browse_results
Jovan Tanevski
2021-10-29
Last updated: 2021-10-29
Checks: 7 0
Knit directory: mistyMBC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 4abf508 | Jovan Tanevski | 2021-10-29 | update result browsing to match new parameters |
| html | cb33a3e | Jovan Tanevski | 2021-09-23 | Build site. |
| Rmd | 02a4d8f | Jovan Tanevski | 2021-09-23 | add metadata and signature pca |
| html | 6a3c444 | Jovan Tanevski | 2021-09-15 | Build site. |
| Rmd | c6d135a | Jovan Tanevski | 2021-09-15 | extend results with celltype analysis |
| html | 6398d1c | Jovan Tanevski | 2021-09-13 | Build site. |
| Rmd | b562161 | Jovan Tanevski | 2021-09-13 | merge codex and merfish results |
| html | c457348 | Jovan Tanevski | 2021-09-09 | Build site. |
| html | aa6ca6b | Jovan Tanevski | 2021-09-09 | Build site. |
| Rmd | 04b30ad | Jovan Tanevski | 2021-09-09 | clean up results, focus on targets with gain |
| html | 2438eb2 | Jovan Tanevski | 2021-09-09 | Build site. |
| Rmd | b124494 | Jovan Tanevski | 2021-09-09 | update codex,merfish; add slideseq cellcom results |
| html | e0c1952 | Jovan Tanevski | 2021-07-22 | Build site. |
| html | 4dd6a02 | Jovan Tanevski | 2021-07-14 | Build site. |
| html | 913535e | Jovan Tanevski | 2021-06-17 | Build site. |
| Rmd | 736d8e3 | Jovan Tanevski | 2021-06-17 | subset merfish results |
| html | 1b68c27 | Jovan Tanevski | 2021-06-15 | Build site. |
| Rmd | 43913a8 | Jovan Tanevski | 2021-06-15 | auto publish date |
| Rmd | 34b1d9e | Jovan Tanevski | 2021-06-15 | explicit codex, merfish and exseq |
| html | e445e6f | Jovan Tanevski | 2021-06-11 | Build site. |
| Rmd | dd0219f | Jovan Tanevski | 2021-06-11 | add result browsing example |
Setup
Load necessary libraries
library(stringr)
library(dplyr)
library(readr)
library(mistyR)
library(ggplot2)
library(future)
library(factoextra)
plan(multisession)Output collection
Find the location of the results for all samples
outputs <- str_subset(list.dirs("output"), "processed") %>%
str_subset("failed", negate = TRUE) %>%
str_subset("slide_seq_processed-[0-9]$", negate = TRUE)Collect the results for all samples, single modality and all replicates
misty.results.codex <- collect_results(str_subset(outputs, "codex"))
Collecting improvements
Collecting contributions
Collecting importances
Aggregatingmisty.results.merfish <- collect_results(str_subset(outputs, "merfish"))
Collecting improvements
Collecting contributions
Collecting importances
Aggregatingmisty.results.ligrcp <- collect_results(str_subset(outputs, "ligrcp"))
Collecting improvements
Collecting contributions
Collecting importances
Aggregatingmisty.results.ligpath <- collect_results(str_subset(outputs, "ligpath"))
Collecting improvements
Collecting contributions
Collecting importances
AggregatingGet metadata
sample.meta <- read.delim("data/HTAPP_MBC_spatial_annotations.tsv",
na.strings = ""
)Plot basic results
CODEX
Filter only genes with mean gain in variance explained of .5% or more to plot the gain and view contributions
misty.results.codex %>%
plot_improvement_stats(trim = 0.5) %>%
plot_view_contributions(trim = 0.5)
Plot interaction heatmaps
misty.results.codex %>%
plot_interaction_heatmap("intra", cutoff = 4, clean = TRUE) %>%
plot_interaction_heatmap("juxta.15", cutoff = 1, clean = TRUE, trim = 0.5) %>%
plot_interaction_heatmap("para.100", cutoff = 1, clean = TRUE, trim = 0.5)
Plot contrasts
misty.results.codex %>%
plot_contrast_heatmap("intra", "juxta.15", cutoff = 1, trim = 0.5) %>%
plot_contrast_heatmap("intra", "para.100", cutoff = 1, trim = 0.5) %>%
