cores
Jovan Tanevski
2021-11-12
Last updated: 2021-11-12
Checks: 7 0
Knit directory: Multispectral HCC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f582507 | Jovan Tanevski | 2021-11-12 | pch 21 in cores, increase resolution |
| html | f041ce0 | Jovan Tanevski | 2021-11-12 | Build site. |
| Rmd | 1a7cca3 | Jovan Tanevski | 2021-11-12 | wflow_publish("analysis/cores_3.Rmd") |
| Rmd | a52096d | Jovan Tanevski | 2021-11-12 | overlay tifs with cluster info, manual silhouette calculation |
| Rmd | baf95ea | Jovan Tanevski | 2021-11-12 | keep all residuals, fix object access |
| Rmd | 6ff2531 | Jovan Tanevski | 2021-11-11 | small fix |
| Rmd | e98088d | Jovan Tanevski | 2021-11-11 | core analysis of all hepatocytes for one core per patient |
| html | 515f7e6 | Jovan Tanevski | 2021-10-28 | Build site. |
| html | 434cb0d | Jovan Tanevski | 2021-10-27 | Build site. |
| html | a915f46 | Jovan Tanevski | 2021-10-27 | Build site. |
| Rmd | a56dc0c | Jovan Tanevski | 2021-10-27 | remove beta-cat, add cores 3 and 4 clusters |
Setup
library(skimr)
library(uwot)
library(limma)
library(NMF)
library(cowplot)
library(pheatmap)
library(RColorBrewer)
library(distances)
library(furrr)
library(raster)
library(RStoolbox)
library(tidyverse)data <- read_csv("data/tumor_hepatocytes.csv", col_types = cols())Warning: One or more parsing issues, see `problems()` for detailstumor.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 one core per patient from the original data.
set.seed(42)
selected.cores <- data %>% select(`Sample Name`) %>%
mutate(sample = str_extract(`Sample Name`, "[0-9]{2}_[0-9]+")) %>%
group_by(`Sample Name`) %>% distinct() %>% ungroup() %>%
group_by(sample) %>% slice_sample() %>% pull(`Sample Name`)
subsamp <- which(data %>% pull(`Sample Name`) %in% selected.cores)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")
}
tumor.hc.umap.sample <-
tumor.hc.umap %>%
`colnames<-`(c("U1", "U2")) %>%
as_tibble()Check if sample is representative in UMAP space
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 <- "output/tumor.hc.nmf.all.3.rds"
if (file.exists(cache)) {
tumor.hc.nmf <- read_rds(cache)
} else {
tumor.hc.nmf <- nmf(as.matrix(t(tumor.hc.rank[subsamp, ])),
rank = 3, method = "snmf/r",
nrun = 10, seed = 42, verbose = TRUE,
.options = "vkp10-m"
)
write_rds(tumor.hc.nmf, cache, "gz")
}Extract basis of NMF (signature of cluster)
basismap(tumor.hc.nmf)
Assign clusters
nmf.clusters <- apply(fit(tumor.hc.nmf)@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()
| Version | Author | Date |
|---|---|---|
| f041ce0 | Jovan Tanevski | 2021-11-12 |
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 14390 rows containing non-finite values (stat_summary).Warning: Removed 16165 rows containing non-finite values (stat_summary).Warning: Removed 24968 rows containing non-finite values (stat_summary).Warning: Removed 1 rows containing missing values (geom_segment).
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",
color = colorRampPalette(brewer.pal(n = 7, name = "YlOrBr"))(100),
fontsize = 6
)
tumor.hc.umap.cores %>%
pull(sample) %>%
unique() %>%
walk(\(s){
output.fig <- paste0("output/cores_3/", 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.
Overlay cluster information on available tiffs
available.images <- list.files("data/core images/", full.names = TRUE)
spatial <- data %>%
select(`Sample Name`, `Cell X Position`, `Cell Y Position`) %>%
`colnames<-`(c("sample", "X", "Y")) %>%
slice(subsamp) %>%
mutate(Cluster = as.factor(nmf.clusters))
available.images %>% walk(\(img){
id <- str_extract(img, "[0-9]{2}_[0-9]+(_[^_\\.]*){4}")
name <- str_extract(img, "[0-9]{2}_[0-9]+(_[^_\\.]*)*.tif")
s <- paste0(id,".im3")
if(s %in% spatial$sample){
rb <- brick(img)
names(rb) <- c("r", "g", "b")
pdf(paste0("output/cores_3/", name, ".pdf"))
#the t should be flipped along the y direction to match coordinates in spatial
(ggRGB(flip(rb, "y"), maxpixels = 1e8) +
geom_point(data = spatial %>% filter(sample == s),
aes(x = X, y = Y, color = Cluster), pch = 21) +
theme_map()) %>%
print()
dev.off()
}
})Differential expression analysis (silhouette)
Calculate the similarity of samples using the expression and the silhouette scores based on the assigned clusters.
