Cut and Tag QC
Steven Yu
2025-07-01
Last updated: 2025-08-20
Checks: 7 0
Knit directory: DXR_continue/
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Cut And Tag QC
Loading Packages
library(tidyverse)
library(readr)
library(edgeR)
library(ComplexHeatmap)
library(data.table)
library(dplyr)
library(stringr)
library(ggplot2)
library(viridis)
library(DT)
library(kableExtra)
library(genomation)
library(GenomicRanges)
library(chromVAR) ## For FRiP analysis and differential analysis
library(DESeq2) ## For differential analysis section
library(ggpubr) ## For customizing figures
library(corrplot) ## For correlation plot
library(ggpmisc)
library(gcplyr)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,
...
)
}Basic Stats
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("Ind1","Ind2","Ind3","Ind4","Ind5"))) %>%
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")
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 | 5 | 5 | 4 | 5 |
| VEH | 24R | 5 | 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)))
Tagging Questionable Libraries by Counts
questionable_ct <- combo_trim_df %>%
dplyr::filter(`Total Sequences` < 2e6) %>%
dplyr::select(Sample, `Total Sequences`) %>% distinct()
questionable_ct# A tibble: 4 × 2
Sample `Total Sequences`
<chr> <dbl>
1 MCW_CT_ROA_175_R1_trim 734837
2 MCW_CT_ROA_175_R2_trim 734837
3 MCW_CT_ROA_214_R1_trim 1569281
4 MCW_CT_ROA_214_R2_trim 1569281combo_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")
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")
combo_trim_df %>%
datatable(., options = list(scrollX = TRUE,
scrollY = "400px",
scrollCollapse = TRUE,
fixedColumns = list(leftColumns =2),
fixedHeader= TRUE),
extensions = c("FixedColumns","Scroller"),
class = "display")# write_delim(combo_trim_df,"data/multiqc_data_trim/Summary_of_multiqcfiles.txt",delim = "\t")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)))
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)))
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)))
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)))
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)")
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)")
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)))
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)))
Alignment Analysis
Data Initialization
alignResult = c()
for(sample in sampleinfo$`Library ID`){
alignRes = read.table(paste0("data/sams/", sample, ".log"), header = FALSE, fill = TRUE)
alignRate = substr(alignRes$V1[6], 1, nchar(as.character(alignRes$V1[6]))-1)
alignResult = data.frame(ID = sample,
Paired_Reads = alignRes$V1[1] %>% as.character %>% as.numeric,
aligned_concordant_0 = alignRes$V1[3] %>% as.character %>% as.numeric,
aligned_concordant_1 = alignRes$V1[4] %>% as.character %>% as.numeric,
aligned_concordant_g1 = alignRes$V1[5] %>% as.character %>% as.numeric,
MappedFragNum_hg38 = alignRes$V1[4] %>% as.character %>% as.numeric + alignRes$V1[5] %>% as.character %>% as.numeric,
percent_alignment = alignRate %>% as.numeric) %>% rbind(alignResult, .)
}
alignResult %>% mutate(percent_alignment = paste0(percent_alignment, "%")) ID Paired_Reads aligned_concordant_0 aligned_concordant_1
