cores
Jovan Tanevski
2021-09-17
Last updated: 2021-09-17
Checks: 7 0
Knit directory: Multispectral HCC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 201876f | Jovan Tanevski | 2021-09-17 | work with normalized data for profiles and de |
| Rmd | b67241d | Jovan Tanevski | 2021-09-17 | switch to snmf/r and lower rank |
| html | c85e3e6 | Jovan Tanevski | 2021-09-17 | Build site. |
| Rmd | 825c1f2 | Jovan Tanevski | 2021-09-17 | pilot run on 1% of hepatocytes from tumor cores |
| html | e7709fe | Jovan Tanevski | 2021-08-03 | Build site. |
| Rmd | 421cbc4 | Jovan Tanevski | 2021-08-03 | add cores analysis placeholder |
Setup
library(tidyverse)
library(skimr)
library(uwot)
library(limma)
library(NMF)
library(factoextra)
library(cowplot)tumor.hc <- read_csv("data/tumor_hepatocytes.csv", col_types = cols()) %>%
select(
`Cytoplasm AGS (Opal 690) Mean (Normalized Counts, Total Weighting)`,
`Cytoplasm BerEP4 (Opal 650) Mean (Normalized Counts, Total Weighting)`,
`Cytoplasm CRP (Opal 540) Mean (Normalized Counts, Total Weighting)`,
`Nucleus p-S6 (Opal 570) Mean (Normalized Counts, Total Weighting)`,
`Nucleus beta-cat. (Opal 520) Mean (Normalized Counts, Total Weighting)`
) %>% `colnames<-`(str_split(colnames(.)," ") %>% map_chr(~.x[2]) %>% make.names)
skim(tumor.hc)| Name | tumor.hc |
| Number of rows | 223846 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| AGS | 0 | 1 | 0.27 | 0.41 | 0 | 0.07 | 0.12 | 0.25 | 5.23 | ▇▁▁▁▁ |
| BerEP4 | 0 | 1 | 1.09 | 2.19 | 0 | 0.21 | 0.32 | 0.58 | 24.08 | ▇▁▁▁▁ |
| CRP | 0 | 1 | 2.51 | 3.08 | 0 | 0.55 | 1.00 | 3.50 | 35.80 | ▇▁▁▁▁ |
| p.S6 | 0 | 1 | 1.31 | 1.25 | 0 | 0.58 | 0.94 | 1.61 | 35.24 | ▇▁▁▁▁ |
| beta.cat. | 0 | 1 | 0.06 | 0.14 | 0 | 0.00 | 0.00 | 0.05 | 4.04 | ▇▁▁▁▁ |
Detect outliers based on Tukey’s interquartile approach and winsorize. Follow by quantile normalization and ranking to get rid of the effect of abundance
quartiles <-apply(tumor.hc, 2, \(x) quantile(x, c(.25,.75)))
lower <- quartiles[1,] - 1.5*(quartiles[2,] - quartiles[1,])
upper <- quartiles[2,] + 1.5*(quartiles[2,] - quartiles[1,])
tumor.hc.winsorized <- tumor.hc %>% imap_dfc(\(x,y){
x[x<lower[y]] <- x[which.min(x - lower[y])]
x[x>upper[y]] <- x[which.min(x - upper[y])]
x
})
skim(tumor.hc.winsorized)| Name | tumor.hc.winsorized |
| Number of rows | 223846 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| AGS | 0 | 1 | 0.12 | 0.11 | 0 | 0.05 | 0.10 | 0.17 | 0.52 | ▇▃▂▁▁ |
| BerEP4 | 0 | 1 | 0.29 | 0.22 | 0 | 0.14 | 0.25 | 0.39 | 1.15 | ▇▆▂▁▁ |
| CRP | 0 | 1 | 1.67 | 1.93 | 0 | 0.45 | 0.82 | 2.12 | 7.93 | ▇▁▁▁▁ |
| p.S6 | 0 | 1 | 1.01 | 0.71 | 0 | 0.50 | 0.83 | 1.38 | 3.16 | ▇▇▃▂▁ |
| beta.cat. | 0 | 1 | 0.01 | 0.03 | 0 | 0.00 | 0.00 | 0.00 | 0.13 | ▇▁▁▁▁ |
tumor.hc.norm <- normalizeQuantiles(data.frame(tumor.hc.winsorized))
skim(tumor.hc.norm)| Name | tumor.hc.norm |
| Number of rows | 223846 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| AGS | 0 | 1 | 0.61 | 0.60 | 0.00 | 0.23 | 0.40 | 0.81 | 2.58 | ▇▂▁▁▁ |
| BerEP4 | 0 | 1 | 0.62 | 0.60 | 0.03 | 0.23 | 0.40 | 0.81 | 2.58 | ▇▂▁▁▁ |
| CRP | 0 | 1 | 0.62 | 0.59 | 0.00 | 0.23 | 0.40 | 0.81 | 2.58 | ▇▂▁▁▁ |
| p.S6 | 0 | 1 | 0.62 | 0.59 | 0.00 | 0.23 | 0.40 | 0.81 | 2.58 | ▇▂▁▁▁ |
| beta.cat. | 0 | 1 | 0.55 | 0.54 | 0.35 | 0.35 | 0.35 | 0.35 | 2.57 | ▇▁▁▁▁ |
tumor.hc.rank <- mutate_all(tumor.hc.winsorized, ~rank(., ties.method = "min"))Pilot run. Subsample 1% of the original data.