cache <- "output/silhouette.nmf.rds"
if (file.exists(cache)) {
silhouette.nmf <- read_rds(cache)
} else {
# manual calculation of silhouette scores with lazily evaluated distance matrix
subsamp.dists <- distances(tumor.hc.norm[subsamp, ])
plan(multisession, workers = 5)
silhouette.nmf <- nmf.clusters %>% future_imap_dbl(\(c, i){
dists <- tibble(d = subsamp.dists[i,][-i], cluster = nmf.clusters[-i]) %>%
group_by(cluster) %>%
summarize(m = mean(d))
a <- dists %>% filter(cluster == c) %>% pluck("m", 1)
b <- dists %>% filter(cluster != c) %>% pull(m) %>% min()
(b - a)/max(a, b)
}, .options = furrr::furrr_options(packages = "distances"), .progress = TRUE)
write_rds(silhouette.nmf, cache, "gz")
}
tibble(c = nmf.clusters, s = silhouette.nmf) %>%
group_by(c) %>%
summarize(m = mean(s))| c | m |
|---|---|
| 1 | 0.3791663 |
| 2 | 0.3017454 |
| 3 | 0.1130977 |
print(paste0("Average silhouette score: ", mean(silhouette.nmf)))[1] "Average silhouette score: 0.250828061783453"Select only the samples with positive silhouette scores as “core samples”
core.samples <- which(silhouette.nmf > 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 | 1 | -1.1509970 | 0 |
| BerEP4 | 0 | 1 | 1.3643187 | 0 |
| CRP | 0 | 1 | -0.8705340 | 0 |
| p.S6 | 0 | 1 | -0.6053700 | 0 |
| AGS | 0 | 3 | -0.4408815 | 0 |
| BerEP4 | 0 | 3 | -0.7472194 | 0 |
| CRP | 0 | 3 | 1.6290324 | 0 |
| p.S6 | 0 | 3 | 1.4251024 | 0 |
| AGS | 0 | 2 | 1.6605926 | 0 |
| BerEP4 | 0 | 2 | -0.7006696 | 0 |
| CRP | 0 | 2 | -0.7032498 | 0 |
| p.S6 | 0 | 2 | -0.7808243 | 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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[4] purrr_0.3.4 readr_2.1.0 tidyr_1.1.4
[7] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[10] RStoolbox_0.2.6 raster_3.5-2 sp_1.4-5
[13] furrr_0.2.3 future_1.23.0 distances_0.1.8
[16] RColorBrewer_1.1-2 pheatmap_1.0.12 cowplot_1.1.1
[19] NMF_0.23.0 synchronicity_1.3.5 bigmemory_4.5.36
[22] Biobase_2.54.0 BiocGenerics_0.40.0 cluster_2.1.2
[25] rngtools_1.5.2 pkgmaker_0.32.2 registry_0.5-1
[28] limma_3.50.0 uwot_0.1.10 Matrix_1.3-4
[31] skimr_2.1.3 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 uuid_1.0-3 backports_1.3.0
[4] Hmisc_4.6-0 plyr_1.8.6 repr_1.1.3
[7] splines_4.1.1 listenv_0.8.0 gridBase_0.4-7
[10] digest_0.6.28 foreach_1.5.1 htmltools_0.5.2
[13] fansi_0.5.0 checkmate_2.0.0 magrittr_2.0.1
[16] doParallel_1.0.16 tzdb_0.2.0 recipes_0.1.17
[19] globals_0.14.0 modelr_0.1.8 gower_0.2.2
[22] vroom_1.5.6 jpeg_0.1-9 colorspace_2.0-2
[25] rvest_1.0.2 haven_2.4.3 xfun_0.28
[28] rgdal_1.5-27 crayon_1.4.2 jsonlite_1.7.2
[31] bigmemory.sri_0.1.3 survival_3.2-13 iterators_1.0.13
[34] glue_1.5.0 gtable_0.3.0 ipred_0.9-12
[37] future.apply_1.8.1 scales_1.1.1 DBI_1.1.1
[40] Rcpp_1.0.7 htmlTable_2.3.0 xtable_1.8-4
[43] foreign_0.8-81 bit_4.0.4 Formula_1.2-4
[46] stats4_4.1.1 lava_1.6.10 prodlim_2019.11.13
[49] htmlwidgets_1.5.4 httr_1.4.2 geosphere_1.5-14
[52] ellipsis_0.3.2 farver_2.1.0 pkgconfig_2.0.3
[55] XML_3.99-0.8 nnet_7.3-16 sass_0.4.0
[58] dbplyr_2.1.1 utf8_1.2.2 caret_6.0-90
[61] labeling_0.4.2 tidyselect_1.1.1 rlang_0.4.12
[64] reshape2_1.4.4 later_1.3.0 munsell_0.5.0
[67] cellranger_1.1.0 tools_4.1.1 cli_3.1.0
[70] generics_0.1.1 broom_0.7.10 evaluate_0.14
[73] fastmap_1.1.0 yaml_2.2.1 bit64_4.0.5
[76] ModelMetrics_1.2.2.2 knitr_1.36 fs_1.5.0
[79] nlme_3.1-153 whisker_0.4 xml2_1.3.2
[82] compiler_4.1.1 rstudioapi_0.13 png_0.1-7
[85] reprex_2.0.1 bslib_0.3.1 stringi_1.7.5
[88] highr_0.9 rgeos_0.5-8 lattice_0.20-45
[91] vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1
[94] jquerylib_0.1.4 data.table_1.14.2 httpuv_1.6.3
[97] latticeExtra_0.6-29 R6_2.5.1 promises_1.2.0.1
[100] gridExtra_2.3 parallelly_1.28.1 codetools_0.2-18
[103] MASS_7.3-54 assertthat_0.2.1 rprojroot_2.0.2
[106] withr_2.4.2 parallel_4.1.1 hms_1.1.1
[109] terra_1.4-11 grid_4.1.1 rpart_4.1-15
[112] timeDate_3043.102 class_7.3-19 rmarkdown_2.11
[115] git2r_0.28.0 pROC_1.18.0 lubridate_1.8.0
[118] base64enc_0.1-3