Cut and Tag QC
Steven Yu
2025-07-01
Last updated: 2025-07-02
Checks: 7 0
Knit directory: WardLab/
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Cut And Tag QC
Bioconductor
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install()Loading Packages
library(tidyverse)
library(readr)
library(kableExtra)
library(DT)
library(edgeR)
library(ComplexHeatmap)
library(data.table)
library(genomation)
library(GenomicRanges)Data Initialization
sampleinfo <- read_delim("data/sample_info.tsv", delim = "\t")
multiqc_gene_stats_trim <- read_delim("data/multiqc_data_trim/multiqc_general_stats.txt",delim = "\t")
multiqc_fastqc_trim <- read_delim("data/multiqc_data_trim/multiqc_fastqc.txt",delim = "\t")Functions
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
pca_plot <-
function(df,
col_var = NULL,
shape_var = NULL,
title = "") {
ggplot(df) + geom_point(aes_string(
x = "PC1",
y = "PC2",
color = col_var,
shape = shape_var
),
size = 5) +
labs(title = title, x = "PC 1", y = "PC 2") +
scale_color_manual(values = c(
"#8B006D",
"#DF707E",
"#F1B72B",
"#3386DD",
"#707031",
"#41B333"
))
}
pca_var_plot <- function(pca) {
# x: class == prcomp
pca.var <- pca$sdev ^ 2
pca.prop <- pca.var / sum(pca.var)
var.plot <-
qplot(PC, prop, data = data.frame(PC = 1:length(pca.prop),
prop = pca.prop)) +
labs(title = 'Variance contributed by each PC',
x = 'PC', y = 'Proportion of variance')
plot(var.plot)
}
calc_pca <- function(x) {
# Performs principal components analysis with prcomp
# x: a sample-by-gene numeric matrix
prcomp(x, scale. = TRUE, retx = TRUE)
}
get_regr_pval <- function(mod) {
# Returns the p-value for the Fstatistic of a linear model
# mod: class lm
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$fstatistic
pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
return(pval)
}
plot_versus_pc <- function(df, pc_num, fac) {
# df: data.frame
# pc_num: numeric, specific PC for plotting
# fac: column name of df for plotting against PC
pc_char <- paste0("PC", pc_num)
# Calculate F-statistic p-value for linear model
pval <- get_regr_pval(lm(df[, pc_char] ~ df[, fac]))
if (is.numeric(df[, f])) {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_point() +
geom_smooth(method = "lm") + labs(title = sprintf("p-val: %.2f", pval))
} else {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_boxplot() +
labs(title = sprintf("p-val: %.2f", pval))
}
}
x_axis_labels = function(labels, every_nth = 1, ...) {
axis(side = 1,
at = seq_along(labels),
labels = F)
text(
x = (seq_along(labels))[seq_len(every_nth) == 1],
y = par("usr")[3] - 0.075 * (par("usr")[4] - par("usr")[3]),
labels = labels[seq_len(every_nth) == 1],
xpd = TRUE,
...
