Peak_Calling
ERM
2024-02-27
Last updated: 2024-02-27
Checks: 7 0
Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0a26679 | reneeisnowhere | 2024-02-27 | adding in the the reads data |
| html | 3d2aa7b | reneeisnowhere | 2024-02-27 | Build site. |
| Rmd | 1a8126f | reneeisnowhere | 2024-02-27 | adding the peak-calling files |
library(tidyverse)
# library(ggsignif)
# library(cowplot)
# library(ggpubr)
# library(scales)
# library(sjmisc)
library(kableExtra)
# library(broom)
# library(biomaRt)
library(RColorBrewer)
# library(gprofiler2)
# library(qvalue)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
loadFile_peakCall <- function(){
file <- choose.files()
file <- readPeakFile(file, header = FALSE)
return(file)
}
prepGRangeObj <- function(seek_object){
seek_object$Peaks = seek_object$V4
seek_object$level = seek_object$V5
seek_object$V4 = seek_object$V5 = NULL
return(seek_object)
}
TSS = getBioRegion(TxDb=txdb, upstream=3000, downstream=3000, by = "gene",
type = "start_site")
ind4_V24hpeaks <- readRDS("data/ind4_V24hpeaks.RDS")
ind1_DA24hpeaks <- readRDS("data/ind1_DA24hpeaks.RDS")
anno_ind4_V24h <- readRDS("data/anno_ind4_V24h.RDS")
anno_ind1_DA24h <- readRDS("data/anno_ind1_DA24h.RDS")This specific page so far contains the QC analysis after calling peaks using MACS2.
Primary scripts used for ATAC data preprocesing will be linked here in the future:
Currently the step were as follows: 1. Basic Fastqc followed by adapter trimming and Fastqc analysis on the leftover fragments. 2. Trimmed reads were aligned to the hg38 human genome. 3. Mitochondrial reads (chrM) were removed 4. samtools was used to removed non-paired, discordantly paired, and mulimapped reads from the .bam. 5. Markduplicates function from Picard was used to mark optical and PCR duplicates, with samtools used to remove these reads using the flag -F 1024. 6. MACS2 was used to call peaks, with QC of the peak files below.
Initial summary of reads after each step:
# library(readr)
# > Ind4_summary <- read_table("~/ATAC_downloads/Ind4/Ind4_summary.txt",
# + col_names = FALSE, col_types = cols(X3 = col_skip(),
# + X4 = col_skip(), X5 = col_skip(),
# + X6 = col_skip(), X7 = col_skip(),
# + X8 = col_skip(), X9 = col_skip(),
# + X10 = col_skip(), X11 = col_skip(),
# + X13 = col_skip(), X14 = col_skip(),
# + X17 = col_skip(), X18 = col_skip()))
#
Ind4_summary <- read.csv("data/Ind4_summary.txt", row.names = 1)
Ind4_reads_summary <- Ind4_summary %>%
separate(counts,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
separate(name, into=c("one","two",NA, "4","sample",NA,"seven","8")) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
mutate(type = if_else(two=="first", "total",
if_else(two=="noM","nuclear",
if_else((seven == "fin"& two=="files"), "unique", "dedup"))))%>%
dplyr::select(sample,type, reads, mapped_reads)%>%
pivot_longer(cols=reads:mapped_reads, names_to = "read_info", values_to = "reads") %>%
unite("type",type:read_info, sep="_") %>%
distinct() %>%
pivot_wider(id_cols = sample, names_from = "type", values_from = "reads") %>%
mutate(per_nuclear_mapped = nuclear_mapped_reads/total_mapped_reads*100) %>%
dplyr::select(sample,total_reads:nuclear_mapped_reads,per_nuclear_mapped,unique_mapped_reads)
# dedup_mapped_reads)
Ind4_reads_summary %>%
kable(., caption= "Reads summary from Ind_4") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(full_width = FALSE,font_size = 18) %>%