1 MCW_CT_ROA_78 8514537 393880 4454677
2 MCW_CT_ROA_79 17230410 750321 10921835
3 MCW_CT_ROA_80 11073255 513904 6094802
4 MCW_CT_ROA_81 10304161 342605 6909479
5 MCW_CT_ROA_082 8830034 388284 5998847
6 MCW_CT_ROA_83 13873472 496709 9607312
7 MCW_CT_ROA_84 9460829 320932 6281523
8 MCW_CT_ROA_85 17871310 609752 12887951
9 MCW_CT_ROA_86 7129722 264900 4998439
10 MCW_CT_ROA_87 10992015 398747 7180888
11 MCW_CT_ROA_88 6136901 231003 4367664
12 MCW_CT_ROA_89 10476954 351625 7372185
13 MCW_CT_ROA_090 16578704 608091 11417139
14 MCW_CT_ROA_091 9570470 354970 6845785
15 MCW_CT_ROA_093 10733977 519575 7639342
16 MCW_CT_ROA_94 7398484 206181 5202764
17 MCW_CT_ROA_95 9244604 533245 5462429
18 MCW_CT_ROA_96 8197575 503975 4828923
19 MCW_CT_ROA_97 6738747 290949 4109592
20 MCW_CT_ROA_99 8097526 351319 5337230
21 MCW_CT_ROA_100 9646404 486736 5746445
22 MCW_CT_ROA_101 5971251 172784 4279384
23 MCW_CT_ROA_102 8994528 397337 5895789
24 MCW_CT_ROA_103 4663599 213987 3024586
25 MCW_CT_ROA_105 10410306 643616 6984311
26 MCW_CT_ROA_106 16439638 740825 10332902
27 MCW_CT_ROA_107 14094464 546132 9669822
28 MCW_CT_ROA_108 18259872 1011915 11565498
29 MCW_CT_ROA_109 13689268 694161 8788342
30 MCW_CT_ROA_111 11910243 633389 7877522
31 MCW_CT_ROA_112 12803412 584653 9061482
32 MCW_CT_ROA_113 12211077 565919 7290370
33 MCW_CT_ROA_114 9122059 489242 5678494
34 MCW_CT_ROA_115 11702871 334002 7501717
35 MCW_CT_ROA_116 11303005 517995 6793739
36 MCW_CT_ROA_117 8839709 498148 5496942
37 MCW_CT_ROA_118 10368651 570077 5963755
38 MCW_CT_ROA_119 10678026 773463 5451719
39 MCW_CT_ROA_120 8814196 546081 5452486
40 MCW_CT_ROA_121 14847165 1629782 6092487
41 MCW_CT_ROA_167 10254437 534908 3892332
42 MCW_CT_ROA_169 12361299 382520 4997060
43 MCW_CT_ROA_171 15049027 361793 7562957
44 MCW_CT_ROA_172 11124138 325848 3611930
45 MCW_CT_ROA_173 12820367 319403 6889739
46 MCW_CT_ROA_174 5638348 131069 3468729
47 MCW_CT_ROA_175 734837 28434 371197
48 MCW_CT_ROA_177 10266750 391316 4669078
49 MCW_CT_ROA_178 10051149 469934 3751581
50 MCW_CT_ROA_180 14437895 365222 6234032
51 MCW_CT_ROA_181 12982038 310000 5865365
52 MCW_CT_ROA_183 11071957 326006 4366138
53 MCW_CT_ROA_184 10755984 277894 4608170
54 MCW_CT_ROA_185 10371500 329493 4739964
55 MCW_CT_ROA_186 10900502 240755 6123832
56 MCW_CT_ROA_189 9018945 281692 4431648
57 MCW_CT_ROA_190 10900566 385116 4348651
58 MCW_CT_ROA_192 9617315 299599 4183944
59 MCW_CT_ROA_193 7903839 177953 3799403
60 MCW_CT_ROA_194 13338827 346582 5058731
61 MCW_CT_ROA_195 14457362 533494 5876489
62 MCW_CT_ROA_196 13394379 616842 4576594
63 MCW_CT_ROA_198 12671191 571712 4766820
64 MCW_CT_ROA_199 11060140 262080 3187035
65 MCW_CT_ROA_201 9042663 267789 3320726
66 MCW_CT_ROA_202 7751943 236557 3106251
67 MCW_CT_ROA_204 17543011 1254631 7582032
68 MCW_CT_ROA_205 12747766 452055 6346330
69 MCW_CT_ROA_206 8455460 285711 4130128
70 MCW_CT_ROA_207 11006304 505492 5110713
71 MCW_CT_ROA_208 5856849 261315 2913687
72 MCW_CT_ROA_209 11297824 678861 4742105
73 MCW_CT_ROA_210 7400743 453340 3172790
74 MCW_CT_ROA_211 8938017 498894 3921761
75 MCW_CT_ROA_123 14153840 285442 10129373
76 MCW_CT_ROA_124 5862953 113298 4569976
77 MCW_CT_ROA_125 13609246 307506 10291964
