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Rmd | d036935 | Jeffrey Pullin | 2021-03-31 | Refactor concordance analysis |
html | ca82ce0 | Jeffrey Pullin | 2021-02-16 | Build site. |
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Rmd | 1ad9d6d | Jeffrey Pullin | 2021-02-10 | Add workflowr website |
library(tibble)
library(dplyr)
library(ggupset)
library(ggplot2)
library(tidyr)
library(SingleCellExperiment)
library(logisticPCA)
library(pals)
library(topconfects)
library(patchwork)
library(ggrepel)
library(purrr)
library(scater)
source(here::here("code", "top-genes.R"))
source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))
<- retrieve_real_data_parameters() %>%
concordance_data filter(data_id == "pbmc3k") %>%
rowwise() %>%
mutate(mgs = list(readRDS(full_filename)$result)) %>%
mutate(mgs = list(split(mgs, mgs$cluster))) %>%
ungroup() %>%
unnest(cols = mgs) %>%
rowwise() %>%
mutate(cluster = mgs$cluster[[1]]) %>%
ungroup()
<- c("random", "binom", "difference", "lm") poor_methods
%>%
concordance_data filter(!(method %in% poor_methods)) %>%
filter(data_id == "pbmc3k" & cluster == "Platelet") %>%
mutate(pars = pars_lookup[pars]) %>%
rowwise() %>%
mutate(mgs = list(dplyr::slice(mgs, 1:10)$gene)) %>%
unnest(col = mgs) %>%
nest_by(mgs) %>%
mutate(data = list(data[["pars"]])) %>%
ungroup() %>%
ggplot(aes(x = data)) +
geom_bar() +
scale_x_upset(n_intersections = 15) +
labs(
x = "",
y = "Number of genes"
)
Warning: Removed 26 rows containing non-finite values (stat_count).
Tried on all genes, but convergence issues in logisticPCA.
<- concordance_data %>%
long_data filter(!(method %in% poor_methods)) %>%
rowwise() %>%
filter(data_id == "pbmc3k", cluster == "B") %>%
mutate(mgs = list(get_top_sel_mgs(mgs, n = 100)$gene)) %>%
ungroup() %>%
unnest_longer(col = mgs) %>%
# Not sure why this occurs.
filter(!is.na(mgs))
<- model.matrix(~ 0 + . , data = long_data["mgs"])
binary_data
<- cbind(
cluster_data pars = long_data$pars,
as.data.frame(binary_data)
%>%
) group_by(pars) %>%
summarise(across(everything(), sum))
<- as.matrix(cluster_data[, -1])
cluster_mat
# Sanity checking.
#rowSums(cluster_mat)
#colSums(cluster_mat)
#max(cluster_mat)
<- prcomp(cluster_mat)
pca <- tibble(
pca_data pc1 = pca$x[, 1],
pc2 = pca$x[, 2],
pars = cluster_data$pars,
plot_pars = pars_lookup[cluster_data$pars]
%>%
) rowwise() %>%
mutate(method = strsplit(pars, "[_]")[[1]][1])
ggplot(pca_data, aes(x = pc1, y = pc2, colour = method)) +
geom_point() +
labs(
x = "PC1",
y = "PC2",
colour = "Method"
+
) theme_bw()
ggplot(pca_data, aes(x = pc1, y = pc2, label = plot_pars, colour = method)) +
geom_point(size = 2) +
geom_label_repel(colour = "black", max.overlaps = 20) +
labs(
x = "PC 1",
y = "PC 2",
colour = "Method"
+
) theme_bw()
ggplot(pca_data, aes(x = pc1, y = pc2, label = plot_pars, colour = method)) +
