suppressPackageStartupMessages(library(tidyverse))
## Warning: package 'ggplot2' was built under R version 4.2.3
## Warning: package 'tidyr' was built under R version 4.2.3
## Warning: package 'readr' was built under R version 4.2.3
## Warning: package 'dplyr' was built under R version 4.2.3
## Warning: package 'stringr' was built under R version 4.2.3
library(survival)
library(survminer)
## Loading required package: ggpubr
##
## Attaching package: 'survminer'
## The following object is masked from 'package:survival':
##
## myeloma
setwd("~/Google Drive/My Drive/Analysis/METTL2A/")
devtools::load_all('~/Google Drive/My Drive/Scripts/R_packages/myUtilities/')
## ℹ Loading myUtilities
# Directory to save figures
fig_expression <- 'Figures/TCGA_GTEx/Expression/'
fig_survival <- 'Figures/TCGA_GTEx/Survival/'
tabledir <- 'Tables/TCGA_GTEx/'
Sys.setenv("VROOM_CONNECTION_SIZE" = 1e+06)
determine_sample_type <- function(df) {
df |>
mutate(
sample_type = case_when(
grepl('Tumor|Primary|Cancer|Metastatic' , `_sample_type`) ~ 'Tumor',
grepl('Normal', `_sample_type`) ~ 'Normal',
.default = NA
)
)
}
fill_primary_site_info <- function(df) {
df |>
mutate(
primary_site = ifelse(
!is.na(primary_site),
yes = primary_site,
no = str_extract(detailed_category, '^([A-Za-z]+)')
)
) |>
mutate(
primary_site = str_remove(primary_site, '\xca')
)
}
convert_primarysite_name <- function(df) {
df |>
mutate(
primary_site_modified = str_replace_all(
primary_site, 'Adrenal gland', 'Adrenal Gland'
)
) |>
mutate(
primary_site_modified = str_replace_all(
primary_site_modified, 'Blood Vessel|White blood cell', 'Blood'
)
)
}
plot_KM <- function(.genename, df, category) {
filtered_df <- df |>
dplyr::filter(gene_name == .genename) |>
dplyr::filter(primary_site_modified == category) |>
mutate(
group = ifelse(norm_count > median(norm_count), 'high', 'low')
)
genename_fit <- surv_fit(
Surv(OS.time, OS) ~ group,
data = filtered_df
)
print(.genename)
print(summary(genename_fit)$table)
survivalplot <-
ggsurvplot(
genename_fit, pval = TRUE, pval.size = 3,
title = paste0(.genename, '\n', category),
palette = c('red', 'blue'),
ggtheme = theme_survminer(base_size = 8),
legend = 'bottom', legend.title = '',
censor.size = .1
)
survivalplot[[1]] |>
ggsave_multiple_formats(
outdir = fig_survival,
basename = paste0(.genename, '_', category),
width = 2.6, height = 4.2, fontsize = 7)
}
Read data
Read survival data
TCGA_survival <-
read_TCGA_survival()
TCGA_survival
## # A tibble: 10,496 × 9
## sample OS OS.time DSS DSS.time DFI DFI.time PFI PFI.time
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCGA-OR-A5J1-01 1 1355 1 1355 1 754 1 754
## 2 TCGA-OR-A5J2-01 1 1677 1 1677 NA NA 1 289
## 3 TCGA-OR-A5J3-01 0 2091 0 2091 1 53 1 53
## 4 TCGA-OR-A5J5-01 1 365 1 365 NA NA 1 50
## 5 TCGA-OR-A5J6-01 0 2703 0 2703 0 2703 0 2703
## 6 TCGA-OR-A5J7-01 1 490 1 490 NA NA 1 162
## 7 TCGA-OR-A5J8-01 1 579 1 579 1 530 1 530
## 8 TCGA-OR-A5J9-01 0 1352 0 1352 1 414 1 414
## 9 TCGA-OR-A5JA-01 1 922 1 922 NA NA 1 922
## 10 TCGA-OR-A5JB-01 1 551 NA 551 NA NA 0 551
## # ℹ 10,486 more rows
Read m3C writer expression
TCGAtargetGTEx_norm_genecounts <-
read_TCGAtargetGTEx_norm.genecount_deseq2()
## New names:
## • `` -> `...1`
TCGAtargetGTEx_norm_genecounts
## # A tibble: 60,498 × 19,041
## gene_id gene_name `TCGA-AD-5900-01` `TCGA-BP-4968-01` `TCGA-NG-A4VU-01`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ENSG00000242… RP11-368… 0 1.81 0
## 2 ENSG00000259… RP11-167… 0 0 0
## 3 ENSG00000270… RP11-742… 0 2.37 4.04
## 4 ENSG00000167… RAB4B 9.39 10.9 8.35
## 5 ENSG00000278… AC104071… 0 0 0
## 6 ENSG00000078… TIGAR 8.44 9.10 8.36
## 7 ENSG00000269… LINC01224 7.08 3.09 6.32
## 8 ENSG00000263… MIR4802 0 0 0
## 9 ENSG00000146… RNF44 11.2 11.9 11.5
## 10 ENSG00000158… DNAH3 5.47 2.77 3.70
## # ℹ 60,488 more rows
## # ℹ 19,036 more variables: `TCGA-CG-4305-01` <dbl>, `TCGA-AO-A03M-01` <dbl>,
## # `TCGA-ZH-A8Y6-01` <dbl>, `TCGA-HT-7686-01` <dbl>, `TCGA-BR-6458-11` <dbl>,
## # `TCGA-29-1699-01` <dbl>, `TCGA-KK-A6E1-01` <dbl>, `TCGA-DX-A7EO-01` <dbl>,
## # `TCGA-AO-A0JD-01` <dbl>, `TCGA-TM-A84Q-01` <dbl>, `TCGA-SR-A6MQ-01` <dbl>,
## # `TCGA-CV-7103-01` <dbl>, `TCGA-EL-A4KD-01` <dbl>, `TCGA-CJ-4890-01` <dbl>,
## # `TCGA-EL-A3ZH-01` <dbl>, `TCGA-A2-A0CT-01` <dbl>, …
TCGAtargetGTEx_gene_TPMs <-
read_TCGAtargetGTEx_rsem_gene_TPM()
TCGAtargetGTEx_gene_TPMs
## # A tibble: 60,498 × 19,133
## gene_id gene_name GTEX-S4Q7-0003-SM-3N…¹ `TCGA-19-1787-01` `TCGA-S9-A7J2-01`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ENSG000… RP11-368… -3.46 -9.97 0.300
## 2 ENSG000… RP11-167… -9.97 -9.97 -9.97
## 3 ENSG000… RP11-742… -3.63 -3.82 -3.05
## 4 ENSG000… RAB4B 4.60 5.30 4.89
## 5 ENSG000… AC104071… -9.97 -9.97 -9.97
## 6 ENSG000… TIGAR 2.26 3.51 2.30
## 7 ENSG000… LINC01224 -6.51 0.865 -1.03
## 8 ENSG000… MIR4802 -9.97 -9.97 -9.97
## 9 ENSG000… RNF44 5.78 4.25 5.38
## 10 ENSG000… DNAH3 -4.29 -5.01 -9.97
## # ℹ 60,488 more rows
## # ℹ abbreviated name: ¹`GTEX-S4Q7-0003-SM-3NM8M`
## # ℹ 19,128 more variables: `GTEX-QV31-1626-SM-2S1QC` <dbl>,
## # `TCGA-G3-A3CH-11` <dbl>, `TCGA-B5-A5OE-01` <dbl>,
## # `GTEX-13QIC-0011-R1a-SM-5O9CJ` <dbl>, `TCGA-B2-5641-11` <dbl>,
## # `GTEX-ZPCL-0126-SM-4WWC8` <dbl>, `TARGET-20-PANGDN-09` <dbl>,
## # `GTEX-S33H-1226-SM-4AD69` <dbl>, `GTEX-X88G-0426-SM-47JZ5` <dbl>, …
m3C_writers_TCGAtargetGTEx_norm_genecounts <-
