Last updated: 2021-11-12
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Knit directory: Multispectral HCC/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/cores_3.Rmd
) and HTML (docs/cores_3.html
) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
File | Version | Author | Date | Message |
---|---|---|---|---|
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 |
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 details
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 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)
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)
Version | Author | Date |
---|---|---|
a915f46 | Jovan Tanevski | 2021-10-27 |
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)
Version | Author | Date |
---|---|---|
a915f46 | Jovan Tanevski | 2021-10-27 |
Assign clusters
nmf.clusters <- apply(fit(tumor.hc.nmf)@H, 2, which.max)
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()
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).
Version | Author | Date |
---|---|---|
a915f46 | Jovan Tanevski | 2021-10-27 |
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)
Version | Author | Date |
---|---|---|
a915f46 | Jovan Tanevski | 2021-10-27 |
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
)
Version | Author | Date |
---|---|---|
a915f46 | Jovan Tanevski | 2021-10-27 |
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")) +
geom_point(data = spatial %>% filter(sample == s), aes(x = X, y = Y, color = Cluster)) +
theme_map()) %>%
print()
dev.off()
}
})
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")
Version | Author | Date |
---|---|---|
a915f46 | Jovan Tanevski | 2021-10-27 |
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