geom_point(size = 3) +
geom_text_repel(
aes(label = plot_pars),
colour = "black",
max.overlaps = 20) +
#coord_cartesian(xlim = c(-5, 7), ylim = c(-6, 4)) +
labs(
x = "PC 1",
y = "PC 2",
colour = "Method"
+
) theme_bw()
# tsne <- Rtsne::Rtsne(cluster_mat, perplexity = 10)
# pca_data <- data.frame(
# pc1 = pca$x[, 1],
# pc2 = pca$x[, 2],
# pars = cluster_data$pars
# )
#
# ggplot(pca_data, aes(x = pc1, y = pc2, colour = pars)) +
# geom_point() +
# scale_colour_manual(values = unname(polychrome(33))) +
# theme_bw()
<- logisticPCA(cluster_mat)
log_pca data.frame(pc1 = log_pca$PCs[, 1], pc2 = log_pca$PCs[, 2],
pars = cluster_data$pars) %>%
ggplot(aes(pc1, pc2, colour = pars)) +
geom_point() +
#scale_colour_manual(values = unname(polychrome(39))) +
theme_bw()
<- readRDS(
scanpy_t ::here("results", "real_data", "pbmc3k-scanpy_t_rankby_raw.rds")
here
)
<- readRDS(
seurat_t ::here("results", "real_data", "pbmc3k-seurat_t.rds")
here
)
<- readRDS(
scanpy_wilcox ::here("results", "real_data", "pbmc3k-scanpy_wilcoxon_rankby_raw.rds")
here
)
<- readRDS(
scanpy_wilcox_tc ::here("results", "real_data", "pbmc3k-scanpy_wilcoxontiecorrect_rankby_raw.rds")
here
)
<- readRDS(
seurat_wilcox ::here("results", "real_data", "pbmc3k-seurat_wilcox.rds")
here
)
<- readRDS(
scran_t_any ::here("results", "real_data", "pbmc3k-scran_t_any.rds")
here )
<- readRDS(
scran_t_any ::here("results", "real_data", "pbmc3k-scran_t_any.rds")
here
)
<- readRDS(
seurat_wilcox ::here("results", "real_data", "pbmc3k-seurat_wilcox.rds")
here
)
<- 20
n <- scran_t_any %>%
top_scran_t_any pluck("result") %>%
filter(cluster == "NK") %>%
get_top_sel_mgs(n = n)
<- seurat_wilcox %>%
top_seurat_wilcox pluck("result") %>%
filter(cluster == "NK") %>%
get_top_sel_mgs(n = n)
<- rank_rank_plot(
scran_vs_seurat_plot $gene,
top_scran_t_any$gene,
top_seurat_wilcoxlabel1 = "Scran default",
label2 = "Seurat default"
+
) ggtitle("Scran vs Seurat")
scran_vs_seurat_plot
<- readRDS(
scanpy_t_raw ::here("results", "real_data", "pbmc3k-scanpy_t_rankby_raw.rds")
here
)
<- readRDS(
seurat_wilcox ::here("results", "real_data", "pbmc3k-seurat_wilcox.rds")
here
)
<- 20
n <- scanpy_t_raw %>%
top_scanpy_t_raw pluck("raw_result") %>%
filter(cluster == "NK") %>%
get_top_sel_mgs(n = n)
<- seurat_wilcox %>%
top_seurat_wilcox pluck("result") %>%
filter(cluster == "NK") %>%
get_top_sel_mgs(n = n)
<- rank_rank_plot(
seurat_vs_scanpy_plot $gene,
top_seurat_wilcox$gene,
top_scanpy_t_rawlabel1 = "Seurat default",
label2 = "Scanpy default"
+
) ggtitle("Seurat vs Scanpy")
seurat_vs_scanpy_plot
wrap_plots(scran_vs_seurat_plot, seurat_vs_scanpy_plot) +
plot_annotation(tag_levels = "A")
<- 40
n <- scanpy_wilcox_tc %>%
top_scanpy_wilcox_tc pluck("result") %>%
filter(cluster == "B") %>%
get_top_sel_mgs(n = n)