78 MCW_CT_ROA_126 9817049 228387 7061107
79 MCW_CT_ROA_127 9069762 210990 7035945
80 MCW_CT_ROA_130 10036729 144062 7735497
81 MCW_CT_ROA_132 10953823 143726 8416880
82 MCW_CT_ROA_133 8741438 178478 6688236
83 MCW_CT_ROA_135 6948539 181264 5196535
84 MCW_CT_ROA_136 10720711 254421 7991394
85 MCW_CT_ROA_138 10446106 153712 7973512
86 MCW_CT_ROA_139 10762175 238762 8064720
87 MCW_CT_ROA_140 8720469 177782 6784325
88 MCW_CT_ROA_141 12193521 157429 9284343
89 MCW_CT_ROA_142 7383669 133449 5801304
90 MCW_CT_ROA_144 7436416 146205 5761043
91 MCW_CT_ROA_145 10381840 169224 7791123
92 MCW_CT_ROA_146 12450965 264074 9158338
93 MCW_CT_ROA_147 7627631 141979 5873262
94 MCW_CT_ROA_148 8242133 128801 6363806
95 MCW_CT_ROA_149 16699371 420261 12588934
96 MCW_CT_ROA_150 7813241 249723 5839994
97 MCW_CT_ROA_151 3381332 111822 2489439
98 MCW_CT_ROA_153 17281183 585576 12636036
99 MCW_CT_ROA_154 6988957 175769 5294346
100 MCW_CT_ROA_156 11950951 283389 8870887
101 MCW_CT_ROA_157 13986120 541326 10211147
102 MCW_CT_ROA_158 8616090 134093 6537637
103 MCW_CT_ROA_159 8134850 113982 6121793
104 MCW_CT_ROA_160 8326979 170965 6240943
105 MCW_CT_ROA_162 5424324 134268 4090158
106 MCW_CT_ROA_163 6954866 184684 5222610
107 MCW_CT_ROA_165 6796188 180535 5210420
108 MCW_CT_ROA_166 9900924 239075 7571148
109 MCW_CT_ROA_213 13499581 257693 7311755
110 MCW_CT_ROA_214 1569281 109424 688084
111 MCW_CT_ROA_215 12237851 278718 5844416
112 MCW_CT_ROA_217 13521248 334225 6700522
113 MCW_CT_ROA_218 15777772 345599 8119847
114 MCW_CT_ROA_219 15900601 313322 8501928
115 MCW_CT_ROA_220 15061110 230197 8078127
116 MCW_CT_ROA_221 11674777 334843 5875284
117 MCW_CT_ROA_222 17147873 390356 9336829
118 MCW_CT_ROA_223 12465025 317913 6133660
119 MCW_CT_ROA_224 7822917 196131 3787480
120 MCW_CT_ROA_225 8022702 184210 4517598
121 MCW_CT_ROA_226 11120375 348306 5648597
122 MCW_CT_ROA_227 11811856 259234 5996517
123 MCW_CT_ROA_228 5703944 109692 3137321
124 MCW_CT_ROA_229 10611034 244570 5424718
125 MCW_CT_ROA_230 8628302 163823 4562488
126 MCW_CT_ROA_231 7583015 512080 3953510
127 MCW_CT_ROA_232 8665986 227318 4543469
128 MCW_CT_ROA_233 11430178 234246 5907899
129 MCW_CT_ROA_234 8763664 178542 4688318
130 MCW_CT_ROA_235 12949588 202560 6967506
131 MCW_CT_ROA_236 9761129 170963 5457706
132 MCW_CT_ROA_237 13450470 220297 7381836
133 MCW_CT_ROA_238 10348384 337923 5139928
134 MCW_CT_ROA_239 11412974 275040 5757179
135 MCW_CT_ROA_240 13078783 360579 6821521
136 MCW_CT_ROA_241 16654461 410674 7543488
137 MCW_CT_ROA_243 10920806 277618 5557589
138 MCW_CT_ROA_244 8099611 293122 3505531
139 MCW_CT_ROA_245 10824038 357471 5000814
140 MCW_CT_ROA_246 12674448 307254 5903745
141 MCW_CT_ROA_247 11631878 281223 5176881
142 MCW_CT_ROA_249 9780958 294833 5647927
143 MCW_CT_ROA_250 14577232 447522 8040186
144 MCW_CT_ROA_252 11121551 227263 6209333
145 MCW_CT_ROA_253 12107678 374882 6464556
146 MCW_CT_ROA_254 7120797 355006 3843884
147 MCW_CT_ROA_255 7255699 557352 4441174
148 MCW_CT_ROA_256 9209722 205492 5168231
aligned_concordant_g1 MappedFragNum_hg38 percent_alignment
1 3665980 8120657 95.37%
2 5558254 16480089 95.65%
3 4464549 10559351 95.36%
4 3052077 9961556 96.68%
