970505e-39 1.537801e-38
JNK_pT183Y185 -0.4861532 -0.21258126 52.37781 3.885665e-37 5.828498e-37
AKT_pT308 1.3833306 0.07032927 43.95006 6.024275e-33 8.032366e-33
P38_pT180Y182 0.7721723 0.48535253 35.13690 4.890317e-28 5.868380e-28tests.leiden <- decideTests(tcga.leiden.eb)
tests.leiden@.Data Cluster1 Cluster2 Cluster3 Cluster4
P53 -1 -1 -1 -1
AKT_pS473 -1 -1 -1 -1
AKT_pT308 -1 1 0 1
BETACATENIN 1 1 1 1
JNK_pT183Y185 -1 -1 -1 0
MEK1_pS217S221 -1 -1 -1 0
P38_pT180Y182 1 1 1 1
P70S6K_pT389 -1 -1 -1 -1
PDK1_pS241 1 1 1 1
S6_pS235S236 -1 -1 -1 -1
YAP_pS127 1 1 1 1
TRANSGLUTAMINASE -1 -1 -1 -1summary(tests.leiden) Cluster1 Cluster2 Cluster3 Cluster4
Down 8 7 7 5
NotSig 0 0 1 2
Up 4 5 4 5Consensus NMF
tcga.nmf.rank <- nmfEstimateRank(as.matrix(t(tcga.rank)), seq(2,12), nrun = 10)
plot(tcga.nmf.rank)Warning: Removed 1 rows containing missing values (geom_point).
| Version | Author | Date |
|---|---|---|
| 55fb7fc | Jovan Tanevski | 2021-08-02 |
tcga.nmf <- nmf(as.matrix(t(tcga.rank)), 6, nrun = 10)Extract basis of NMF (signature of cluster)
basismap(tcga.nmf)
| Version | Author | Date |
|---|---|---|
| 55fb7fc | Jovan Tanevski | 2021-08-02 |
Extract coefficients of NMF (soft clustering of samples)
coefmap(tcga.nmf)
| Version | Author | Date |
|---|---|---|
| 55fb7fc | Jovan Tanevski | 2021-08-02 |
Check for signs of overfitting
consensusmap(tcga.nmf)
| Version | Author | Date |
|---|---|---|
| 55fb7fc | Jovan Tanevski | 2021-08-02 |
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 |
|---|---|---|
| 55fb7fc | Jovan Tanevski | 2021-08-02 |
ggplot(tcga.umap.clus %>% mutate(Cluster = as.factor(nmf.clusters)), aes(x = U1, y = U2, color = Cluster)) +
geom_point() +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 55fb7fc | Jovan Tanevski | 2021-08-02 |
Differential expression analysis
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
YAP_pS127 2.1669466 2.6346838 1.97932489 2.50812401 1.8835078
P53 -1.4304623 -1.5385675 -0.76462047 -1.42192857 -1.2592257
BETACATENIN 1.0289145 1.6266837 1.39191141 2.21519980 0.8743556
P70S6K_pT389 -1.7313384 -1.9147869 -0.67579019 -1.66431017 -1.4321143
PDK1_pS241 0.2869675 0.6233431 0.22696575 0.61545120 0.4634347
AKT_pS473 -1.0510982 -0.2240496 -0.16760173 -1.14207558 -1.1754947
S6_pS235S236 0.3211474 -1.0115932 -0.47211361 -1.33119424 -0.9696062
P38_pT180Y182 0.1560010 0.4185472 0.62130407 0.04148853 0.1819230
JNK_pT183Y185 -0.2805842 -0.4033670 -0.05755174 -0.40469993 0.0600595
TRANSGLUTAMINASE 0.2117830 -0.2387284 -0.54528210 -0.41489845 -0.1489069
Cluster6 AveExpr F P.Value adj.P.Val
YAP_pS127 2.3801914 2.2096629 484.08011 1.575811e-109 1.890974e-108
P53 -1.4474868 -1.1302653 408.09012 3.513724e-103 2.108234e-102
BETACATENIN 1.0729739 1.5021704 222.63071 2.327240e-81 9.308961e-81
P70S6K_pT389 -1.5678306 -1.2148558 168.63789 7.569716e-72 2.270915e-71
PDK1_pS241 0.5326748 0.4005464 87.42230 4.067236e-51 9.761367e-51
AKT_pS473 -1.0879843 -0.6302143 67.80680 6.084641e-44 1.216928e-43