TCGAtargetGTEx_norm_genecounts |>
filter(grepl('^METTL[2|6|8][AB]?$', gene_name)) |>
pivot_longer(
cols = -c(gene_id, gene_name),
names_to = 'sample', values_to = 'norm_count'
) |>
left_join(TCGAtargetGTEx_sampleinfo) |>
filter(!is.na(sample_type))
## Joining with `by = join_by(sample)`
m3C_writers_TCGAtargetGTEx_norm_genecounts
## # A tibble: 74,700 × 12
## gene_id gene_name sample norm_count detailed_category primary disease or t…¹
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 ENSG000… METTL8 TCGA-… 9.55 Colon Adenocarci… Colon Adenocarcinoma
## 2 ENSG000… METTL8 TCGA-… 9.45 Kidney Clear Cel… Kidney Clear Cell Car…
## 3 ENSG000… METTL8 TCGA-… 10.4 Uterine Carcinos… Uterine Carcinosarcoma
## 4 ENSG000… METTL8 TCGA-… 10.1 Stomach Adenocar… Stomach Adenocarcinoma
## 5 ENSG000… METTL8 TCGA-… 9.49 Breast Invasive … Breast Invasive Carci…
## 6 ENSG000… METTL8 TCGA-… 9.23 Cholangiocarcino… Cholangiocarcinoma
## 7 ENSG000… METTL8 TCGA-… 9.48 Brain Lower Grad… Brain Lower Grade Gli…
## 8 ENSG000… METTL8 TCGA-… 9.34 Stomach Adenocar… Stomach Adenocarcinoma
## 9 ENSG000… METTL8 TCGA-… 9.55 Ovarian Serous C… Ovarian Serous Cystad…
## 10 ENSG000… METTL8 TCGA-… 10.6 Prostate Adenoca… Prostate Adenocarcino…
## # ℹ 74,690 more rows
## # ℹ abbreviated name: ¹`primary disease or tissue`
## # ℹ 6 more variables: primary_site <chr>, `_sample_type` <chr>,
## # `_gender` <chr>, `_study` <chr>, sample_type <chr>,
## # primary_site_modified <chr>
m3C_writers_TCGAtargetGTEx_gene_TPMs <-
TCGAtargetGTEx_gene_TPMs |>
filter(grepl('^METTL[2|6|8][AB]?$', gene_name)) |>
pivot_longer(
cols = -c(gene_id, gene_name),
names_to = 'sample', values_to = 'TPM'
) |>
left_join(TCGAtargetGTEx_sampleinfo) |>
filter(!is.na(sample_type))
## Joining with `by = join_by(sample)`
m3C_writers_TCGAtargetGTEx_gene_TPMs
## # A tibble: 74,788 × 12
## gene_id gene_name sample TPM detailed_category primary disease or t…¹
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 ENSG00000123… METTL8 TCGA-… 3.25 Glioblastoma Mul… Glioblastoma Multifor…
## 2 ENSG00000123… METTL8 TCGA-… 2.52 Brain Lower Grad… Brain Lower Grade Gli…
## 3 ENSG00000123… METTL8 GTEX-… 3.09 Artery - Tibial Artery - Tibial
## 4 ENSG00000123… METTL8 TCGA-… 0.896 Liver Hepatocell… Liver Hepatocellular …
## 5 ENSG00000123… METTL8 TCGA-… 4.38 Uterine Corpus E… Uterine Corpus Endome…
## 6 ENSG00000123… METTL8 GTEX-… 2.06 Brain - Hippocam… Brain - Hippocampus
## 7 ENSG00000123… METTL8 TCGA-… 2.32 Kidney Clear Cel… Kidney Clear Cell Car…
## 8 ENSG00000123… METTL8 GTEX-… 1.90 Thyroid Thyroid
## 9 ENSG00000123… METTL8 TARGE… 1.64 Acute Myeloid Le… Acute Myeloid Leukemia
## 10 ENSG00000123… METTL8 GTEX-… 0.178 Pancreas Pancreas
## # ℹ 74,778 more rows
## # ℹ abbreviated name: ¹`primary disease or tissue`
## # ℹ 6 more variables: primary_site <chr>, `_sample_type` <chr>,
## # `_gender` <chr>, `_study` <chr>, sample_type <chr>,
## # primary_site_modified <chr>
Plot distribution
m3C_writers_TCGAtargetGTEx_norm_genecounts |>
ggplot(aes(
x = primary_site_modified, y = norm_count,
colour = sample_type, fill = sample_type)
) +
geom_violin() +
scale_y_log10() +
coord_flip() +
facet_wrap( ~ gene_name)
## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## Warning: Removed 114 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
Calculate mean expression in normal tissues
median_normcount_in_normal <-
m3C_writers_TCGAtargetGTEx_norm_genecounts |>
filter(sample_type == 'Normal') |>
group_by(gene_name, primary_site_modified) |>
reframe(median_in_normal = median(norm_count, na.rm = TRUE))
median_normcount_in_normal
## # A tibble: 152 × 3
## gene_name primary_site_modified median_in_normal
## <chr> <chr> <dbl>
## 1 METTL2A Adipose Tissue 7.83
## 2 METTL2A Adrenal Gland 8.38
## 3 METTL2A Bile duct 8.87
## 4 METTL2A Bladder 9.24
## 5 METTL2A Blood 7.75
## 6 METTL2A Brain 8.27
## 7 METTL2A Breast 8.12
## 8 METTL2A Cervix 9.75
## 9 METTL2A Cervix Uteri 7.66
## 10 METTL2A Colon 8.14
## # ℹ 142 more rows
# mean_TPM_in_normal <-
# m3C_writers_TCGAtargetGTEx_gene_TPMs |>
# filter(sample_type == 'Normal') |>
# group_by(gene_name, primary_site_modified) |>
# reframe(mean_in_normal = mean(TPM, na.rm = TRUE))
# mean_TPM_in_normal
Calculate relative expression
m3C_writers_TCGAtargetGTEx_rel_expression_normcount <-
m3C_writers_TCGAtargetGTEx_norm_genecounts |>
left_join(median_normcount_in_normal) |>
mutate(rel_expression = norm_count - median_in_normal)
## Joining with `by = join_by(gene_name, primary_site_modified)`
m3C_writers_TCGAtargetGTEx_rel_expression_normcount