5 2442903 8441750 95.6%
6 3769451 13376763 96.42%
7 2858374 9139897 96.61%
8 4373607 17261558 96.59%
9 1866383 6864822 96.28%
10 3412380 10593268 96.37%
11 1538234 5905898 96.24%
12 2753144 10125329 96.64%
13 4553474 15970613 96.33%
14 2369715 9215500 96.29%
15 2575060 10214402 95.16%
16 1989539 7192303 97.21%
17 3248930 8711359 94.23%
18 2864677 7693600 93.85%
19 2338206 6447798 95.68%
20 2408977 7746207 95.66%
21 3413223 9159668 94.95%
22 1519083 5798467 97.11%
23 2701402 8597191 95.58%
24 1425026 4449612 95.41%
25 2782379 9766690 93.82%
26 5365911 15698813 95.49%
27 3878510 13548332 96.13%
28 5682459 17247957 94.46%
29 4206765 12995107 94.93%
30 3399332 11276854 94.68%
31 3157277 12218759 95.43%
32 4354788 11645158 95.37%
33 2954323 8632817 94.64%
34 3867152 11368869 97.15%
35 3991271 10785010 95.42%
36 2844619 8341561 94.36%
37 3834819 9798574 94.5%
38 4452844 9904563 92.76%
39 2815629 8268115 93.8%
40 7124896 13217383 89.02%
41 5827197 9719529 94.78%
42 6981719 11978779 96.91%
43 7124277 14687234 97.6%
44 7186360 10798290 97.07%
45 5611225 12500964 97.51%
46 2038550 5507279 97.68%
47 335206 706403 96.13%
48 5206356 9875434 96.19%
49 5829634 9581215 95.32%
50 7838641 14072673 97.47%
51 6806673 12672038 97.61%
52 6379813 10745951 97.06%
53 5869920 10478090 97.42%
54 5302043 10042007 96.82%
55 4535915 10659747 97.79%
56 4305605 8737253 96.88%
57 6166799 10515450 96.47%
58 5133772 9317716 96.88%
59 3926483 7725886 97.75%
60 7933514 12992245 97.4%
61 8047379 13923868 96.31%
62 8200943 12777537 95.39%
63 7332659 12099479 95.49%
64 7611025 10798060 97.63%
65 5454148 8774874 97.04%
66 4409135 7515386 96.95%
67 8706348 16288380 92.85%
68 5949381 12295711 96.45%
69 4039621 8169749 96.62%
70 5390099 10500812 95.41%
71 2681847 5595534 95.54%
72 5876858 10618963 93.99%
73 3774613 6947403 93.87%
74 4517362 8439123 94.42%
75 3739025 13868398 97.98%
76 1179679 5749655 98.07%
77 3009776 13301740 97.74%
78 2527555 9588662 97.67%
79 1822827 8858772 97.67%
80 2157170 9892667 98.56%
81 2393217 10810097 98.69%
82 1874724 8562960 97.96%
83 1570740 6767275 97.39%
84 2474896 10466290 97.63%
85 2318882 10292394 98.53%
86 2458693 10523413 97.78%
87 1758362 8542687 97.96%
88 2751749 12036092 98.71%
89 1448916 7250220 98.19%
90 1529168 7290211 98.03%
91 2421493 10212616 98.37%
92 3028553 12186891 97.88%
93 1612390 7485652 98.14%
94 1749526 8113332 98.44%
95 3690176 16279110 97.48%
96 1723524 7563518 96.8%
97 780071 3269510 96.69%
98 4059571 16695607 96.61%
99 1518842 6813188 97.49%
100 2796675 11667562 97.63%
101 3233647 13444794 96.13%
102 1944360 8481997 98.44%
103 1899075 8020868 98.6%
104 1915071 8156014 97.95%
105 1199898 5290056 97.52%
106 1547572 6770182 97.34%
107 1405233 6615653 97.34%
108 2090701 9661849 97.59%
109 5930133 13241888 98.09%
110 771773 1459857 93.03%
111 6114717 11959133 97.72%
112 6486501 13187023 97.53%
113 7312326 15432173 97.81%
114 7085351 15587279 98.03%
115 6752786 14830913 98.47%
116 5464650 11339934 97.13%
117 7420688 16757517 97.72%
118 6013452 12147112 97.45%
119 3839306 7626786 97.49%
120 3320894 7838492 97.7%
121 5123472 10772069 96.87%
122 5556105 11552622 97.81%
123 2456931 5594252 98.08%
124 4941746 10366464 97.7%
