Crossover
Last updated: 2018-11-23
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7d8a1dd | Luke Zappia | 2018-11-23 | Minor fixes to output |
| Rmd | 91ebd58 | Luke Zappia | 2018-11-21 | Move summariseClusts function to file |
| html | 91ebd58 | Luke Zappia | 2018-11-21 | Move summariseClusts function to file |
| html | a61f9c9 | Luke Zappia | 2018-09-13 | Rebuild site |
| html | ad10b21 | Luke Zappia | 2018-09-13 | Switch to GitHub |
| Rmd | bff4d5b | Luke Zappia | 2018-08-14 | Add crossover document |
# Presentation
library("knitr")
library("glue")
# Paths
library("here")
# Tidyverse
library("tidyverse")source(here("R/output.R"))
source(here("R/crossover.R"))orgs.path <- here("output/04_Organoids_Clustering/cluster_assignments.csv")
orgs.neph.path <- here("output/04B_Organoids_Nephron/cluster_assignments.csv")
comb.path <- here("output/07_Combined_Clustering/cluster_assignments.csv")
comb.neph.path <- here("output/07B_Combined_Nephron/cluster_assignments.csv")Introduction
In this document we are going to load the results of the various clustering analyses and compare them. The goal is to see if they are consistent by checking that clusters in different analyses that have been assigned the same cell types actually contain the same cells.
orgs.clusts <- read_csv(orgs.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(Organoids = Cluster)
orgs.neph.clusts <- read_csv(orgs.neph.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(OrgsNephron = Cluster)
comb.clusts <- read_csv(comb.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(Combined = Cluster)
comb.neph.clusts <- read_csv(comb.neph.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(CombNephron = Cluster)
clusts <- comb.clusts %>%
left_join(comb.neph.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode")) %>%
left_join(orgs.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode")) %>%
left_join(orgs.neph.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode"))We are going to do this using a kind of heatmap. Clustering results from two separate analyses will form the x and y axes and each cell will represent the overlap in samples between two clusters. We will colour the cells using the Jaccard index, a measure of similarity between to groups that is equal to the size of the intersect divided by the size of the union. This will highlight clusters that are particularly similar. We will also label cells with the proportion of samples in a cluster that are also in another, so that rows and columns will each sum to one (using a separate colour for each).
Organoids
vs Organoids Nephron
summariseClusts(clusts, Organoids, OrgsNephron) %>%
ggplot(aes(x = Organoids, y = OrgsNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(OrgsNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({OrganoidsTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({OrgsNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Organoids cluster",
y = "Organoids nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
vs Combined
summariseClusts(clusts, Organoids, Combined) %>%
ggplot(aes(x = Organoids, y = Combined, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 4) +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 4) +
geom_text(aes(label = glue("({OrganoidsTotal})")), y = -0.05,
size = 3.5, colour = "#ff698f") +
geom_text(aes(label = glue("({CombinedTotal})")), x = -0.05,
size = 3.5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Organoids cluster",
y = "Combined cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
vs Combined Nephron
summariseClusts(clusts, Organoids, CombNephron) %>%
ggplot(aes(x = Organoids, y = CombNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(CombNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({OrganoidsTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({CombNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Organoids cluster",
y = "Combined nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
Combined
vs Combined Nephron
summariseClusts(clusts, Combined, CombNephron) %>%
ggplot(aes(x = Combined, y = CombNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(CombNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({CombinedTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({CombNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined cluster",
y = "Combined nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
vs Organoids
summariseClusts(clusts, Combined, Organoids) %>%
ggplot(aes(x = Combined, y = Organoids, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 4) +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 4) +
geom_text(aes(label = glue("({CombinedTotal})")), y = -0.05,
size = 4, colour = "#ff698f") +
geom_text(aes(label = glue("({OrganoidsTotal})")), x = -0.05,
size = 4, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined cluster",
y = "Organoids cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
vs Organoids Nephron
summariseClusts(clusts, Combined, OrgsNephron) %>%
ggplot(aes(x = Combined, y = OrgsNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(OrgsNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({CombinedTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({OrgsNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined cluster",
y = "Organoids nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
Combined nephron
vs Organoids Nephron
summariseClusts(clusts, CombNephron, OrgsNephron) %>%
ggplot(aes(x = CombNephron, y = OrgsNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombNephronPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(OrgsNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({CombNephronTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({OrgsNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined nephron cluster",
y = "Organoids nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
Summary
Output files
This table describes the output files produced by this document. Right click and Save Link As… to download the results.
dir.create(here("output", DOCNAME), showWarnings = FALSE)
write_csv(clusts, here("output", DOCNAME, "cluster_assignments.csv"))
kable(data.frame(
File = c(
glue("[cluster_assignments.csv]",
"({getDownloadURL('cluster_assignments.csv', DOCNAME)})")
),
Description = c(
"Cluster assignments for all clustering analyses"
)
))| File | Description |
|---|---|
| cluster_assignments.csv | Cluster assignments for all clustering analyses |
Session information
devtools::session_info() setting value
version R version 3.5.0 (2018-04-23)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
tz Australia/Melbourne
date 2018-11-23
package * version date source
assertthat 0.2.0 2017-04-11 CRAN (R 3.5.0)
backports 1.1.2 2017-12-13 CRAN (R 3.5.0)
base * 3.5.0 2018-06-18 local
bindr 0.1.1 2018-03-13 cran (@0.1.1)
bindrcpp * 0.2.2 2018-03-29 cran (@0.2.2)
broom 0.5.0 2018-07-17 cran (@0.5.0)
cellranger 1.1.0 2016-07-27 CRAN (R 3.5.0)
cli 1.0.0 2017-11-05 CRAN (R 3.5.0)
colorspace 1.3-2 2016-12-14 cran (@1.3-2)
compiler 3.5.0 2018-06-18 local
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devtools 1.13.6 2018-06-27 CRAN (R 3.5.0)
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git2r 0.21.0 2018-01-04 CRAN (R 3.5.0)
glue * 1.3.0 2018-07-17 cran (@1.3.0)
graphics * 3.5.0 2018-06-18 local
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grid 3.5.0 2018-06-18 local
gtable 0.2.0 2016-02-26 cran (@0.2.0)
haven 1.1.2 2018-06-27 CRAN (R 3.5.0)
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magrittr 1.5 2014-11-22 CRAN (R 3.5.0)
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nlme 3.1-137 2018-04-07 CRAN (R 3.5.0)
pillar 1.3.0 2018-07-14 cran (@1.3.0)
pkgconfig 2.0.1 2017-03-21 cran (@2.0.1)
plyr 1.8.4 2016-06-08 cran (@1.8.4)
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R6 2.2.2 2017-06-17 CRAN (R 3.5.0)
Rcpp 0.12.18 2018-07-23 cran (@0.12.18)
readr * 1.1.1 2017-05-16 CRAN (R 3.5.0)
readxl 1.1.0 2018-04-20 CRAN (R 3.5.0)
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rmarkdown 1.10.2 2018-07-30 Github (rstudio/rmarkdown@18207b9)
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scales 0.5.0 2017-08-24 cran (@0.5.0)
stats * 3.5.0 2018-06-18 local
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tidyselect 0.2.4 2018-02-26 cran (@0.2.4)
tidyverse * 1.2.1 2017-11-14 CRAN (R 3.5.0)
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viridisLite 0.3.0 2018-02-01 cran (@0.3.0)
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yaml 2.2.0 2018-07-25 cran (@2.2.0) This reproducible R Markdown analysis was created with workflowr 1.1.1