## # A tibble: 74,700 × 14
## gene_id gene_name sample norm_count detailed_category primary disease or t…¹
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 ENSG000… METTL8 TCGA-… 9.55 Colon Adenocarci… Colon Adenocarcinoma
## 2 ENSG000… METTL8 TCGA-… 9.45 Kidney Clear Cel… Kidney Clear Cell Car…
## 3 ENSG000… METTL8 TCGA-… 10.4 Uterine Carcinos… Uterine Carcinosarcoma
## 4 ENSG000… METTL8 TCGA-… 10.1 Stomach Adenocar… Stomach Adenocarcinoma
## 5 ENSG000… METTL8 TCGA-… 9.49 Breast Invasive … Breast Invasive Carci…
## 6 ENSG000… METTL8 TCGA-… 9.23 Cholangiocarcino… Cholangiocarcinoma
## 7 ENSG000… METTL8 TCGA-… 9.48 Brain Lower Grad… Brain Lower Grade Gli…
## 8 ENSG000… METTL8 TCGA-… 9.34 Stomach Adenocar… Stomach Adenocarcinoma
## 9 ENSG000… METTL8 TCGA-… 9.55 Ovarian Serous C… Ovarian Serous Cystad…
## 10 ENSG000… METTL8 TCGA-… 10.6 Prostate Adenoca… Prostate Adenocarcino…
## # ℹ 74,690 more rows
## # ℹ abbreviated name: ¹`primary disease or tissue`
## # ℹ 8 more variables: primary_site <chr>, `_sample_type` <chr>,
## # `_gender` <chr>, `_study` <chr>, sample_type <chr>,
## # primary_site_modified <chr>, median_in_normal <dbl>, rel_expression <dbl>
# m3C_writers_TCGAtargetGTEx_rel_expression_TPM <-
# m3C_writers_TCGAtargetGTEx_gene_TPMs |>
# left_join(mean_TPM_in_normal) |>
# mutate(rel_expression = TPM / mean_in_normal)
# m3C_writers_TCGAtargetGTEx_rel_expression_TPM
Plot heatmap
m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary <-
m3C_writers_TCGAtargetGTEx_rel_expression_normcount |>
filter(gene_name != 'ALKBH1') |>
filter(sample_type == 'Tumor') |>
group_by(gene_name, sample_type, primary_site_modified) |>
reframe(
n = n(),
mean = mean(rel_expression),
median = median(rel_expression)
) |>
arrange(-mean)
m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary |>
export_tsv(outdir = tabledir)
##
## Exported to: Tables/TCGA_GTEx/m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary_2024-07-29.tsv
## # A tibble: 120 × 6
## gene_name sample_type primary_site_modified n mean median
## <chr> <chr> <chr> <int> <dbl> <dbl>
## 1 METTL2A Tumor Ovary 427 2.47 2.43
## 2 METTL2A Tumor Uterus 57 2.39 2.40
## 3 METTL2A Tumor Lung 1013 2.35 2.31
## 4 METTL2B Tumor Testis 154 2.30 2.32
## 5 METTL8 Tumor Stomach 413 2.15 2.21
## 6 METTL2B Tumor Uterus 57 2.00 2.04
## 7 METTL8 Tumor Pancreas 179 1.96 1.95
## 8 METTL2B Tumor Ovary 427 1.94 1.99
## 9 METTL2A Tumor Breast 1099 1.94 1.81
## 10 METTL8 Tumor Lung 1013 1.93 1.89
## # ℹ 110 more rows
m3C_relexpression_in_tumor_heatmap <-
m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary |>
filter(!is.na(median)) |>
ggplot(aes(
x = gene_name |> str_remove('METTL'),
y = reorder(primary_site_modified, mean),
fill = mean
)) +
geom_tile() +
scale_fill_gradient2(
low = 'blue', mid = 'white', high = 'red', midpoint = 0,
limits = c(-2.5, 2.5)
) +
labs(x = '', y = '') +
theme_minimal(base_size = 8) +
theme(
legend.position = 'bottom',
axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))
m3C_relexpression_in_tumor_heatmap |>
ggsave_multiple_formats(
outdir = fig_expression,
width = 4.5, height = 9, units = 'cm', fontsize = 7
)
Plot distribution of relative expression
m3C_writers_TCGAtargetGTEx_rel_expression_normcount |>
filter(gene_name != 'ALKBH1') |>
filter(sample_type == 'Tumor') |>
filter(!is.na(rel_expression)) |>
ggplot(aes(
x = reorder(primary_site_modified, rel_expression),
y = rel_expression)) +
geom_violin() +
geom_hline(yintercept = 1) +
scale_y_log10(limits = c(0.4, 2.2), breaks = c(0.5, 0.8, 1, 1.25, 2)) +
labs(x = '', y = 'relative expression (normal = 1)') +
coord_flip() +
facet_wrap( ~ gene_name)
## Warning in transformation$transform(x): NaNs produced
## Warning in scale_y_log10(limits = c(0.4, 2.2), breaks = c(0.5, 0.8, 1, 1.25, :
## log-10 transformation introduced infinite values.
## Warning: Removed 14741 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
m3C_writers_TCGAtargetGTEx_rel_expression_normcount |>
filter(gene_name != 'ALKBH1') |>
filter(sample_type == 'Tumor') |>
ggplot(aes(
x = reorder(primary_site_modified, rel_expression, median),
y = rel_expression)) +
geom_violin() +
geom_hline(yintercept = 1) +
scale_y_log10() +
coord_flip() +
facet_wrap( ~ gene_name)
## Warning in transformation$transform(x): NaNs produced
## Warning in scale_y_log10(): log-10 transformation introduced infinite values.
## Warning: Removed 6898 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
test
m3C_relexpression_in_tumor_pvalues <-
m3C_writers_TCGAtargetGTEx_rel_expression_normcount |>
filter(gene_name != 'ALKBH1') |>
right_join(
m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary |>
select(primary_site_modified) |>
distinct()
) |>
filter(!is.na(rel_expression)) |>
group_by(gene_name, primary_site_modified) |>
rstatix::wilcox_test(rel_expression ~ sample_type, ref.group = 'Normal') |>
left_join(m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary) |>
filter(!is.na(mean))