125 3901991 8464479 98.1%
126 3117425 7070935 93.25%
127 3895199 8438668 97.38%
128 5288033 11195932 97.95%
129 3896804 8585122 97.96%
130 5779522 12747028 98.44%
131 4132460 9590166 98.25%
132 5848337 13230173 98.36%
133 4870533 10010461 96.73%
134 5380755 11137934 97.59%
135 5896683 12718204 97.24%
136 8700299 16243787 97.53%
137 5085599 10643188 97.46%
138 4300958 7806489 96.38%
139 5465753 10466567 96.7%
140 6463449 12367194 97.58%
141 6173774 11350655 97.58%
142 3838198 9486125 96.99%
143 6089524 14129710 96.93%
144 4684955 10894288 97.96%
145 5268240 11732796 96.9%
146 2921907 6765791 95.01%
147 2257173 6698347 92.32%
148 3835999 9004230 97.77%for_plots <- alignResult %>%
left_join(.,sampleinfo, by=c("ID"="Library ID"))%>%
dplyr::select(ID:aligned_concordant_0,aligned_concordant_1, aligned_concordant_g1,percent_alignment, Histone_Mark, Individual, Treatment, Timepoint) %>%
distinct()
for_plots <- for_plots[(!for_plots$Treatment %in% "5FU"),]
# write_delim(for_plots,"data/alignment_summary.txt",delim= "\t")Read Visualization
for_plots %>%
group_by(Histone_Mark) %>%
ggplot(., aes(x=ID, y=Paired_Reads))+
geom_col(aes(fill = Histone_Mark)) +
theme_classic()+
ggtitle("Graph of total paired reads")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
for_plots %>%
group_by(Histone_Mark) %>%
ggplot(., aes(x=ID, y=percent_alignment))+
geom_col(aes(fill = Histone_Mark)) +
theme_classic()+
ggtitle("Percent aligned of paired reads") +
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous(expand = expansion(mult = c(0, .1)))+
coord_cartesian(ylim=c(80,100))
for_plots %>%
group_by(Histone_Mark) %>%
mutate(percent_aligned_unique = round(aligned_concordant_1 / Paired_Reads * 100, 2)) %>%
ggplot(., aes(x=ID, y=percent_aligned_unique))+
geom_col(aes(fill = Histone_Mark)) +
theme_classic()+
ggtitle("Percentage of paired reads aligning once")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
Read Analysis
Data Initialization
file_list_filter <- list.files(path="data/bam_no_multi",
pattern ="frag_len_count\\.txt$",full.names = TRUE)
read_and_label <- function(file) {
df <- read_delim(file, delim = "\t", col_names = c("Col1", "Col2")) # Adjust delimiter if needed
df <- df %>%
mutate(File = basename(file), # Add filename column
weight = Col2/sum(Col2))
return(df)
}
combined_df <- map_df(file_list_filter, read_and_label)
annotated_combo_df <- combined_df %>%
mutate(sample = gsub("_frag_len_count.txt","",File)) %>%
left_join(., sampleinfo, by = c("sample"="Library ID"))
annotated_combo_df <- annotated_combo_df[(!annotated_combo_df$Treatment %in% "5FU"),]Read Visualization
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K9me3") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K9me3",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K9me3") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K9me3")+
ylab("Fragment Length") +
xlab("")
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K27ac") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K27ac",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K27ac") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K27ac")+
ylab("Fragment Length") +
xlab("")
