cores
Jovan Tanevski
2021-10-27
Last updated: 2021-10-27
Checks: 7 0
Knit directory: Multispectral HCC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a56dc0c | Jovan Tanevski | 2021-10-27 | remove beta-cat, add cores 3 and 4 clusters |
Setup
library(tidyverse)
library(skimr)
library(uwot)
library(limma)
library(NMF)
library(factoextra)
library(cowplot)
library(pheatmap)
library(RColorBrewer)data <- read_csv("data/tumor_hepatocytes.csv", col_types = cols())
tumor.hc <- data %>%
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 | 4 |
| _______________________ | |
| Column type frequency: | |
| numeric | 4 |
| ________________________ | |
| 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 | ▇▁▁▁▁ |
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(abs(x - lower[y]))]
x[x > upper[y]] <- x[which.min(abs(x - upper[y]))]
x
})
skim(tumor.hc.winsorized)| Name | tumor.hc.winsorized |
| Number of rows | 223846 |
| Number of columns | 4 |
| _______________________ | |
| Column type frequency: | |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| AGS | 0 | 1 | 0.19 | 0.16 | 0 | 0.07 | 0.12 | 0.25 | 0.52 | ▇▅▂▁▃ |
| BerEP4 | 0 | 1 | 0.46 | 0.35 | 0 | 0.21 | 0.32 | 0.58 | 1.15 | ▆▇▂▁▃ |
| CRP | 0 | 1 | 2.31 | 2.50 | 0 | 0.55 | 1.00 | 3.50 | 7.93 | ▇▂▁▁▂ |
| p.S6 | 0 | 1 | 1.21 | 0.83 | 0 | 0.58 | 0.94 | 1.61 | 3.16 | ▆▇▃▂▂ |
tumor.hc.norm <- normalizeQuantiles(data.frame(tumor.hc.winsorized))
skim(tumor.hc.norm)| Name | tumor.hc.norm |
| Number of rows | 223846 |
| Number of columns | 4 |
| _______________________ | |
| Column type frequency: | |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| AGS | 0 | 1 | 1.06 | 0.99 | 0 | 0.35 | 0.6 | 1.49 | 3.19 | ▇▃▁▁▂ |
| BerEP4 | 0 | 1 | 1.07 | 1.00 | 0 | 0.35 | 0.6 | 1.49 | 3.15 | ▇▃▂▁▂ |
| CRP | 0 | 1 | 1.04 | 0.96 | 0 | 0.35 | 0.6 | 1.49 | 3.19 | ▇▃▁▁▂ |
| p.S6 | 0 | 1 | 1.04 | 0.96 | 0 | 0.35 | 0.6 | 1.49 | 3.19 | ▇▃▁▁▂ |
tumor.hc.rank <- mutate_all(tumor.hc.winsorized, ~ rank(., ties.method = "min"))Pilot run. Subsample 10% of the original data.
set.seed(42)
percent <- 10
subsamp <- sample(nrow(tumor.hc.rank), (percent / 100) * nrow(tumor.hc.rank))Dimensionality reduction
cache <- "output/tumor.hc.umap.rds"
if (file.exists(cache)) {
tumor.hc.umap <- read_rds(cache)
} else {
tumor.hc.umap <- umap(tumor.hc.norm, n_neighbors = 100,
min_dist = 0.2, n_threads = 7)
write_rds(tumor.hc.umap, cache, "gz")
}Check if sample is representative in UMAP space
tumor.hc.umap.sample <-
tumor.hc.umap %>%
`colnames<-`(c("U1", "U2")) %>%
as_tibble()
all <- ggplot(tumor.hc.umap.sample, aes(x = U1, y = U2)) +
geom_point(size = 0.5) +
theme_classic()
sampled <- ggplot(tumor.hc.umap.sample %>% slice(subsamp), aes(x = U1, y = U2)) +
geom_point(color = "darkgreen", size = 0.5) +
theme_classic()
unsampled <- ggplot(tumor.hc.umap.sample %>% slice(-subsamp), aes(x = U1, y = U2)) +
geom_point(color = "darkred", size = 0.5) +
theme_classic()
plot_grid(all, sampled, unsampled)
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.