<- seurat_wilcox %>%
top_seurat_wilcox pluck("result") %>%
filter(cluster == "B") %>%
get_top_sel_mgs(n = n)
%>%
top_seurat_wilcox mutate(rank = 1:n()) %>%
filter(gene == "NKG7") %>%
select(-raw_statistic)
# A tibble: 0 × 7
# … with 7 variables: p_value <dbl>, p_value_adj <dbl>, cluster <fct>,
# log_fc <dbl>, gene <chr>, scaled_statistic <dbl>, rank <int>
<- scanpy_wilcox %>%
top_scanpy_wilcox pluck("result") %>%
filter(cluster == "B") %>%
get_top_sel_mgs(n = n)
<- scanpy_t %>%
top_scanpy_t_processed pluck("result") %>%
filter(cluster == "NK") %>%
get_top_sel_mgs(n = n)
<- scanpy_t %>%
top_scanpy_t_raw pluck("raw_result") %>%
filter(cluster == "B")
<- seurat_t %>%
top_seurat_t pluck("result") %>%
filter(cluster == "B") %>%
get_top_sel_mgs(n = n, direction = "both")
<- scran_t_any %>%
top_scran_t_any pluck("result") %>%
filter(cluster == "B") %>%
get_top_sel_mgs(n = n, direction = "both")
<- scran_t_any %>%
top_scran_t_any pluck("result") %>%
filter(cluster == "B") %>%
get_top_sel_mgs(n = n, direction = "both")
rank_rank_plot(
$gene,
top_scanpy_wilcox_tc$gene,
top_seurat_wilcoxlabel1 = "Scanpy Wilcoxon (tie corrected)",
label2 = "Seurat Wilcoxon"
)
rank_rank_plot(
$gene,
top_scanpy_wilcox$gene,
top_seurat_wilcoxlabel1 = "Scanpy Wilcoxon",
label2 = "Seurat Wilcoxon"
+
) ggtitle("Scanpy Wilcoxon vs Seurat Wilcoxon") +
theme(axis.ticks.y = element_blank())
rank_rank_plot(
$gene,
top_scanpy_t_raw$gene,
top_seurat_tlabel1 = "Scanpy t",
label2 = "Seurat t"
)
<- readRDS(
zeisel_seurat_wilcox ::here("results", "real_data", "zeisel-seurat_wilcox.rds")
here
)
<- readRDS(
zeisel_scanpy_wilcox_tc ::here("results", "real_data", "zeisel-scanpy_wilcoxontiecorrect_rankby_raw.rds")
here
)
<- 40
n <- zeisel_scanpy_wilcox_tc %>%
zeisel_top_scanpy_wilcox_tc pluck("result") %>%
filter(cluster == "oligodendrocytes") %>%
get_top_sel_mgs(n = n)
<- zeisel_seurat_wilcox %>%
zeisel_top_seurat_wilcox pluck("result") %>%
filter(cluster == "oligodendrocytes") %>%
get_top_sel_mgs(n = n)
rank_rank_plot(
$gene,
zeisel_top_seurat_wilcox$gene,
zeisel_top_scanpy_wilcox_tclabel1 = "Seurat Wilcoxon",
label2 = "Scanpy Wilcoxon (tie corrected)"
)
<- readRDS(here::here("data", "real_data", "pbmc3k.rds"))
pbmc3k plotExpression(pbmc3k, x = "label", "HLA-DRA")
plotExpression(pbmc3k, x = "label", "CD74")
top_scanpy_t_raw
# A tibble: 2,000 × 7
cluster gene scaled_statistic p_value p_value_adj log_fc raw_statistic
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 B CD74 79.0 0 0 4.07 0
2 B HLA-DRA 74.7 0 0 4.89 0
3 B CD79A 52.3 4.61e-169 1.32e-166 7.78 0
4 B HLA-DPB1 52.1 1.59e-257 1.06e-254 4.04 0
5 B HLA-DRB1 47.9 1.45e-233 7.26e-231 3.88 0
6 B CD79B 44.0 1.57e-152 3.15e-150 5.54 0
7 B HLA-DPA1 43.5 2.36e-201 7.86e-199 3.68 0
8 B HLA-DQA1 38.8 1.31e-135 2.01e-133 5.34 0