set.seed(42)
subsamp <- sample(nrow(tumor.hc.rank), 0.01*nrow(tumor.hc.rank))Dimensionality reduction
tumor.hc.pca <- prcomp(tumor.hc.norm[subsamp,])
summary(tumor.hc.pca)Importance of components:
PC1 PC2 PC3 PC4 PC5
Standard deviation 0.6882 0.6099 0.5817 0.5112 0.4843
Proportion of Variance 0.2819 0.2214 0.2014 0.1556 0.1396
Cumulative Proportion 0.2819 0.5033 0.7048 0.8604 1.0000tumor.hc.umap <- umap(tumor.hc.norm[subsamp, ], n_threads = 7)Consensus NMF
We use an efficient implementation of alternating non negative least-squares with regularized to favor sparse coefficient matrices snmf/r. In this way we aim for cleaner clustering.
tumor.hc.nmf.rank <- nmfEstimateRank(as.matrix(t(tumor.hc.rank[subsamp,])),
range = seq(2,6), method = "snmf/r",
nrun = 10, seed = 42)
plot(tumor.hc.nmf.rank)Warning: Removed 2 rows containing missing values (geom_point).
| Version | Author | Date |
|---|---|---|
| c85e3e6 | Jovan Tanevski | 2021-09-17 |
tumor.hc.nmf <- nmf(as.matrix(t(tumor.hc.rank[subsamp,])),
rank = 3, method = "snmf/r",
nrun = 10, seed = 42)Extract basis of NMF (signature of cluster)
basismap(tumor.hc.nmf)
| Version | Author | Date |
|---|---|---|
| c85e3e6 | Jovan Tanevski | 2021-09-17 |
Extract coefficients of NMF (soft clustering of samples)
coefmap(tumor.hc.nmf)
| Version | Author | Date |
|---|---|---|
| c85e3e6 | Jovan Tanevski | 2021-09-17 |
Assign clusters
nmf.clusters <- apply(tumor.hc.nmf@fit@H, 2, which.max)Plot in 2D PCA and UMAP
fviz_pca_ind(tumor.hc.pca, geom = "point", col.ind = as.factor(nmf.clusters)) +
theme_classic()
tumor.hc.umap.clus <-
tumor.hc.umap %>%
cbind(nmf.clusters) %>%
`colnames<-`(c("U1", "U2", "Cluster")) %>%
as_tibble() %>%
mutate_at("Cluster", as.factor)
ggplot(tumor.hc.umap.clus, aes(x = U1, y = U2, color = Cluster)) +
geom_point() +
theme_classic()
Expression profiles per cluster
tumor.hc.clustered.nmf <- tumor.hc.norm[subsamp, ] %>%
mutate(Cluster = as.factor(nmf.clusters)) %>%
pivot_longer(names_to = "Marker", values_to = "Norm.value", -Cluster)
profiles <- seq_len(max(nmf.clusters)) %>% map(~
ggplot(
tumor.hc.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 |
|---|---|---|
| c85e3e6 | Jovan Tanevski | 2021-09-17 |
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(tumor.hc.norm[subsamp,]))
fviz_silhouette(silhouette.nmf) cluster size ave.sil.width
1 1 834 0.21
2 2 276 0.21
3 3 1128 0.10
| Version | Author | Date |
|---|---|---|
| c85e3e6 | Jovan Tanevski | 2021-09-17 |
Select only the samples with positive silhouette scores as “core samples”
core.samples <- which(silhouette.nmf[,3] > 0)
tumor.hc.core.samples <- tumor.hc.norm[subsamp,] %>% 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(tumor.hc.core.samples$Cluster) %>% map_dfr(\(c){