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K27me3") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K27me3",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K27me3") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K27me3")+
ylab("Fragment Length") +
xlab("")
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K36me3") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K36me3",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_combo_df %>%
dplyr::filter(Histone_Mark=="H3K36me3") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K36me3")+
ylab("Fragment Length") +
xlab("")
Tagging Questionable Libraries by Frag Len
peaks <- data.frame(sample = unique(annotated_combo_df$sample))
peaks[,"peakNum"] <- NA
for (s in peaks$sample) {
weights <- annotated_combo_df %>%
dplyr::filter(sample==s) %>%
dplyr::select(Col1, weight)
weights$smooth <- smooth_data(x = weights$Col1, y = weights$weight, sm_method = "moving-average", window_width_n = 15)
weights$peak <- find_peaks(weights$smooth, span = 31, global.threshold = 0.01)
peaks[peaks$sample == s,"peakNum"] <- sum(as.numeric(weights$peak[-1:-150]))
}
questionable_frag = peaks[(peaks$peakNum == 0),]
questionable_frag %>%
left_join(., sampleinfo, by =c("sample"="Library ID")) sample peakNum Histone_Mark Individual Treatment Timepoint
1 MCW_CT_ROA_126 0 H3K27me3 Ind1 DOX 24R
2 MCW_CT_ROA_151 0 H3K27me3 Ind4 VEH 24T
3 MCW_CT_ROA_169 0 H3K36me3 Ind1 VEH 24T
4 MCW_CT_ROA_172 0 H3K36me3 Ind1 VEH 24R
5 MCW_CT_ROA_175 0 H3K36me3 Ind1 VEH 144R
6 MCW_CT_ROA_178 0 H3K36me3 Ind2 VEH 24T
7 MCW_CT_ROA_181 0 H3K36me3 Ind2 VEH 24R
8 MCW_CT_ROA_183 0 H3K36me3 Ind2 DOX 144R
9 MCW_CT_ROA_190 0 H3K36me3 Ind3 VEH 24R
10 MCW_CT_ROA_195 0 H3K36me3 Ind4 DOX 24T
11 MCW_CT_ROA_196 0 H3K36me3 Ind4 VEH 24T
12 MCW_CT_ROA_198 0 H3K36me3 Ind4 DOX 24R
13 MCW_CT_ROA_201 0 H3K36me3 Ind4 DOX 144R
14 MCW_CT_ROA_202 0 H3K36me3 Ind4 VEH 144R
15 MCW_CT_ROA_211 0 H3K36me3 Ind5 VEH 144R
16 MCW_CT_ROA_213 0 H3K9me3 Ind1 DOX 24T
17 MCW_CT_ROA_214 0 H3K9me3 Ind1 VEH 24T
18 MCW_CT_ROA_217 0 H3K9me3 Ind1 VEH 24R
19 MCW_CT_ROA_222 0 H3K9me3 Ind2 DOX 24T
20 MCW_CT_ROA_223 0 H3K9me3 Ind2 VEH 24T
21 MCW_CT_ROA_226 0 H3K9me3 Ind2 VEH 24R
22 MCW_CT_ROA_238 0 H3K9me3 Ind3 VEH 144R
23 MCW_CT_ROA_241 0 H3K9me3 Ind4 VEH 24T
24 MCW_CT_ROA_244 0 H3K9me3 Ind4 VEH 24RRead Analysisafter Filtering
Data Initialization
file_list_noM <- list.files(path="data/bam_final",
pattern ="frag_len_noM\\.txt$",full.names = TRUE)
read_and_label <- function(file) {
df <- read_delim(file, delim = "\t", col_names = c("Col1", "Col2")) # Adjust delimiter if needed
df <- df %>%
mutate(File = basename(file), # Add filename column
weight = Col2/sum(Col2))
return(df)
}
combined_df <- map_df(file_list_noM, read_and_label)
annotated_noM_df <- combined_df %>%
mutate(sample = gsub("_frag_len_noM.txt","",File)) %>%
left_join(., sampleinfo, by = c("sample"="Library ID"))
annotated_noM_df <- annotated_noM_df[(!annotated_noM_df$Treatment %in% "5FU"),]Read Visualization
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K9me3") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K9me3",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K9me3") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K9me3")+