## Joining with `by = join_by(primary_site_modified)`
## Joining with `by = join_by(gene_name, primary_site_modified)`
m3C_relexpression_in_tumor_pvalues
## # A tibble: 104 × 13
## gene_name primary_site_modified .y. group1 group2 n1 n2 statistic
## <chr> <chr> <chr> <chr> <chr> <int> <int> <dbl>
## 1 METTL2A Adrenal Gland rel_expr… Normal Tumor 126 77 1394
## 2 METTL2A Bile duct rel_expr… Normal Tumor 9 36 3
## 3 METTL2A Bladder rel_expr… Normal Tumor 28 407 2066
## 4 METTL2A Blood rel_expr… Normal Tumor 941 595 71280
## 5 METTL2A Brain rel_expr… Normal Tumor 1153 689 2586.
## 6 METTL2A Breast rel_expr… Normal Tumor 291 1099 22319
## 7 METTL2A Cervix rel_expr… Normal Tumor 3 305 249
## 8 METTL2A Colon rel_expr… Normal Tumor 348 289 5250.
## 9 METTL2A Endometrium rel_expr… Normal Tumor 23 181 707
## 10 METTL2A Esophagus rel_expr… Normal Tumor 667 182 1207
## # ℹ 94 more rows
## # ℹ 5 more variables: p <dbl>, sample_type <chr>, n <int>, mean <dbl>,
## # median <dbl>
m3C_relexpression_in_tumor_pvalues |>
export_tsv(outdir = tabledir)
##
## Exported to: Tables/TCGA_GTEx/m3C_relexpression_in_tumor_pvalues_2024-07-29.tsv
## # A tibble: 104 × 13
## gene_name primary_site_modified .y. group1 group2 n1 n2 statistic
## <chr> <chr> <chr> <chr> <chr> <int> <int> <dbl>
## 1 METTL2A Adrenal Gland rel_expr… Normal Tumor 126 77 1394
## 2 METTL2A Bile duct rel_expr… Normal Tumor 9 36 3
## 3 METTL2A Bladder rel_expr… Normal Tumor 28 407 2066
## 4 METTL2A Blood rel_expr… Normal Tumor 941 595 71280
## 5 METTL2A Brain rel_expr… Normal Tumor 1153 689 2586.
## 6 METTL2A Breast rel_expr… Normal Tumor 291 1099 22319
## 7 METTL2A Cervix rel_expr… Normal Tumor 3 305 249
## 8 METTL2A Colon rel_expr… Normal Tumor 348 289 5250.
## 9 METTL2A Endometrium rel_expr… Normal Tumor 23 181 707
## 10 METTL2A Esophagus rel_expr… Normal Tumor 667 182 1207
## # ℹ 94 more rows
## # ℹ 5 more variables: p <dbl>, sample_type <chr>, n <int>, mean <dbl>,
## # median <dbl>
m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary |>
arrange(median)
## # A tibble: 120 × 6
## gene_name sample_type primary_site_modified n mean median
## <chr> <chr> <chr> <int> <dbl> <dbl>
## 1 METTL8 Tumor Paraganglia 182 -0.451 -0.423
## 2 METTL8 Tumor Soft tissue,Bone 262 -0.385 -0.343
## 3 METTL6 Tumor Testis 154 -0.275 -0.284
## 4 METTL2A Tumor Paraganglia 182 -0.256 -0.225
## 5 METTL8 Tumor Endometrium 181 -0.109 -0.182
## 6 METTL2B Tumor Cervix 305 -0.182 -0.173
## 7 METTL2B Tumor Head and Neck region 520 -0.169 -0.169
## 8 METTL8 Tumor Rectum 93 -0.174 -0.158
## 9 METTL8 Tumor Thyroid Gland 512 -0.169 -0.133
## 10 METTL2A Tumor Rectum 93 -0.0836 -0.0511
## # ℹ 110 more rows
m3C_relexpression_in_tumor_pvalues_heatmap <-
m3C_relexpression_in_tumor_pvalues |>
mutate(
minuslog10p = -log10(p),
group = case_when(
p < .05 & mean > 0 ~ 'A',
p < .05 & mean < 0 ~ 'B',
.default = 'C'
)
) |>
ggplot(aes(
x = gene_name |> str_remove('METTL'),
y = reorder(primary_site_modified, mean),
fill = group
)) +
geom_tile() +
labs(x = '', y = '') +
scale_fill_manual(values = c('red', 'blue', 'gray')) +
theme_minimal(base_size = 8) +
theme(
legend.position = 'bottom',
axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))
m3C_relexpression_in_tumor_pvalues_heatmap |>
ggsave_multiple_formats(
outdir = fig_expression,
width = 4.5, height = 9, units = 'cm', fontsize = 7
)
# m3C_writers_TCGAtargetGTEx_rel_expression_TPM |>
# filter(sample_type == 'Tumor') |>
# ggplot(aes(
# x = reorder(primary_site_modified, rel_expression),
# y = rel_expression)) +
# geom_violin() +
# geom_hline(yintercept = 1) +
# scale_y_log10() +
# coord_flip() +
# facet_wrap( ~ gene_name)
Survival analysis
# m3C_writers_TCGA_gene_TPMs_survival <-
# m3C_writers_TCGAtargetGTEx_gene_TPMs |>
# right_join(TCGA_survival)
# m3C_writers_TCGA_gene_TPMs_survival
m3C_writers_TCGA_norm_genecounts_survival <-
m3C_writers_TCGAtargetGTEx_norm_genecounts |>
right_join(TCGA_survival)
## Joining with `by = join_by(sample)`
m3C_writers_TCGA_norm_genecounts_survival
## # A tibble: 41,969 × 20
## gene_id gene_name sample norm_count detailed_category primary disease or t…¹
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 ENSG000… METTL8 TCGA-… 9.55 Colon Adenocarci… Colon Adenocarcinoma