cache <- paste0("output/tumor.hc.nmf.rank.", percent, ".rds")
if (file.exists(cache)) {
tumor.hc.nmf.rank <- read_rds(cache)
} else {
tumor.hc.nmf.rank <- nmfEstimateRank(as.matrix(t(tumor.hc.rank[subsamp, ])),
range = seq(2, 5), method = "snmf/r",
nrun = 10, seed = 42, verbose = TRUE,
.options = "mp5"
)
write_rds(tumor.hc.nmf.rank, cache, "gz")
}
plot(tumor.hc.nmf.rank)Warning: Removed 2 rows containing missing values (geom_point).
tumor.hc.nmf <- tumor.hc.nmf.rank$fit[["4"]]
rm(tumor.hc.nmf.rank)Extract basis of NMF (signature of cluster)
basismap(tumor.hc.nmf)
Extract coefficients of NMF (soft clustering of samples) if a reasonable amount of cells is subselected.
if (percent <= 2) coefmap(tumor.hc.nmf)Assign clusters
nmf.clusters <- apply(tumor.hc.nmf@fit@H, 2, which.max)Plot in 2D
tumor.hc.umap.clus <-
tumor.hc.umap.sample %>%
slice(subsamp) %>%
mutate(Cluster = as.factor(nmf.clusters))
ggplot(tumor.hc.umap.clus, aes(x = U1, y = U2, color = Cluster)) +
geom_point(size = 0.5) +
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) +
scale_color_brewer(palette = "Set2") +
ylim(-1, 3) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)))
plot_grid(plotlist = profiles, labels = paste("Cluster", seq_len(max(nmf.clusters))))Warning: Removed 1532 rows containing non-finite values (stat_summary).Warning: Removed 1 rows containing missing values (geom_segment).Warning: Removed 2500 rows containing non-finite values (stat_summary).Warning: Removed 3221 rows containing non-finite values (stat_summary).Warning: Removed 1 rows containing missing values (geom_segment).Warning: Removed 2927 rows containing non-finite values (stat_summary).
Marker abundance plots
tumor.hc.umap.markers <- tumor.hc.norm %>%
bind_cols(tumor.hc.umap.sample) %>%
slice(subsamp)
low <- RColorBrewer::brewer.pal(8, "Set2")[8]
highs <- RColorBrewer::brewer.pal(8, "Set2")[seq_len(ncol(tumor.hc.norm))]
tumor.hc.umap.markers.plots <- colnames(tumor.hc.norm) %>%
map2(highs, \(marker, color){
ggplot(tumor.hc.umap.markers, aes_string(x = "U1", y = "U2", color = marker)) +
geom_point(size = 0.5) +
scale_color_gradient(low = low, high = color) +
theme_classic()
})
plot_grid(plotlist = tumor.hc.umap.markers.plots)
Core plots
tumor.hc.umap.cores <- data %>%
select(`Sample Name`) %>%
bind_cols(tumor.hc.umap.sample) %>%
slice(subsamp) %>%
mutate(
c = nmf.clusters,
sample = str_extract(`Sample Name`, "[0-9]+_[0-9]+")
)
tumor.hc.umap.cores %>%
group_by(sample) %>%
summarize(
Fraction = table(c) / n(),
Cluster = names(Fraction),
.groups = "drop"
) %>%
mutate(Fraction = as.numeric(Fraction)) %>%
pivot_wider(names_from = "Cluster", values_from = "Fraction") %>%
column_to_rownames("sample") %>%
mutate(across(everything(), ~ replace_na(., 0))) %>%
as.matrix() %>%
pheatmap(
scale = "none",
cellheight = 12,
color = colorRampPalette(brewer.pal(n = 7, name = "YlOrBr"))(100)
)
tumor.hc.umap.cores %>%
pull(sample) %>%
unique() %>%
walk(\(s){
output.fig <- paste0("output/cores_4/", s, ".png")
if (!file.exists(output.fig)) {
png(output.fig, width = 800, height = 800)
(ggplot(
tumor.hc.umap.cores %>%
mutate(c = ifelse(sample == s, c, NA), Cluster = as.factor(c)) %>%
arrange(!is.na(Cluster), Cluster),
aes(x = U1, y = U2, color = Cluster)
) +
geom_point(size = 0.5) +
scale_color_discrete(na.value = "gray80") +
theme_classic()) %>%
print()
dev.off()
}
})Figures with UMAPs for each core can be found in output.