ylab("Fragment Length") +
xlab("")
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K27ac") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K27ac",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K27ac") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K27ac")+
ylab("Fragment Length") +
xlab("")
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K27me3") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K27me3",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K27me3") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K27me3")+
ylab("Fragment Length") +
xlab("")
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K36me3") %>%
ggplot(., aes(x=Col1, y=Col2, color = sample))+
geom_line(size=1)+
scale_x_continuous(breaks = seq(0, max(annotated_combo_df$Col1), by = 50))+
facet_wrap(~sample)+
labs(title = "Fragment length, H3K36me3",
x = "Fragment Length (bp)",
y = "Count",
color= "Sample")+
theme_minimal()
annotated_noM_df %>%
dplyr::filter(Histone_Mark=="H3K36me3") %>%
ggplot(., aes(x=sample, y=Col1, weight = weight,fill = Histone_Mark))+
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 90) +
ggtitle("Fragment lengths for H3K36me3")+
ylab("Fragment Length") +
xlab("")
Tagging Questionable Libraries by Frag Len
peaks <- data.frame(sample = unique(annotated_noM_df$sample))
peaks[,"peakNum"] <- NA
for (s in peaks$sample) {
weights <- annotated_combo_df %>%
dplyr::filter(sample==s) %>%
dplyr::select(Col1, weight)
weights$smooth <- smooth_data(x = weights$Col1, y = weights$weight, sm_method = "moving-average", window_width_n = 15)
weights$peak <- ggpmisc::find_peaks(weights$smooth, span = 31, global.threshold = 0.01)
peaks[peaks$sample == s,"peakNum"] <- sum(as.numeric(weights$peak[-1:-150]))
}
questionable_frag_filter = peaks[(peaks$peakNum == 0),]
questionable_frag_filter %>%
left_join(., sampleinfo, by =c("sample"="Library ID")) sample peakNum Histone_Mark Individual Treatment Timepoint
1 MCW_CT_ROA_126 0 H3K27me3 Ind1 DOX 24R
2 MCW_CT_ROA_151 0 H3K27me3 Ind4 VEH 24T
3 MCW_CT_ROA_169 0 H3K36me3 Ind1 VEH 24T
4 MCW_CT_ROA_172 0 H3K36me3 Ind1 VEH 24R
5 MCW_CT_ROA_175 0 H3K36me3 Ind1 VEH 144R
6 MCW_CT_ROA_178 0 H3K36me3 Ind2 VEH 24T
7 MCW_CT_ROA_181 0 H3K36me3 Ind2 VEH 24R
8 MCW_CT_ROA_183 0 H3K36me3 Ind2 DOX 144R
9 MCW_CT_ROA_190 0 H3K36me3 Ind3 VEH 24R
10 MCW_CT_ROA_195 0 H3K36me3 Ind4 DOX 24T
11 MCW_CT_ROA_196 0 H3K36me3 Ind4 VEH 24T
12 MCW_CT_ROA_198 0 H3K36me3 Ind4 DOX 24R
13 MCW_CT_ROA_201 0 H3K36me3 Ind4 DOX 144R
14 MCW_CT_ROA_202 0 H3K36me3 Ind4 VEH 144R
15 MCW_CT_ROA_211 0 H3K36me3 Ind5 VEH 144R
16 MCW_CT_ROA_213 0 H3K9me3 Ind1 DOX 24T
17 MCW_CT_ROA_214 0 H3K9me3 Ind1 VEH 24T
18 MCW_CT_ROA_217 0 H3K9me3 Ind1 VEH 24R
19 MCW_CT_ROA_222 0 H3K9me3 Ind2 DOX 24T
20 MCW_CT_ROA_223 0 H3K9me3 Ind2 VEH 24T
21 MCW_CT_ROA_226 0 H3K9me3 Ind2 VEH 24R
22 MCW_CT_ROA_238 0 H3K9me3 Ind3 VEH 144R
23 MCW_CT_ROA_241 0 H3K9me3 Ind4 VEH 24T
24 MCW_CT_ROA_244 0 H3K9me3 Ind4 VEH 24R# write_delim(peaks,"data/number_frag_peaks_summary.txt",delim="\t")
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
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] gcplyr_1.12.0 ggpmisc_0.6.2