)
}Analysis
Data Processing
combo_trim_df <- multiqc_fastqc_trim %>%
extract(., Sample, into = c("prefix","read"), regex= "(.+)_R(\\d+)", remove=FALSE) %>%
mutate(read = paste0("R", read)) %>%
left_join(., sampleinfo, by =c("prefix"="Library ID")) %>%
left_join(., multiqc_gene_stats_trim, by = c("Sample" = "Sample")) %>%
mutate(ind=factor(Individual, levels = c("1","2","3","4","5"))) %>%
mutate(trt=factor(Treatment, levels = c("VEH","5FU","DOX"))) %>%
mutate(time=factor(Timepoint, levels=c("24T","24R","144R")))
combo_trim_df <- combo_trim_df[(!combo_trim_df$trt %in% "5FU"),]Data Visualization
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
group_by(trt, time, Histone_Mark) %>%
tally() %>%
ggplot(., aes(x = time, y= n))+
geom_col(position="dodge",aes(fill=trt)) +
facet_wrap(~Histone_Mark)+
theme(axis.text.x=element_text(angle=90))+
ylab("number of samples")+
ggtitle("Breakdown of samples by mark and trt-time")
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
group_by(trt,time,Histone_Mark) %>%
tally() %>%
pivot_wider(., id_cols=c(trt,time), names_from = Histone_Mark, values_from = n) %>%
kable(.,caption = ("Sample counts")) %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE,font_size = 16) %>%
scroll_box(width = "100%", height = "500px")| trt | time | H3K27ac | H3K27me3 | H3K36me3 | H3K9me3 |
|---|---|---|---|---|---|
| VEH | 24T | 4 | 5 | 4 | 5 |
| VEH | 24R | 6 | 5 | 5 | 5 |
| VEH | 144R | 5 | 5 | 5 | 5 |
| DOX | 24T | 5 | 5 | 4 | 5 |
| DOX | 24R | 5 | 5 | 5 | 4 |
| DOX | 144R | 5 | 4 | 5 | 5 |
Visualization of Counts
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
ggplot(., aes(x = Sample, y= `Total Sequences`))+
geom_col(aes(fill=Histone_Mark)) +
theme(axis.text.x=element_text(vjust = .2,angle=90))+
ylab("sequene count")+
ggtitle("Read counts by sample and histone mark trimmed adapters")+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
ggplot(., aes(x = Histone_Mark, y= `Total Sequences`))+
geom_boxplot(aes(fill=Histone_Mark)) +
geom_point(aes(color=ind))+
facet_wrap(trt~time)+
ylab("count")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
ggtitle("Sequence count by trt and time trimmed adapter")
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
Trim Info
combo_trim_df %>%
ggplot(., aes(x = read, y= avg_sequence_length))+
geom_boxplot(aes(fill=read))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
ggplot(., aes(x = read, y= avg_sequence_length))+
geom_boxplot(aes(fill=Histone_Mark)) +
ggtitle("Boxplot of trim read length across histone marks")
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
datatable(., options = list(scrollX = TRUE,
scrollY = "400px",
scrollCollapse = TRUE,
fixedColumns = list(leftColumns =2),
fixedHeader= TRUE),
extensions = c("FixedColumns","Scroller"),
class = "display")combo_trim_df %>%
dplyr::filter(read=="R1") %>%
ggplot(., aes(x = Sample, y= avg_sequence_length))+
geom_col(aes(fill=Histone_Mark)) +
geom_hline( yintercept = 75)+
theme_classic()+
ggtitle("Graph of average read length across R1 samples")+ theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R2") %>%
ggplot(., aes(x = Sample, y= avg_sequence_length))+
geom_col(aes(fill=Histone_Mark)) +
geom_hline( yintercept = 75)+
theme_classic()+
ggtitle("Graph of average read length across R2 samples")+ theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
ggplot(., aes(x = Sample, y= `%GC`))+
geom_col(aes(fill=Histone_Mark)) +
theme_classic()+
ggtitle("Graph of %GC for R1 trimmed")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R2") %>%
ggplot(., aes(x = Sample, y= `%GC`))+
geom_col(aes(fill=Histone_Mark)) +
theme_classic()+
ggtitle("Graph of %GC for R2 trimmed")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
Duplication Info
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