9 B MS4A1 36.4 3.65e-122 4.30e-120 6.42 0
10 B HLA-DQB1 33.9 4.98e-117 5.25e-115 4.97 0
# … with 1,990 more rows
top_scanpy_wilcox_tc
# A tibble: 40 × 7
cluster gene scaled_statistic p_value p_value_adj log_fc raw_statistic
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 B CD79A 43.9 0 0 7.78 0
2 B MS4A1 39.7 0 0 6.42 0
3 B CD79B 35.4 2.65e-274 1.77e-271 5.54 0
4 B LINC00926 35.3 2.39e-272 1.20e-269 7.50 0
5 B TCL1A 35.2 9.46e-271 3.78e-268 6.94 0
6 B HLA-DQA1 34.9 2.94e-266 9.79e-264 5.34 0
7 B VPREB3 32.9 2.74e-237 7.82e-235 7.48 0
8 B HLA-DQB1 32.4 1.17e-229 2.93e-227 4.97 0
9 B CD74 29.0 5.25e-185 1.17e-182 4.07 0
10 B HLA-DRA 28.9 1.95e-183 3.90e-181 4.89 0
# … with 30 more rows
top_scanpy_wilcox
# A tibble: 40 × 7
cluster gene scaled_statistic p_value p_value_adj log_fc raw_statistic
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 B CD74 29.0 2.58e-184 5.16e-181 4.07 0
2 B CD79A 27.9 4.51e-171 4.51e-168 7.78 0
3 B HLA-DRA 27.6 5.15e-168 3.43e-165 4.89 0
4 B CD79B 26.6 4.93e-156 2.46e-153 5.54 0
5 B HLA-DPB1 25.8 4.01e-147 1.60e-144 4.04 0
6 B HLA-DQA1 25.2 4.33e-140 1.44e-137 5.34 0
7 B MS4A1 25.2 1.25e-139 3.56e-137 6.42 0
8 B HLA-DRB1 24.3 4.73e-130 1.12e-127 3.88 0
9 B HLA-DQB1 24.3 5.03e-130 1.12e-127 4.97 0
10 B CD37 24.1 3.28e-128 6.56e-126 2.46 0
# … with 30 more rows
plotExpression(pbmc3k, x = "label", "CD74")
plotExpression(pbmc3k, x = "label", "CD79A")
plotExpression(pbmc3k, x = "label", "PF4")
::session_info() devtools
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.0 (2021-05-18)
os Red Hat Enterprise Linux
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2022-02-08
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.1.0)
beachmat 2.10.0 2021-10-26 [1] Bioconductor
beeswarm 0.4.0 2021-06-01 [2] CRAN (R 4.1.0)
Biobase * 2.54.0 2021-10-26 [1] Bioconductor
BiocGenerics * 0.40.0 2021-10-26 [1] Bioconductor
BiocNeighbors 1.12.0 2021-10-26 [1] Bioconductor
BiocParallel 1.28.3 2021-12-09 [1] Bioconductor
BiocSingular 1.10.0 2021-10-26 [1] Bioconductor
bitops 1.0-7 2021-04-24 [2] CRAN (R 4.1.0)
bslib 0.2.5.1 2021-05-18 [2] CRAN (R 4.1.0)
cachem 1.0.6 2021-08-19 [1] CRAN (R 4.1.0)
callr 3.7.0 2021-04-20 [2] CRAN (R 4.1.0)
cli 3.1.0 2021-10-27 [1] CRAN (R 4.1.0)
colorspace 2.0-2 2021-06-24 [1] CRAN (R 4.1.0)
cowplot 1.1.1 2020-12-30 [2] CRAN (R 4.1.0)
crayon 1.4.2 2021-10-29 [1] CRAN (R 4.1.0)
DBI 1.1.2 2021-12-20 [1] CRAN (R 4.1.0)
DelayedArray 0.20.0 2021-10-26 [1] Bioconductor
DelayedMatrixStats 1.16.0 2021-10-26 [1] Bioconductor
desc 1.3.0 2021-03-05 [2] CRAN (R 4.1.0)
devtools 2.4.1 2021-05-05 [2] CRAN (R 4.1.0)
dichromat 2.0-0 2013-01-24 [2] CRAN (R 4.1.0)
digest 0.6.29 2021-12-01 [1] CRAN (R 4.1.0)
dplyr * 1.0.7 2021-06-18 [1] CRAN (R 4.1.0)
ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.1.0)