## 2 ENSG000… METTL8 TCGA-… 9.45 Kidney Clear Cel… Kidney Clear Cell Car…
## 3 ENSG000… METTL8 TCGA-… 10.4 Uterine Carcinos… Uterine Carcinosarcoma
## 4 ENSG000… METTL8 TCGA-… 10.1 Stomach Adenocar… Stomach Adenocarcinoma
## 5 ENSG000… METTL8 TCGA-… 9.49 Breast Invasive … Breast Invasive Carci…
## 6 ENSG000… METTL8 TCGA-… 9.23 Cholangiocarcino… Cholangiocarcinoma
## 7 ENSG000… METTL8 TCGA-… 9.48 Brain Lower Grad… Brain Lower Grade Gli…
## 8 ENSG000… METTL8 TCGA-… 9.34 Stomach Adenocar… Stomach Adenocarcinoma
## 9 ENSG000… METTL8 TCGA-… 9.55 Ovarian Serous C… Ovarian Serous Cystad…
## 10 ENSG000… METTL8 TCGA-… 10.6 Prostate Adenoca… Prostate Adenocarcino…
## # ℹ 41,959 more rows
## # ℹ abbreviated name: ¹`primary disease or tissue`
## # ℹ 14 more variables: primary_site <chr>, `_sample_type` <chr>,
## # `_gender` <chr>, `_study` <chr>, sample_type <chr>,
## # primary_site_modified <chr>, OS <dbl>, OS.time <dbl>, DSS <dbl>,
## # DSS.time <dbl>, DFI <dbl>, DFI.time <dbl>, PFI <dbl>, PFI.time <dbl>
m3C_writers_TCGA_norm_genecounts_survival |>
export_tsv(outdir = tabledir, compression = 'gz')
##
## Exported to: Tables/TCGA_GTEx/m3C_writers_TCGA_norm_genecounts_survival_2024-07-29.tsv.gz
## # A tibble: 41,969 × 20
## gene_id gene_name sample norm_count detailed_category primary disease or t…¹
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 ENSG000… METTL8 TCGA-… 9.55 Colon Adenocarci… Colon Adenocarcinoma
## 2 ENSG000… METTL8 TCGA-… 9.45 Kidney Clear Cel… Kidney Clear Cell Car…
## 3 ENSG000… METTL8 TCGA-… 10.4 Uterine Carcinos… Uterine Carcinosarcoma
## 4 ENSG000… METTL8 TCGA-… 10.1 Stomach Adenocar… Stomach Adenocarcinoma
## 5 ENSG000… METTL8 TCGA-… 9.49 Breast Invasive … Breast Invasive Carci…
## 6 ENSG000… METTL8 TCGA-… 9.23 Cholangiocarcino… Cholangiocarcinoma
## 7 ENSG000… METTL8 TCGA-… 9.48 Brain Lower Grad… Brain Lower Grade Gli…
## 8 ENSG000… METTL8 TCGA-… 9.34 Stomach Adenocar… Stomach Adenocarcinoma
## 9 ENSG000… METTL8 TCGA-… 9.55 Ovarian Serous C… Ovarian Serous Cystad…
## 10 ENSG000… METTL8 TCGA-… 10.6 Prostate Adenoca… Prostate Adenocarcino…
## # ℹ 41,959 more rows
## # ℹ abbreviated name: ¹`primary disease or tissue`
## # ℹ 14 more variables: primary_site <chr>, `_sample_type` <chr>,
## # `_gender` <chr>, `_study` <chr>, sample_type <chr>,
## # primary_site_modified <chr>, OS <dbl>, OS.time <dbl>, DSS <dbl>,
## # DSS.time <dbl>, DFI <dbl>, DFI.time <dbl>, PFI <dbl>, PFI.time <dbl>
temp <-
m3C_writers_TCGA_norm_genecounts_survival |>
filter(gene_name == 'METTL2A') |>
filter(primary_site_modified == 'Pancreas') |>
#group_by(primary_site_modified) |>
mutate(
group = ifelse(norm_count > median(norm_count), 'high', 'low')
)
temp
## # A tibble: 183 × 21
## gene_id gene_name sample norm_count detailed_category primary disease or t…¹
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 ENSG000… METTL2A TCGA-… 9.68 Pancreatic Adeno… Pancreatic Adenocarci…
## 2 ENSG000… METTL2A TCGA-… 9.5 Pancreatic Adeno… Pancreatic Adenocarci…
## 3 ENSG000… METTL2A TCGA-… 9.33 Pancreatic Adeno… Pancreatic Adenocarci…
## 4 ENSG000… METTL2A TCGA-… 9.64 Pancreatic Adeno… Pancreatic Adenocarci…
## 5 ENSG000… METTL2A TCGA-… 9.08 Pancreatic Adeno… Pancreatic Adenocarci…
## 6 ENSG000… METTL2A TCGA-… 9.73 Pancreatic Adeno… Pancreatic Adenocarci…
## 7 ENSG000… METTL2A TCGA-… 10.4 Pancreatic Adeno… Pancreatic Adenocarci…
## 8 ENSG000… METTL2A TCGA-… 9.49 Pancreatic Adeno… Pancreatic Adenocarci…
## 9 ENSG000… METTL2A TCGA-… 10.1 Pancreatic Adeno… Pancreatic Adenocarci…
## 10 ENSG000… METTL2A TCGA-… 9.55 Pancreatic Adeno… Pancreatic Adenocarci…
## # ℹ 173 more rows
## # ℹ abbreviated name: ¹`primary disease or tissue`
## # ℹ 15 more variables: primary_site <chr>, `_sample_type` <chr>,
## # `_gender` <chr>, `_study` <chr>, sample_type <chr>,
## # primary_site_modified <chr>, OS <dbl>, OS.time <dbl>, DSS <dbl>,
## # DSS.time <dbl>, DFI <dbl>, DFI.time <dbl>, PFI <dbl>, PFI.time <dbl>,
## # group <chr>
do_coxph_2group <- function(df) {
df <- df |>
mutate(
group = ifelse(norm_count > median(norm_count), 'high', 'low')
)
coxph(Surv(OS.time, OS) ~ group, df) |>
broom::tidy()
}
temp |>
do_coxph_2group()
## # A tibble: 1 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 grouplow -0.448 0.211 -2.12 0.0336
m3C_writers_TCGA_norm_genecounts_survival_coxph <-
m3C_writers_TCGA_norm_genecounts_survival |>
filter(!is.na(primary_site_modified)) |>
group_by(gene_name, primary_site_modified) |>
nest() |>
mutate(model = map(data, do_coxph_2group)) |>
select(-data) |>
unnest(cols = model) |>
mutate(group = case_when(
estimate > 0 & p.value < .05 ~ 'favorable in high',
estimate < 0 & p.value < .05 ~ 'unfavorable in high',
.default = 'not significant'
)
)
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `model = map(data, do_coxph_2group)`.