tumor.hc.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 = tumor.hc.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 |
|---|---|---|---|---|
| beta.cat. | 0 | 2 | 1.5812421 | 0 |
| BerEP4 | 0 | 3 | -0.6642024 | 0 |
| p.S6 | 0 | 1 | -0.5883269 | 0 |
| p.S6 | 0 | 3 | 0.6729983 | 0 |
| CRP | 0 | 1 | -0.4360204 | 0 |
| CRP | 0 | 3 | 0.4639126 | 0 |
| BerEP4 | 0 | 1 | 0.4870888 | 0 |
| beta.cat. | 0 | 1 | -0.3628184 | 0 |
| beta.cat. | 0 | 3 | -0.3783264 | 0 |
| BerEP4 | 0 | 2 | 0.3956980 | 0 |
| p.S6 | 0 | 2 | -0.2002105 | 0 |
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 cowplot_1.1.1 factoextra_1.0.7
[7] NMF_0.23.0 synchronicity_1.3.5 bigmemory_4.5.36
[10] Biobase_2.52.0 BiocGenerics_0.38.0 cluster_2.1.2
[13] rngtools_1.5 pkgmaker_0.32.2 registry_0.5-1
[16] limma_3.48.3 uwot_0.1.10 Matrix_1.3-4
[19] skimr_2.1.3 forcats_0.5.1 stringr_1.4.0
[22] dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
[25] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5
[28] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] bigmemory.sri_0.1.3 colorspace_2.0-2 ggsignif_0.6.3
[4] rio_0.5.27 ellipsis_0.3.2 rprojroot_2.0.2
[7] htmlTable_2.2.1 base64enc_0.1-3 fs_1.5.0
[10] rstudioapi_0.13 ggpubr_0.4.0 farver_2.1.0
[13] ggrepel_0.9.1 bit64_4.0.5 RSpectra_0.16-0
[16] fansi_0.5.0 lubridate_1.7.10 xml2_1.3.2
[19] splines_4.1.1 codetools_0.2-18 knitr_1.34
[22] Formula_1.2-4 jsonlite_1.7.2 broom_0.7.9
[25] gridBase_0.4-7 dbplyr_2.1.1 png_0.1-7
[28] compiler_4.1.1 httr_1.4.2 backports_1.2.1
[31] assertthat_0.2.1 fastmap_1.1.0 cli_3.0.1
[34] later_1.3.0 htmltools_0.5.2 tools_4.1.1
[37] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
[40] Rcpp_1.0.7 carData_3.0-4 cellranger_1.1.0
[43] jquerylib_0.1.4 vctrs_0.3.8 xfun_0.26
[46] openxlsx_4.2.4 rvest_1.0.1 lifecycle_1.0.0
[49] rstatix_0.7.0 scales_1.1.1 vroom_1.5.5
[52] hms_1.1.0 promises_1.2.0.1 curl_4.3.2
[55] yaml_2.2.1 gridExtra_2.3 sass_0.4.0
[58] rpart_4.1-15 latticeExtra_0.6-29 stringi_1.7.4
[61] highr_0.9 checkmate_2.0.0 zip_2.2.0
[64] repr_1.1.3 rlang_0.4.11 pkgconfig_2.0.3
[67] evaluate_0.14 lattice_0.20-44 htmlwidgets_1.5.4
[70] labeling_0.4.2 bit_4.0.4 tidyselect_1.1.1
[73] plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
[76] Hmisc_4.5-0 generics_0.1.0 DBI_1.1.1
[79] foreign_0.8-81 pillar_1.6.2 haven_2.4.3
[82] whisker_0.4 withr_2.4.2 nnet_7.3-16
[85] survival_3.2-13 abind_1.4-5 car_3.0-11
[88] modelr_0.1.8 crayon_1.4.1 uuid_0.1-4
[91] utf8_1.2.2 tzdb_0.1.2 rmarkdown_2.11
[94] jpeg_0.1-9 grid_4.1.1 readxl_1.3.1
[97] data.table_1.14.0 FNN_1.1.3 git2r_0.28.0
[100] reprex_2.0.1 digest_0.6.27 xtable_1.8-4
[103] httpuv_1.6.3 munsell_0.5.0 bslib_0.3.0