plot_contrast_heatmap("para.100", "juxta.15", cutoff = 1, trim = 0.5) %>%
plot_contrast_heatmap("juxta.15", "para.100", cutoff = 1, trim = 0.5)
Plot interaction communities
misty.results.codex %>%
plot_interaction_communities("intra", cutoff = 4) %>%
plot_interaction_communities("juxta.15", cutoff = 1) %>%
plot_interaction_communities("para.100", cutoff = 1)
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
Signatures and clustering
signature.per.codex <- misty.results.codex %>%
extract_signature("performance", trim = 0.5) %>%
mutate(sample = str_extract(sample, "HTAPP(-[:alnum:]+){3}"))
signature.per.pca <- signature.per.codex %>%
select(-sample) %>%
prcomp()
signature.per.pca.ann <- left_join(bind_cols(
signature.per.codex %>% select(sample),
as.data.frame(signature.per.pca$x)
),
sample.meta,
by = c("sample" = "name")
)
ggplot(signature.per.pca.ann, aes(x = PC1, y = PC2)) +
geom_point(aes(color = site_biopsy)) +
theme_classic()
ggplot(signature.per.pca.ann, aes(x = PC1, y = PC2)) +
geom_point(aes(color = receptors_biopsy)) +
theme_classic()
fviz_pca_var(signature.per.pca, col.var = "cos2", gradient.cols = c("#666666", "#377EB8", "#E41A1C"), repel = TRUE) + theme_classic()
MERFISH
We combine the information from binned and unbinned samples.
Filter only genes with mean gain in variance explained of 2 or more to plot the gain and view contributions
MERFISH shows much better performance improvement than CODEX, but captures much less information in the intraview on average.
misty.results.merfish %>%
plot_improvement_stats(trim = 2) %>%
plot_view_contributions(trim = 2)
MERFISH shows much better performance improvement than CODEX, but captures much less information in the intraview on average.
misty.results.merfish$improvements %>%
filter(measure == "intra.R2") %>%
pull(value) %>%
hist(main = "MERFISH distribution of variance explained in intraview")
misty.results.merfish$improvements %>%
filter(measure == "gain.R2") %>%
pull(value) %>%
hist(main = "MERFISH distribution of gain in variance explained")
misty.results.codex$improvements %>%
filter(measure == "intra.R2") %>%
pull(value) %>%
hist(main = "CODEX distribution of variance explained in intraview")
misty.results.codex$improvements %>%
filter(measure == "gain.R2") %>%
pull(value) %>%
hist(main = "CODEX distribution of gain in variance explained")
Plot interaction heatmaps
misty.results.merfish %>%
plot_interaction_heatmap("intra", cutoff = 6, clean = TRUE) %>%
plot_interaction_heatmap("juxta.15", cutoff = 1.5, clean = TRUE, trim = 2) %>%
plot_interaction_heatmap("para.100", cutoff = 1.5, clean = TRUE, trim = 2)
Plot contrasts
misty.results.merfish %>%
plot_contrast_heatmap("intra", "juxta.15", cutoff = 1.5, trim = 2) %>%
plot_contrast_heatmap("intra", "para.100", cutoff = 1.5, trim = 2) %>%
plot_contrast_heatmap("para.100", "juxta.15", cutoff = 1.5, trim = 2) %>%
plot_contrast_heatmap("juxta.15", "para.100", cutoff = 1.5, trim = 2)
Plot interaction communities
misty.results.merfish %>%
plot_interaction_communities("intra", cutoff = 6) %>%
plot_interaction_communities("juxta.15", cutoff = 2) %>%
plot_interaction_communities("para.100", cutoff = 2)
Signatures and clustering
We observe better clustering based on biopsy site than receptor status
signature.per.merfish <- misty.results.merfish %>%
extract_signature("performance", trim = 2) %>%
mutate(sample = str_extract(sample, "HTAPP(-[:alnum:]+){3}"))
signature.per.pca <- signature.per.merfish %>%
select(-sample) %>%
prcomp()
signature.per.pca.ann <- left_join(bind_cols(
signature.per.merfish %>% select(sample),