[3] ggpp_0.5.9 corrplot_0.95
[5] ggpubr_0.6.1 DESeq2_1.46.0
[7] SummarizedExperiment_1.36.0 Biobase_2.66.0
[9] MatrixGenerics_1.18.1 matrixStats_1.5.0
[11] chromVAR_1.28.0 GenomicRanges_1.58.0
[13] GenomeInfoDb_1.42.3 IRanges_2.40.1
[15] S4Vectors_0.44.0 BiocGenerics_0.52.0
[17] genomation_1.38.0 kableExtra_1.4.0
[19] DT_0.33 viridis_0.6.5
[21] viridisLite_0.4.2 data.table_1.17.8
[23] ComplexHeatmap_2.22.0 edgeR_4.4.2
[25] limma_3.62.2 lubridate_1.9.4
[27] forcats_1.0.0 stringr_1.5.1
[29] dplyr_1.1.4 purrr_1.1.0
[31] readr_2.1.5 tidyr_1.3.1
[33] tibble_3.3.0 ggplot2_3.5.2
[35] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.4.2 later_1.4.2
[3] BiocIO_1.16.0 bitops_1.0-9
[5] R.oo_1.27.1 XML_3.99-0.18
[7] DirichletMultinomial_1.48.0 lifecycle_1.0.4
[9] rstatix_0.7.2 pwalign_1.2.0
[11] doParallel_1.0.17 rprojroot_2.1.0
[13] vroom_1.6.5 MASS_7.3-65
[15] processx_3.8.6 lattice_0.22-7
[17] crosstalk_1.2.1 backports_1.5.0
[19] magrittr_2.0.3 plotly_4.11.0
[21] sass_0.4.10 rmarkdown_2.29
[23] jquerylib_0.1.4 yaml_2.3.10
[25] plotrix_3.8-4 httpuv_1.6.16
[27] DBI_1.2.3 CNEr_1.42.0
[29] RColorBrewer_1.1-3 abind_1.4-8
[31] zlibbioc_1.52.0 R.utils_2.13.0
[33] RCurl_1.98-1.17 git2r_0.36.2
[35] circlize_0.4.16 GenomeInfoDbData_1.2.13
[37] seqLogo_1.72.0 MatrixModels_0.5-4
[39] annotate_1.84.0 svglite_2.2.1
[41] codetools_0.2-20 DelayedArray_0.32.0
[43] xml2_1.4.0 tidyselect_1.2.1
[45] shape_1.4.6.1 UCSC.utils_1.2.0
[47] farver_2.1.2 GenomicAlignments_1.42.0
[49] jsonlite_2.0.0 GetoptLong_1.0.5
[51] Formula_1.2-5 survival_3.8-3
[53] iterators_1.0.14 systemfonts_1.2.3
[55] foreach_1.5.2 splus2R_1.3-5
[57] tools_4.4.2 TFMPvalue_0.0.9
[59] Rcpp_1.1.0 glue_1.8.0
[61] gridExtra_2.3 SparseArray_1.6.2
[63] xfun_0.52 withr_3.0.2
[65] fastmap_1.2.0 SparseM_1.84-2
[67] callr_3.7.6 caTools_1.18.3
[69] digest_0.6.37 timechange_0.3.0
[71] R6_2.6.1 mime_0.13
[73] seqPattern_1.38.0 textshaping_1.0.1
[75] colorspace_2.1-1 GO.db_3.20.0
[77] gtools_3.9.5 poweRlaw_1.0.0
[79] dichromat_2.0-0.1 RSQLite_2.4.3
[81] R.methodsS3_1.8.2 utf8_1.2.6
[83] generics_0.1.4 rtracklayer_1.66.0
[85] httr_1.4.7 htmlwidgets_1.6.4
[87] S4Arrays_1.6.0 TFBSTools_1.44.0
[89] whisker_0.4.1 pkgconfig_2.0.3
[91] gtable_0.3.6 blob_1.2.4
[93] impute_1.80.0 XVector_0.46.0
[95] htmltools_0.5.8.1 carData_3.0-5
[97] clue_0.3-66 scales_1.4.0
[99] png_0.1-8 knitr_1.50
[101] rstudioapi_0.17.1 tzdb_0.5.0
[103] reshape2_1.4.4 rjson_0.2.23
[105] curl_7.0.0 cachem_1.1.0
[107] GlobalOptions_0.1.2 KernSmooth_2.23-26
[109] parallel_4.4.2 miniUI_0.1.2
[111] AnnotationDbi_1.68.0 restfulr_0.0.16
[113] pillar_1.11.0 vctrs_0.6.5
[115] promises_1.3.3 car_3.1-3
[117] xtable_1.8-4 cluster_2.1.8.1
[119] evaluate_1.0.4 cli_3.6.5
[121] locfit_1.5-9.12 compiler_4.4.2
[123] Rsamtools_2.22.0 rlang_1.1.6
[125] crayon_1.5.3 ggsignif_0.6.4
[127] labeling_0.4.3 ps_1.9.1
[129] getPass_0.2-4 plyr_1.8.9
[131] fs_1.6.6 stringi_1.8.7
[133] gridBase_0.4-7 BiocParallel_1.40.2
[135] Biostrings_2.74.1 lazyeval_0.2.2
[137] quantreg_6.1 Matrix_1.7-3
[139] BSgenome_1.74.0 hms_1.1.3
[141] bit64_4.6.0-1 KEGGREST_1.46.0
[143] statmod_1.5.0 shiny_1.11.1
[145] broom_1.0.9 memoise_2.0.1
[147] bslib_0.9.0 bit_4.6.0
[149] polynom_1.4-1