## ℹ In group 55: `gene_name = "METTL2B"` and `primary_site_modified = "Testis"`.
## Caused by warning in `coxph.fit()`:
## ! Loglik converged before variable 1 ; coefficient may be infinite.
m3C_writers_TCGA_norm_genecounts_survival_coxph
## # A tibble: 116 × 8
## # Groups: gene_name, primary_site_modified [116]
## gene_name primary_site_modified term estimate std.error statistic p.value
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 METTL8 Colon grouplow 0.00905 0.222 0.0407 9.68e-1
## 2 METTL8 Kidney grouplow -0.589 0.128 -4.61 3.94e-6
## 3 METTL8 Uterus grouplow 0.764 0.351 2.18 2.96e-2
## 4 METTL8 Stomach grouplow -0.185 0.155 -1.20 2.32e-1
## 5 METTL8 Breast grouplow 0.112 0.143 0.785 4.32e-1
## 6 METTL8 Bile duct grouplow 0.122 0.427 0.285 7.75e-1
## 7 METTL8 Brain grouplow -0.418 0.125 -3.35 8.23e-4
## 8 METTL8 Ovary grouplow 0.335 0.124 2.71 6.71e-3
## 9 METTL8 Prostate grouplow 0.343 0.648 0.530 5.96e-1
## 10 METTL8 Soft tissue,Bone grouplow 0.538 0.205 2.63 8.62e-3
## # ℹ 106 more rows
## # ℹ 1 more variable: group <chr>
m3C_writers_TCGA_norm_genecounts_survival_coxph |>
export_tsv(outdir = tabledir)
##
## Exported to: Tables/TCGA_GTEx/m3C_writers_TCGA_norm_genecounts_survival_coxph_2024-07-29.tsv
## # A tibble: 116 × 8
## # Groups: gene_name, primary_site_modified [116]
## gene_name primary_site_modified term estimate std.error statistic p.value
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 METTL8 Colon grouplow 0.00905 0.222 0.0407 9.68e-1
## 2 METTL8 Kidney grouplow -0.589 0.128 -4.61 3.94e-6
## 3 METTL8 Uterus grouplow 0.764 0.351 2.18 2.96e-2
## 4 METTL8 Stomach grouplow -0.185 0.155 -1.20 2.32e-1
## 5 METTL8 Breast grouplow 0.112 0.143 0.785 4.32e-1
## 6 METTL8 Bile duct grouplow 0.122 0.427 0.285 7.75e-1
## 7 METTL8 Brain grouplow -0.418 0.125 -3.35 8.23e-4
## 8 METTL8 Ovary grouplow 0.335 0.124 2.71 6.71e-3
## 9 METTL8 Prostate grouplow 0.343 0.648 0.530 5.96e-1
## 10 METTL8 Soft tissue,Bone grouplow 0.538 0.205 2.63 8.62e-3
## # ℹ 106 more rows
## # ℹ 1 more variable: group <chr>
m3C_prognosis_in_tumors_heatmap <-
m3C_writers_TCGA_norm_genecounts_survival_coxph |>
filter(gene_name != 'ALKBH1') |>
left_join(m3C_writers_TCGAtargetGTEx_rel_expression_normcount_summary) |>
filter(!is.na(mean)) |>
ggplot(aes(
x = gene_name |> str_remove('METTL'),
y = reorder(primary_site_modified, mean),
fill = group
)) +
geom_tile() +
scale_fill_manual(values = c('blue', 'gray', 'red')) +
labs(x = '', y = '') +
theme_minimal(base_size = 8) +
theme(
legend.position = 'bottom',
axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))
## Joining with `by = join_by(gene_name, primary_site_modified)`
m3C_prognosis_in_tumors_heatmap |>
ggsave_multiple_formats(
outdir = fig_survival,
width = 4.5, height = 9, units = 'cm', fontsize = 7
)
#figdir_survival <- 'Figures/TCGA/Survival/Temp/'
plot_KM('METTL2A', m3C_writers_TCGA_norm_genecounts_survival, 'Pancreas')
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 91 91 91 58 875.9087 107.4757 532 470
## group=low 92 92 92 37 1375.0710 164.4187 695 592
## 0.95UCL
## group=high 684
## group=low NA
plot_KM('METTL2A', m3C_writers_TCGA_norm_genecounts_survival, 'Uterus')
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 28 28 28 11 2356.7010 397.0849 3115 714
## group=low 29 29 29 24 710.7536 125.3212 541 418
## 0.95UCL
## group=high NA
## group=low 911
plot_KM('METTL2A', m3C_writers_TCGA_norm_genecounts_survival, 'Ovary')
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 212 212 212 133 1728.303 119.3594 1348 1199
## group=low 213 213 213 132 1791.977 131.1958 1355 1213
## 0.95UCL
## group=high 1492
## group=low 1650
m3C_writers <- c('METTL2A', 'METTL2B', 'METTL6', 'METTL8')
m3C_writers |>
walk(
plot_KM,
df = m3C_writers_TCGA_norm_genecounts_survival,
category = 'Pancreas'
)
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 91 91 91 58 875.9087 107.4757 532 470
## group=low 92 92 92 37 1375.0710 164.4187 695 592
## 0.95UCL
## group=high 684
## group=low NA
## [1] "METTL2B"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 91 91 91 54 958.7456 124.0640 518 466
## group=low 92 92 92 41 1198.6804 152.4417 627 592
## 0.95UCL
## group=high 732
## group=low NA
## [1] "METTL6"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 91 91 91 53 913.1053 132.0717 518 460
## group=low 92 92 92 42 1186.0440 143.4653 666 598
## 0.95UCL
## group=high 691
## group=low NA
## [1] "METTL8"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 91 91 91 50 967.4709 123.8576 593 498
## group=low 92 92 92 45 1206.2863 144.5124 661 545
## 0.95UCL
## group=high 732
## group=low 1130
m3C_writers |>
walk(
plot_KM,
df = m3C_writers_TCGA_norm_genecounts_survival,
category = 'Uterus'
)
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 28 28 28 11 2356.7010 397.0849 3115 714
## group=low 29 29 29 24 710.7536 125.3212 541 418
## 0.95UCL
## group=high NA
## group=low 911
## [1] "METTL2B"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 28 28 28 15 1969.475 385.2990 838 550
## group=low 29 29 29 20 1196.072 220.4269 771 522
## 0.95UCL
## group=high NA
## group=low NA
## [1] "METTL6"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 28 28 28 15 1769.479 372.0517 1526 714
## group=low 29 29 29 20 1102.441 225.2465 667 522
## 0.95UCL
## group=high NA
## group=low NA
## [1] "METTL8"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 28 28 28 15 1849.7537 348.2840 1591 714
## group=low 29 29 29 20 986.3187 199.6186 611 378
## 0.95UCL
## group=high NA
## group=low NA
m3C_writers |>
walk(
plot_KM,
df = m3C_writers_TCGA_norm_genecounts_survival,
category = 'Ovary'
)
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 212 212 212 133 1728.303 119.3594 1348 1199
## group=low 213 213 213 132 1791.977 131.1958 1355 1213
## 0.95UCL
## group=high 1492
## group=low 1650
## [1] "METTL2B"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 211 211 211 137 1745.609 124.6838 1359 1249
## group=low 214 214 214 128 1788.410 127.9364 1336 1155
## 0.95UCL
## group=high 1492
## group=low 1699
## [1] "METTL6"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 213 213 213 127 1883.474 143.47617 1348 1189
## group=low 212 212 212 138 1656.316 99.64171 1369 1213
## 0.95UCL
## group=high 1492
## group=low 1583
## [1] "METTL8"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 212 212 212 126 2021.881 141.54335 1492 1359
## group=low 213 213 213 139 1512.350 96.29616 1247 1088
## 0.95UCL
## group=high 1725
## group=low 1369
m3C_writers |>