as.data.frame(signature.per.pca$x)
),
sample.meta,
by = c("sample" = "name")
)
ggplot(signature.per.pca.ann, aes(x = PC1, y = PC2)) +
geom_point(aes(color = site_biopsy)) +
theme_classic()
ggplot(signature.per.pca.ann, aes(x = PC1, y = PC2)) +
geom_point(aes(color = receptors_biopsy)) +
theme_classic()
fviz_pca_var(signature.per.pca, col.var = "cos2", gradient.cols = c("#666666", "#377EB8", "#E41A1C"), repel = TRUE) + theme_classic()
Merge CODEX and MERFISH results
Filter only genes with mean gain in variance explained of 1 or more to plot the gain and view contributions
misty.results.merged <- collect_results(str_subset(outputs, "(codex|merfish)"))
Collecting improvements
Collecting contributions
Collecting importances
Aggregatingmisty.results.merged %>%
plot_improvement_stats(trim = 2) %>%
plot_view_contributions(trim = 2)
Plot interaction heatmaps
misty.results.merged %>%
plot_interaction_heatmap("intra", cutoff = 8, clean = TRUE) %>%
plot_interaction_heatmap("juxta.15", cutoff = 1.5, clean = TRUE, trim = 2) %>%
plot_interaction_heatmap("para.100", cutoff = 1.5, clean = TRUE, trim = 2) %>%
plot_contrast_heatmap("intra", "juxta.15", cutoff = 1.5, trim = 2) %>%
plot_contrast_heatmap("intra", "para.100", cutoff = 1.5, trim = 2)
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
Signatures
There are no common targets with improvements above 1% between the two technologies, therefore we extract the signature using all markers, although we expect to have a technology based batch effect.
sig.perf <- extract_signature(misty.results.merged, "performance")
sig.perf.pca <- sig.perf %>%
select(-sample) %>%
prcomp()
sig.perf.join <- sig.perf %>%
mutate(
tech = str_extract(sample, "(merfish_processed|merfish_bin|codex)"),
sample = str_extract(sample, "HTAPP-[0-9]{3}-SMP-[0-9]{3,4}")
) %>%
select(tech, sample) %>%
bind_cols(as.data.frame(sig.perf.pca$x)) %>%
left_join(sample.meta, by = c("sample" = "name"))
ggplot(sig.perf.join, aes(x = PC1, y = PC2)) +
geom_point(aes(color = tech)) +
theme_classic()
ggplot(sig.perf.join, aes(x = PC1, y = PC2)) +
geom_point(aes(color = site_biopsy)) +
theme_classic()
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
SlideSeq
Filter only ligands then pathways with mean gain in variance explained of 1 or more to plot the gain and view contributions
misty.results.ligrcp %>%
plot_improvement_stats(trim = 0.5) %>%
plot_view_contributions(trim = 0.5)Warning: Removed 1 rows containing missing values (geom_segment).
misty.results.ligpath %>%
plot_improvement_stats(trim = 1) %>%
plot_view_contributions(trim = 1)
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
Plot interaction heatmaps
misty.results.ligrcp %>%
plot_interaction_heatmap("intra", cutoff = 5, clean = TRUE) %>%
plot_interaction_heatmap("juxta.25", cutoff = 3, clean = TRUE, trim = 0.5) %>%
plot_interaction_heatmap("para.150", cutoff = 2, clean = TRUE, trim = 0.5)
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
misty.results.ligpath %>%
plot_interaction_heatmap("intra", cutoff = 1.5, clean = TRUE) %>%
plot_interaction_heatmap("juxta.25", cutoff = 3, clean = TRUE, trim = 1) %>%
plot_interaction_heatmap("para.150", cutoff = 1.5, clean = TRUE, trim = 1)
Plot intrinsic pathway communities
misty.results.ligpath %>% plot_interaction_communities("intra", cutoff = 1.5)
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
Cell-type based analysis
We are interested in predicting the probability of a cell being of a cell-type of interest, by looking at the distribution of cell types in the neighborhood of 100 cells.