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 4873 0.19
2 2 6142 0.31
3 3 5034 0.05
4 4 6335 0.31
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. Plot the differences.
de.table <- 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)
de.table %>%
filter(q <= 0.05) %>%
arrange(q)| Marker | p | Cluster | Difference | q |
|---|---|---|---|---|
| AGS | 0 | 4 | -0.9499835 | 0 |
| BerEP4 | 0 | 4 | 1.5084513 | 0 |
| CRP | 0 | 4 | -0.6417107 | 0 |
| BerEP4 | 0 | 1 | -0.8194855 | 0 |
| CRP | 0 | 1 | 1.0641100 | 0 |
| p.S6 | 0 | 3 | 2.0526531 | 0 |
| AGS | 0 | 2 | 1.4461681 | 0 |
| BerEP4 | 0 | 2 | -0.5484985 | 0 |
| CRP | 0 | 2 | -0.7260599 | 0 |
| p.S6 | 0 | 2 | -0.7265464 | 0 |
| p.S6 | 0 | 4 | -0.4302170 | 0 |
| AGS | 0 | 1 | -0.4419383 | 0 |
| CRP | 0 | 3 | 0.7819918 | 0 |
| BerEP4 | 0 | 3 | -0.4803686 | 0 |
| p.S6 | 0 | 1 | -0.1942013 | 0 |
| AGS | 0 | 3 | -0.1855235 | 0 |
de.table %>%
pivot_wider(names_from = "Cluster", values_from = "Difference", -c(p, q)) %>%
column_to_rownames("Marker") %>%
as.matrix() %>%
pheatmap(scale = "none")
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 pheatmap_1.0.12 cowplot_1.1.1
[4] factoextra_1.0.7 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_1.5.2 pkgmaker_0.32.2
[13] registry_0.5-1 limma_3.48.3 uwot_0.1.10
[16] Matrix_1.3-4 skimr_2.1.3 forcats_0.5.1
[19] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[22] readr_2.0.2 tidyr_1.1.4 tibble_3.1.5
[25] ggplot2_3.3.5 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_2.3.0 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 fansi_0.5.0
[16] lubridate_1.8.0 xml2_1.3.2 codetools_0.2-18
[19] splines_4.1.1 doParallel_1.0.16 knitr_1.36
[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 iterators_1.0.13
[46] xfun_0.27 openxlsx_4.2.4 rvest_1.0.2
[49] lifecycle_1.0.1 rstatix_0.7.0 scales_1.1.1
[52] vroom_1.5.5 hms_1.1.1 promises_1.2.0.1
[55] curl_4.3.2 yaml_2.2.1 gridExtra_2.3
[58] sass_0.4.0 rpart_4.1-15 latticeExtra_0.6-29
[61] stringi_1.7.5 highr_0.9 foreach_1.5.1
[64] checkmate_2.0.0 zip_2.2.0 repr_1.1.3
[67] rlang_0.4.12 pkgconfig_2.0.3 evaluate_0.14
[70] lattice_0.20-45 htmlwidgets_1.5.4 labeling_0.4.2
[73] bit_4.0.4 tidyselect_1.1.1 plyr_1.8.6
[76] magrittr_2.0.1 R6_2.5.1 generics_0.1.0
[79] Hmisc_4.6-0 DBI_1.1.1 foreign_0.8-81
[82] pillar_1.6.4 haven_2.4.3 whisker_0.4
[85] withr_2.4.2 abind_1.4-5 nnet_7.3-16
[88] survival_3.2-13 car_3.0-11 modelr_0.1.8
[91] crayon_1.4.1 uuid_0.1-4 utf8_1.2.2
[94] tzdb_0.1.2 rmarkdown_2.11 jpeg_0.1-9
[97] grid_4.1.1 readxl_1.3.1 data.table_1.14.2
[100] git2r_0.28.0 reprex_2.0.1 digest_0.6.28
[103] xtable_1.8-4 httpuv_1.6.3 munsell_0.5.0
[106] bslib_0.3.1