walk(
plot_KM,
df = m3C_writers_TCGA_norm_genecounts_survival,
category = 'Lung'
)
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 553 553 553 221 2212.573 174.0980 1485 1338
## group=low 554 554 554 230 2612.685 210.8861 1655 1258
## 0.95UCL
## group=high 1736
## group=low 1975
## [1] "METTL2B"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 555 555 555 209 2649.204 231.9337 1499 1315
## group=low 552 552 552 242 2069.752 117.0246 1531 1258
## 0.95UCL
## group=high 1798
## group=low 1912
## [1] "METTL6"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 555 555 555 231 2277.850 197.3345 1423 1229
## group=low 552 552 552 220 2515.205 204.2985 1679 1379
## 0.95UCL
## group=high 1713
## group=low 2160
## [1] "METTL8"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 552 552 552 234 2252.22 187.8763 1499 1288
## group=low 555 555 555 217 2561.99 220.5698 1528 1344
## 0.95UCL
## group=high 1933
## group=low 1790
m3C_writers |>
walk(
plot_KM,
df = m3C_writers_TCGA_norm_genecounts_survival,
category = 'Stomach'
)
## [1] "METTL2A"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 220 220 220 83 1467.923 181.1143 940 762
## group=low 222 222 222 84 1864.486 163.4992 1095 652
## 0.95UCL
## group=high 2197
## group=low NA
## [1] "METTL2B"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 220 220 220 82 1528.919 198.9949 1043 766
## group=low 222 222 222 85 1764.579 177.2954 881 652
## 0.95UCL
## group=high NA
## group=low NA
## [1] "METTL6"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 222 222 222 91 1570.857 187.5039 779 640
## group=low 220 220 220 76 1763.544 181.3018 1294 869
## 0.95UCL
## group=high NA
## group=low NA
## [1] "METTL8"
## records n.max n.start events rmean se(rmean) median 0.95LCL
## group=high 220 220 220 84 1449.755 196.2948 940 675
## group=low 222 222 222 83 1796.860 186.3028 1095 779
## 0.95UCL
## group=high NA
## group=low NA
Expression in tumor and normal
#
m3C_writers_TCGAtargetGTEx_norm_genecounts_Pancreas <-
m3C_writers_TCGAtargetGTEx_norm_genecounts |>
filter(primary_site_modified == 'Pancreas')
m3C_writers_TCGAtargetGTEx_norm_genecounts_Pancreas |>
export_tsv(outdir = tabledir)
##
## Exported to: Tables/TCGA_GTEx/m3C_writers_TCGAtargetGTEx_norm_genecounts_Pancreas_2024-07-29.tsv
## # A tibble: 1,400 × 12
## gene_id gene_name sample norm_count detailed_category primary disease or t…¹
## <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 ENSG000… METTL8 TCGA-… 10.6 Pancreatic Adeno… Pancreatic Adenocarci…
## 2 ENSG000… METTL8 TCGA-… 9.54 Pancreatic Adeno… Pancreatic Adenocarci…
## 3 ENSG000… METTL8 TCGA-… 10.7 Pancreatic Adeno… Pancreatic Adenocarci…
## 4 ENSG000… METTL8 TCGA-… 9.30 Pancreatic Adeno… Pancreatic Adenocarci…
## 5 ENSG000… METTL8 TCGA-… 9.43 Pancreatic Adeno… Pancreatic Adenocarci…
## 6 ENSG000… METTL8 TCGA-… 10.7 Pancreatic Adeno… Pancreatic Adenocarci…
## 7 ENSG000… METTL8 TCGA-… 9.72 Pancreatic Adeno… Pancreatic Adenocarci…
## 8 ENSG000… METTL8 TCGA-… 10.0 Pancreatic Adeno… Pancreatic Adenocarci…
## 9 ENSG000… METTL8 TCGA-… 9.43 Pancreatic Adeno… Pancreatic Adenocarci…
## 10 ENSG000… METTL8 TCGA-… 9.58 Pancreatic Adeno… Pancreatic Adenocarci…
## # ℹ 1,390 more rows
## # ℹ abbreviated name: ¹`primary disease or tissue`
## # ℹ 6 more variables: primary_site <chr>, `_sample_type` <chr>,
## # `_gender` <chr>, `_study` <chr>, sample_type <chr>,
## # primary_site_modified <chr>
m3C_writers_TCGAtargetGTEx_norm_genecounts |>
filter(primary_site_modified == 'Pancreas') |>
group_by(gene_name) |>
rstatix::wilcox_test(norm_count ~ sample_type)
## # A tibble: 4 × 8
## gene_name .y. group1 group2 n1 n2 statistic p
## * <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
## 1 METTL2A norm_count Normal Tumor 171 179 183 1.74e-57
## 2 METTL2B norm_count Normal Tumor 171 179 183 1.74e-57
## 3 METTL6 norm_count Normal Tumor 171 179 402. 6.90e-56
## 4 METTL8 norm_count Normal Tumor 171 179 674 6.29e-54
m3C_writers_expression_pancreas_violin <-
m3C_writers_TCGAtargetGTEx_norm_genecounts |>
filter(primary_site_modified == 'Pancreas') |>
ggplot(aes(
x = sample_type |> str_remove('ormal|umor'),
y = norm_count,
fill = sample_type,
#colour = sample_type
)) +
geom_violin(lwd = .1) +
geom_boxplot(width = .1, fill = 'white', coef = Inf, lwd = .1) +
#ggforce::geom_sina(size = .2) +
facet_wrap( ~ gene_name, scales = 'free_x', nrow = 1) +
#scale_x_discrete(guide = ggh4x::guide_axis_nested(delim = '.') ) +
scale_y_continuous(limits = c(0, 13)) +
scale_fill_manual(values = c('blue', 'red')) +
labs(x = '', y = 'log2 (normalized count + 1)') +
#coord_flip() +
theme_classic(base_size = 7) +
theme(
legend.position = 'bottom',
# axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1)
)
m3C_writers_expression_pancreas_violin |>
ggsave_multiple_formats(
outdir = fig_expression,
width = 7, height = 4, fontsize = 7
)
Sesison info
sessioninfo::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.2 (2022-10-31)
## os macOS 14.5
## system aarch64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Tokyo
## date 2024-07-29
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0)
## backports 1.5.0 2024-05-23 [1] CRAN (R 4.2.3)
## bit 4.0.5 2022-11-15 [1] CRAN (R 4.2.0)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
## broom 1.0.6 2024-05-17 [1] CRAN (R 4.2.3)
## bslib 0.7.0 2024-03-29 [1] CRAN (R 4.2.3)
## cachem 1.1.0 2024-05-16 [1] CRAN (R 4.2.3)
## car 3.1-2 2023-03-30 [1] CRAN (R 4.2.0)
## carData 3.0-5 2022-01-06 [1] CRAN (R 4.2.0)
## class 7.3-22 2023-05-03 [1] CRAN (R 4.2.0)
## cli 3.6.2 2023-12-11 [1] CRAN (R 4.2.3)
## codetools 0.2-20 2024-03-31 [1] CRAN (R 4.2.3)
## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