S6_pS235S236 -0.8043381 -0.7220376 59.92689 1.183764e-40 2.029309e-40
P38_pT180Y182 1.0238573 0.4853525 48.26332 3.152630e-35 4.728945e-35
JNK_pT183Y185 -0.3560675 -0.2125813 38.41788 5.351536e-30 7.135381e-30
TRANSGLUTAMINASE -0.4989314 -0.4221190 23.90494 8.492965e-21 1.019156e-20tests.nmf <- decideTests(tcga.nmf.eb)
tests.nmf@.Data Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6
P53 -1 -1 -1 -1 -1 -1
AKT_pS473 -1 0 -1 -1 -1 -1
AKT_pT308 0 1 1 -1 0 0
BETACATENIN 1 1 1 1 1 1
JNK_pT183Y185 -1 -1 -1 -1 0 -1
MEK1_pS217S221 0 -1 -1 -1 1 0
P38_pT180Y182 0 1 1 0 0 1
P70S6K_pT389 -1 -1 -1 -1 -1 -1
PDK1_pS241 1 1 1 1 1 1
S6_pS235S236 0 -1 -1 -1 -1 -1
YAP_pS127 1 1 1 1 1 1
TRANSGLUTAMINASE 0 0 -1 -1 0 -1summary(tests.nmf) Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6
Down 4 5 7 8 4 6
NotSig 5 2 0 1 4 2
Up 3 5 5 3 4 4
sessionInfo()R version 4.1.0 (2021-05-18)
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.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 Biobase_2.52.0
[7] BiocGenerics_0.38.0 cluster_2.1.2 rngtools_1.5
[10] pkgmaker_0.32.2 registry_0.5-1 limma_3.48.1
[13] leiden_0.3.9 igraph_1.2.6 FNN_1.1.3
[16] cowplot_1.1.1 factoextra_1.0.7 uwot_0.1.10
[19] Matrix_1.3-4 skimr_2.1.3 forcats_0.5.1
[22] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[25] readr_2.0.0 tidyr_1.1.3 tibble_3.1.3
[28] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 Hmisc_4.5-0
[4] plyr_1.8.6 repr_1.1.3 splines_4.1.0
[7] gridBase_0.4-7 digest_0.6.27 htmltools_0.5.1.1
[10] viridis_0.6.1 fansi_0.5.0 checkmate_2.0.0
[13] magrittr_2.0.1 tzdb_0.1.2 openxlsx_4.2.4
[16] modelr_0.1.8 vroom_1.5.3 jpeg_0.1-9
[19] colorspace_2.0-2 rvest_1.0.1 ggrepel_0.9.1
[22] haven_2.4.1 xfun_0.24 crayon_1.4.1
[25] jsonlite_1.7.2 survival_3.2-11 glue_1.4.2
[28] gtable_0.3.0 car_3.0-11 abind_1.4-5
[31] scales_1.1.1 DBI_1.1.1 rstatix_0.7.0
[34] Rcpp_1.0.7 htmlTable_2.2.1 viridisLite_0.4.0
[37] xtable_1.8-4 reticulate_1.20 foreign_0.8-81
[40] bit_4.0.4 Formula_1.2-4 htmlwidgets_1.5.3
[43] httr_1.4.2 ellipsis_0.3.2 pkgconfig_2.0.3
[46] farver_2.1.0 nnet_7.3-16 sass_0.4.0
[49] dbplyr_2.1.1 utf8_1.2.2 tidyselect_1.1.1
[52] labeling_0.4.2 rlang_0.4.11 reshape2_1.4.4
[55] later_1.2.0 munsell_0.5.0 cellranger_1.1.0
[58] tools_4.1.0 cli_3.0.1 generics_0.1.0
[61] broom_0.7.9 evaluate_0.14 yaml_2.2.1
[64] knitr_1.33 bit64_4.0.5 fs_1.5.0
[67] zip_2.2.0 dendextend_1.15.1 whisker_0.4
[70] xml2_1.3.2 compiler_4.1.0 rstudioapi_0.13
[73] curl_4.3.2 png_0.1-7 ggsignif_0.6.2
[76] reprex_2.0.0 bslib_0.2.5.1 stringi_1.7.3
[79] highr_0.9 RSpectra_0.16-0 lattice_0.20-44
[82] vctrs_0.3.8 pillar_1.6.2 lifecycle_1.0.0
[85] jquerylib_0.1.4 data.table_1.14.0 httpuv_1.6.1
[88] R6_2.5.0 latticeExtra_0.6-29 promises_1.2.0.1
[91] gridExtra_2.3 rio_0.5.27 codetools_0.2-18
[94] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2
[97] hms_1.1.0 grid_4.1.0 rpart_4.1-15
[100] rmarkdown_2.9 carData_3.0-4 git2r_0.28.0
[103] ggpubr_0.4.0 lubridate_1.7.10 base64enc_0.1-3