ggplot(., aes(x = Histone_Mark, y= `FastQC_mqc-generalstats-fastqc-percent_duplicates`))+
geom_boxplot(aes(fill=Histone_Mark)) +
geom_point(aes(color=ind))+
facet_wrap(trt~time)+
ylab("percent duplication")+
theme(axis.text.x=element_text(angle=90))+
ggtitle("Duplication percentage (R1 trimmed)")
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
ggplot(., aes(x = interaction(time,trt), y= `FastQC_mqc-generalstats-fastqc-percent_duplicates`))+
geom_boxplot(aes(fill=Histone_Mark)) +
geom_point(aes(color=ind))+
facet_wrap(~Histone_Mark)+
ylab("percent duplication")+
theme(axis.text.x=element_text(angle=90))+
ggtitle("Duplication percentage (R1 trimmed)")
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R1") %>%
ggplot(., aes(x = Sample, y= `FastQC_mqc-generalstats-fastqc-percent_duplicates`))+
geom_col(aes(fill=Histone_Mark)) +
theme_classic()+
ggtitle("Graph of percent duplicates for R1 trimmed")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( limits = c(0,100),expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
combo_trim_df %>%
dplyr::filter(read=="R2") %>%
ggplot(., aes(x = Sample, y= `FastQC_mqc-generalstats-fastqc-percent_duplicates`))+
geom_col(aes(fill=Histone_Mark)) +
theme_classic()+
ggtitle("Graph of percent duplicates for R2 trimmed")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous(limits = c(0,100), expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| 0644589 | infurnoheat | 2025-07-02 |
sessionInfo()R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0 IRanges_2.42.0
[4] S4Vectors_0.46.0 BiocGenerics_0.54.0 generics_0.1.4
[7] genomation_1.40.1 data.table_1.17.6 ComplexHeatmap_2.24.1
[10] edgeR_4.6.2 limma_3.64.1 DT_0.33
[13] kableExtra_1.4.0 lubridate_1.9.4 forcats_1.0.0
[16] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4
[19] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
[22] ggplot2_3.5.2 tidyverse_2.0.0 BiocManager_1.30.26
[25] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] bitops_1.0-9 rlang_1.1.6
[3] magrittr_2.0.3 gridBase_0.4-7
[5] clue_0.3-66 GetoptLong_1.0.5
[7] git2r_0.36.2 matrixStats_1.5.0
[9] compiler_4.5.1 getPass_0.2-4
[11] reshape2_1.4.4 png_0.1-8
[13] systemfonts_1.2.3 callr_3.7.6
[15] vctrs_0.6.5 pkgconfig_2.0.3
[17] shape_1.4.6.1 crayon_1.5.3
[19] fastmap_1.2.0 XVector_0.48.0
[21] labeling_0.4.3 Rsamtools_2.24.0
[23] promises_1.3.3 rmarkdown_2.29
[25] tzdb_0.5.0 UCSC.utils_1.4.0
[27] ps_1.9.1 bit_4.6.0
[29] xfun_0.52 cachem_1.1.0
[31] jsonlite_2.0.0 later_1.4.2
[33] DelayedArray_0.34.1 BiocParallel_1.42.1
[35] parallel_4.5.1 cluster_2.1.8.1
[37] R6_2.6.1 bslib_0.9.0
[39] stringi_1.8.7 RColorBrewer_1.1-3
[41] rtracklayer_1.68.0 jquerylib_0.1.4
[43] SummarizedExperiment_1.38.1 Rcpp_1.0.14
[45] iterators_1.0.14 knitr_1.50
[47] Matrix_1.7-3 httpuv_1.6.16
[49] timechange_0.3.0 tidyselect_1.2.1
[51] seqPattern_1.40.0 abind_1.4-8
[53] rstudioapi_0.17.1 yaml_2.3.10
[55] doParallel_1.0.17 codetools_0.2-20
[57] curl_6.4.0 processx_3.8.6
[59] plyr_1.8.9 lattice_0.22-7
[61] Biobase_2.68.0 withr_3.0.2
[63] evaluate_1.0.4 xml2_1.3.8
[65] circlize_0.4.16 Biostrings_2.76.0
[67] pillar_1.10.2 MatrixGenerics_1.20.0
[69] KernSmooth_2.23-26 whisker_0.4.1
[71] foreach_1.5.2 vroom_1.6.5
[73] RCurl_1.98-1.17 rprojroot_2.0.4
[75] hms_1.1.3 scales_1.4.0
[77] glue_1.8.0 tools_4.5.1
[79] BiocIO_1.18.0 GenomicAlignments_1.44.0
[81] BSgenome_1.76.0 locfit_1.5-9.12
[83] fs_1.6.6 XML_3.99-0.18
[85] plotrix_3.8-4 impute_1.82.0
[87] crosstalk_1.2.1 colorspace_2.1-1
[89] GenomeInfoDbData_1.2.14 restfulr_0.0.16
[91] cli_3.6.5 textshaping_1.0.1
[93] S4Arrays_1.8.1 viridisLite_0.4.2
[95] svglite_2.2.1 gtable_0.3.6
[97] sass_0.4.10 digest_0.6.37
[99] SparseArray_1.8.0 rjson_0.2.23
[101] htmlwidgets_1.6.4 farver_2.1.2
[103] htmltools_0.5.8.1 lifecycle_1.0.4
[105] httr_1.4.7 GlobalOptions_0.1.2
[107] statmod_1.5.0 bit64_4.6.0-1