More conservative trimming of above 10% variance explained since the intraview is bypassed. The contribution of the intraview can be interpreted as the amount of variance captured only by the mean of the probability estimate for that cell-type. For a good result it is expected to be close to zero.
misty.results.ctype <- collect_results(str_subset(outputs, "ctype"))
Collecting improvements
Collecting contributions
Collecting importances
Aggregatingmisty.results.ctype %>%
plot_improvement_stats(trim = 10) %>%
plot_view_contributions(trim = 10)
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
Plot neighborhood interactions
misty.results.ctype %>%
plot_interaction_heatmap("juxta.25", trim = 10, cutoff = 0.5) %>%
plot_interaction_heatmap("para.150", trim = 10, cutoff = 0.5)
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
| Version | Author | Date |
|---|---|---|
| cb33a3e | Jovan Tanevski | 2021-09-23 |
Plot contrasts
misty.results.ctype %>%
plot_contrast_heatmap("para.150", "juxta.25", trim = 10, cutoff = 0.5) %>%
plot_contrast_heatmap("juxta.25", "para.150", trim = 10, cutoff = 0.5)
Plot communities
misty.results.ctype %>%
plot_interaction_communities("juxta.25", cutoff = 1) %>%
plot_interaction_communities("para.150", cutoff = 1)
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] factoextra_1.0.7 future_1.22.1 ggplot2_3.3.5 mistyR_1.1.14
[5] readr_2.0.2 dplyr_1.0.7 stringr_1.4.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] sass_0.4.0 tidyr_1.1.4 jsonlite_1.7.2 carData_3.0-4
[5] R.utils_2.11.0 bslib_0.3.1 assertthat_0.2.1 highr_0.9
[9] cellranger_1.1.0 yaml_2.2.1 ggrepel_0.9.1 globals_0.14.0
[13] pillar_1.6.4 backports_1.3.0 glue_1.4.2 digest_0.6.28
[17] RColorBrewer_1.1-2 promises_1.2.0.1 ggsignif_0.6.3 colorspace_2.0-2
[21] htmltools_0.5.2 httpuv_1.6.3 R.oo_1.24.0 pkgconfig_2.0.3
[25] broom_0.7.9 listenv_0.8.0 haven_2.4.3 purrr_0.3.4
[29] scales_1.1.1 whisker_0.4 openxlsx_4.2.4 later_1.3.0
[33] rio_0.5.27 tzdb_0.2.0 git2r_0.28.0 tibble_3.1.5
[37] generics_0.1.1 farver_2.1.0 car_3.0-11 ellipsis_0.3.2
[41] ggpubr_0.4.0 withr_2.4.2 furrr_0.2.3 readxl_1.3.1
[45] magrittr_2.0.1 crayon_1.4.1 evaluate_0.14 R.methodsS3_1.8.1
[49] fs_1.5.0 fansi_0.5.0 parallelly_1.28.1 rstatix_0.7.0
[53] forcats_0.5.1 foreign_0.8-81 tools_4.1.1 data.table_1.14.2
[57] hms_1.1.1 lifecycle_1.0.1 munsell_0.5.0 zip_2.2.0
[61] compiler_4.1.1 jquerylib_0.1.4 rlang_0.4.12 grid_4.1.1
[65] igraph_1.2.7 labeling_0.4.2 rmarkdown_2.11 gtable_0.3.0
[69] codetools_0.2-18 abind_1.4-5 DBI_1.1.1 curl_4.3.2
[73] R6_2.5.1 knitr_1.36 fastmap_1.1.0 utf8_1.2.2
[77] rprojroot_2.0.2 stringi_1.7.5 parallel_4.1.1 Rcpp_1.0.7
[81] vctrs_0.3.8 tidyselect_1.1.1 xfun_0.27