evaluate 0.14 2019-05-28 [2] CRAN (R 4.1.0)
fansi 0.5.0 2021-05-25 [2] CRAN (R 4.1.0)
farver 2.1.0 2021-02-28 [2] CRAN (R 4.1.0)
fastmap 1.1.0 2021-01-25 [2] CRAN (R 4.1.0)
fs 1.5.0 2020-07-31 [2] CRAN (R 4.1.0)
generics 0.1.1 2021-10-25 [1] CRAN (R 4.1.0)
GenomeInfoDb * 1.30.0 2021-10-26 [1] Bioconductor
GenomeInfoDbData 1.2.7 2021-12-03 [1] Bioconductor
GenomicRanges * 1.46.1 2021-11-18 [1] Bioconductor
ggbeeswarm 0.6.0 2017-08-07 [2] CRAN (R 4.1.0)
ggplot2 * 3.3.5 2021-06-25 [1] CRAN (R 4.1.0)
ggrepel * 0.9.1 2021-01-15 [2] CRAN (R 4.1.0)
ggupset * 0.3.0 2020-05-05 [1] CRAN (R 4.1.0)
git2r 0.28.0 2021-01-10 [2] CRAN (R 4.1.0)
glue 1.6.0 2021-12-17 [1] CRAN (R 4.1.0)
gridExtra 2.3 2017-09-09 [2] CRAN (R 4.1.0)
gtable 0.3.0 2019-03-25 [2] CRAN (R 4.1.0)
here 1.0.1 2020-12-13 [1] CRAN (R 4.1.0)
highr 0.9 2021-04-16 [2] CRAN (R 4.1.0)
htmltools 0.5.1.1 2021-01-22 [2] CRAN (R 4.1.0)
httpuv 1.6.1 2021-05-07 [2] CRAN (R 4.1.0)
IRanges * 2.28.0 2021-10-26 [1] Bioconductor
irlba 2.3.3 2019-02-05 [2] CRAN (R 4.1.0)
jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.1.0)
jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.1.0)
knitr 1.36 2021-09-29 [1] CRAN (R 4.1.0)
labeling 0.4.2 2020-10-20 [2] CRAN (R 4.1.0)
later 1.2.0 2021-04-23 [2] CRAN (R 4.1.0)
lattice 0.20-44 2021-05-02 [2] CRAN (R 4.1.0)
lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.0)
logisticPCA * 0.2 2016-03-14 [1] CRAN (R 4.1.0)
magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.1.0)
mapproj 1.2.7 2020-02-03 [1] CRAN (R 4.1.0)
maps 3.3.0 2018-04-03 [2] CRAN (R 4.1.0)
Matrix 1.3-4 2021-06-01 [2] CRAN (R 4.1.0)
MatrixGenerics * 1.6.0 2021-10-26 [1] Bioconductor
matrixStats * 0.61.0 2021-09-17 [1] CRAN (R 4.1.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.1.0)
munsell 0.5.0 2018-06-12 [2] CRAN (R 4.1.0)
pals * 1.7 2021-04-17 [1] CRAN (R 4.1.0)
patchwork * 1.1.1 2020-12-17 [2] CRAN (R 4.1.0)
pillar 1.6.4 2021-10-18 [1] CRAN (R 4.1.0)
pkgbuild 1.2.0 2020-12-15 [2] CRAN (R 4.1.0)
pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.1.0)
pkgload 1.2.1 2021-04-06 [2] CRAN (R 4.1.0)
prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.1.0)
processx 3.5.2 2021-04-30 [2] CRAN (R 4.1.0)
promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.1.0)
ps 1.6.0 2021-02-28 [2] CRAN (R 4.1.0)
purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.1.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0)
Rcpp 1.0.7 2021-07-07 [1] CRAN (R 4.1.0)
RCurl 1.98-1.5 2021-09-17 [1] CRAN (R 4.1.0)
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rlang 0.4.12 2021-10-18 [1] CRAN (R 4.1.0)
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[1] /home/jpullin/R/x86_64-pc-linux-gnu-library/4.1
[2] /usr/local/easybuild-2019/easybuild/software/mpi/gcc/10.2.0/openmpi/4.0.5/r/4.1.0/lib64/R/library