## curl 5.2.1 2024-03-01 [1] CRAN (R 4.2.3)
## data.table 1.15.4 2024-03-30 [1] CRAN (R 4.2.3)
## desc 1.4.3 2023-12-10 [1] CRAN (R 4.2.3)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
## dials 1.2.1 2024-02-22 [1] CRAN (R 4.2.3)
## DiceDesign 1.10 2023-12-07 [1] CRAN (R 4.2.3)
## digest 0.6.35 2024-03-11 [1] CRAN (R 4.2.3)
## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.2.3)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
## evaluate 0.24.0 2024-06-10 [1] CRAN (R 4.2.2)
## fansi 1.0.6 2023-12-08 [1] CRAN (R 4.2.3)
## farver 2.1.2 2024-05-13 [1] CRAN (R 4.2.3)
## fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.2.3)
## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.0)
## foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.0)
## fs 1.6.4 2024-04-25 [1] CRAN (R 4.2.3)
## furrr 0.3.1 2022-08-15 [1] CRAN (R 4.2.0)
## future 1.33.2 2024-03-26 [1] CRAN (R 4.2.3)
## future.apply 1.11.2 2024-03-28 [1] CRAN (R 4.2.3)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
## ggforce 0.4.2 2024-02-19 [1] CRAN (R 4.2.3)
## ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.2.3)
## ggpubr * 0.6.0 2023-02-10 [1] CRAN (R 4.2.0)
## ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.2.3)
## ggsignif 0.6.4 2022-10-13 [1] CRAN (R 4.2.0)
## globals 0.16.3 2024-03-08 [1] CRAN (R 4.2.3)
## glue 1.7.0 2024-01-09 [1] CRAN (R 4.2.3)
## gower 1.0.1 2022-12-22 [1] CRAN (R 4.2.0)
## GPfit 1.0-8 2019-02-08 [1] CRAN (R 4.2.0)
## gprofiler2 0.2.3 2024-02-23 [1] CRAN (R 4.2.3)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.2.0)
## gtable 0.3.5 2024-04-22 [1] CRAN (R 4.2.3)
## hardhat 1.4.0 2024-06-02 [1] CRAN (R 4.2.2)
## highr 0.11 2024-05-26 [1] CRAN (R 4.2.3)
## hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0)
## htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.2.3)
## htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.2.3)
## httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.2.3)
## httr 1.4.7 2023-08-15 [1] CRAN (R 4.2.0)
## ipred 0.9-14 2023-03-09 [1] CRAN (R 4.2.0)
## iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
## jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.2.3)
## km.ci 0.5-6 2022-04-06 [1] CRAN (R 4.2.0)
## KMsurv 0.1-5 2012-12-03 [1] CRAN (R 4.2.0)
## knitr 1.47 2024-05-29 [1] CRAN (R 4.2.3)
## labeling 0.4.3 2023-08-29 [1] CRAN (R 4.2.0)
## later 1.3.2 2023-12-06 [1] CRAN (R 4.2.3)
## lattice 0.22-6 2024-03-20 [1] CRAN (R 4.2.3)
## lava 1.8.0 2024-03-05 [1] CRAN (R 4.2.3)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.2.0)
## lhs 1.1.6 2022-12-17 [1] CRAN (R 4.2.0)
## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.2.3)
## listenv 0.9.1 2024-01-29 [1] CRAN (R 4.2.3)
## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.2.0)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
## MASS 7.3-60.0.1 2024-01-13 [1] CRAN (R 4.2.3)
## Matrix 1.6-5 2024-01-11 [1] CRAN (R 4.2.3)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
## munsell 0.5.1 2024-04-01 [1] CRAN (R 4.2.3)
## R myUtilities * 0.0.0.9000 <NA> [?] <NA>
## nnet 7.3-19 2023-05-03 [1] CRAN (R 4.2.0)
## parallelly 1.37.1 2024-02-29 [1] CRAN (R 4.2.3)
## parsnip 1.2.1 2024-03-22 [1] CRAN (R 4.2.3)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
## pkgbuild 1.4.4 2024-03-17 [1] CRAN (R 4.2.3)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
## pkgload 1.3.4 2024-01-16 [1] CRAN (R 4.2.3)
## plotly 4.10.4 2024-01-13 [1] CRAN (R 4.2.3)
## polyclip 1.10-6 2023-09-27 [1] CRAN (R 4.2.0)
## prodlim 2023.08.28 2023-08-28 [1] CRAN (R 4.2.0)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
## promises 1.3.0 2024-04-05 [1] CRAN (R 4.2.3)
## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.0)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
## ragg 1.3.2 2024-05-15 [1] CRAN (R 4.2.3)
## Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.2.3)
## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.2.3)
## recipes 1.0.10 2024-02-18 [1] CRAN (R 4.2.3)
## remotes 2.5.0 2024-03-17 [1] CRAN (R 4.2.3)
## rlang 1.1.4 2024-06-04 [1] CRAN (R 4.2.2)
## rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.2.3)
## rpart 4.1.23 2023-12-05 [1] CRAN (R 4.2.3)
## rprojroot 2.0.4 2023-11-05 [1] CRAN (R 4.2.0)
## rsample 1.2.1 2024-03-25 [1] CRAN (R 4.2.3)
## rstatix 0.7.2 2023-02-01 [1] CRAN (R 4.2.0)
## rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.2.3)
## sass 0.4.9 2024-03-15 [1] CRAN (R 4.2.3)
## scales 1.3.0 2023-11-28 [1] CRAN (R 4.2.3)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
## shiny 1.8.1.1 2024-04-02 [1] CRAN (R 4.2.3)
## stringi 1.8.4 2024-05-06 [1] CRAN (R 4.2.3)
## stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.2.3)
## survival * 3.7-0 2024-06-05 [1] CRAN (R 4.2.2)
## survminer * 0.4.9.999 2022-11-21 [1] Github (kassambara/survminer@5ce1833)
## survMisc 0.5.6 2022-04-07 [1] CRAN (R 4.2.0)
## svglite 2.1.3 2023-12-08 [1] CRAN (R 4.2.3)
## systemfonts 1.1.0 2024-05-15 [1] CRAN (R 4.2.3)
## textshaping 0.4.0 2024-05-24 [1] CRAN (R 4.2.3)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.2.3)
## tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.2.3)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.0)
## timechange 0.3.0 2024-01-18 [1] CRAN (R 4.2.3)
## timeDate 4032.109 2023-12-14 [1] CRAN (R 4.2.3)
## tune 1.2.1 2024-04-18 [1] CRAN (R 4.2.3)
## tweenr 2.0.3 2024-02-26 [1] CRAN (R 4.2.3)
## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.2.3 2024-02-19 [1] CRAN (R 4.2.3)
## utf8 1.2.4 2023-10-22 [1] CRAN (R 4.2.0)
## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.2.3)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## vroom 1.6.5 2023-12-05 [1] CRAN (R 4.2.3)
## withr 3.0.0 2024-01-16 [1] CRAN (R 4.2.3)
## workflows 1.1.4 2024-02-19 [1] CRAN (R 4.2.3)
## xfun 0.44 2024-05-15 [1] CRAN (R 4.2.3)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## yaml 2.3.8 2023-12-11 [1] CRAN (R 4.2.3)
## yardstick 1.3.1 2024-03-21 [1] CRAN (R 4.2.3)
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
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## [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
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## R ── Package was removed from disk.
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