scroll_box(width = "100%", height = "400px")| sample | total_reads | total_mapped_reads | nuclear_reads | nuclear_mapped_reads | per_nuclear_mapped | unique_mapped_reads |
|---|---|---|---|---|---|---|
| 79V24h | 77013488 | 75962835 | 27439164 | 26415467 | 34.77420 | 18870010 |
| 79DA24h | 83930268 | 83140985 | 39561574 | 38811928 | 46.68206 | 29459906 |
| 79DA3h | 114610072 | 112679708 | 74283554 | 72386250 | 64.24071 | 55956416 |
| 79DX24h | 78395382 | 77692597 | 25391928 | 24733417 | 31.83497 | 17146642 |
| 79DX3h | 77601292 | 76058132 | 47116936 | 45593581 | 59.94570 | 35460994 |
| 79E24h | 86039200 | 85324746 | 29965542 | 29300484 | 34.33996 | 21004354 |
| 79E3h | 86993222 | 85671962 | 46372712 | 45084280 | 52.62431 | 34578548 |
| 79M24h | 82061002 | 81424543 | 27148372 | 26562054 | 32.62168 | 19085838 |
| 79M3h | 83929214 | 82368935 | 48985626 | 47454435 | 57.61205 | 36937534 |
| 79T24h | 90875858 | 89680567 | 31347532 | 30204755 | 33.68038 | 21789834 |
| 79T3h | 106856444 | 105027081 | 65690664 | 63898507 | 60.84003 | 49669024 |
| 79V3h | 74863328 | 73508067 | 52535548 | 51196256 | 69.64713 | 40497298 |
**note deduped mapped reads were taken out of this list due to problem with script returning invalid numbers. The read-numbers for deduplicated read pairs will be added here after reprocessing the files
# plotAvgProf(anno_ind4_V24h,xlim=c(-3000,3000))
plotAnnoBar(anno_ind4_V24h, main = "Genomic Feature Distribution")+ ggtitle("Ind4 VEH 24 hour")
plotAnnoBar(anno_ind1_DA24h, main = "Genomic Feature Distribution")+ ggtitle("Ind1 DNR 24 hour")
ind4_V24hpeaks_gr <- prepGRangeObj(ind4_V24hpeaks)
ind1_DA24hpeaks_gr <- prepGRangeObj((ind1_DA24hpeaks))
Epi_list <- GRangesList(ind1_DA24hpeaks_gr, ind4_V24hpeaks_gr)
##plotting the TSS average window (making an overlap of each using Epi_list as list holder)
Epi_list_tagMatrix = lapply(Epi_list, getTagMatrix, windows = TSS)>> preparing start_site regions by gene... 2024-02-27 4:19:10 PM
>> preparing tag matrix... 2024-02-27 4:19:10 PM
>> preparing start_site regions by gene... 2024-02-27 4:19:24 PM
>> preparing tag matrix... 2024-02-27 4:19:24 PM plotAvgProf(Epi_list_tagMatrix, xlim=c(-3000, 3000), ylab = "Count Frequency")>> plotting figure... 2024-02-27 4:19:33 PM 
#plotPeakProf(Epi_list_tagMatrix, facet = "none", conf = 0.95)Ind4 3 hour and 24 hour fragment sizes
Ind4_frag_files <- read.csv("data/Ind4_fragment_files.txt", row.names = 1)
Ind4_frag_files %>%
dplyr::filter(time =="3h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
ggtitle("Individual 4\n3 hour fragment sizes")+
theme_classic()+
scale_color_manual(values=drug_pal_fact)
| Version | Author | Date |
|---|---|---|
| 3d2aa7b | reneeisnowhere | 2024-02-27 |
Ind4_frag_files %>%
dplyr::filter(time =="24h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
ggtitle("Individual 4\n24 hour fragment sizes")+
theme_classic()+
scale_color_manual(values=drug_pal_fact)
| Version | Author | Date |
|---|---|---|
| 3d2aa7b | reneeisnowhere | 2024-02-27 |
So, fragment lengths are not so great. Lots of noise.
sessionInfo()R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
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 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] org.Hs.eg.db_3.17.0
[2] TxDb.Hsapiens.UCSC.hg38.knownGene_3.17.0
[3] GenomicFeatures_1.52.2
[4] AnnotationDbi_1.62.2
[5] Biobase_2.60.0
[6] GenomicRanges_1.52.1
[7] GenomeInfoDb_1.36.4
[8] IRanges_2.34.1
[9] S4Vectors_0.38.2
[10] BiocGenerics_0.46.0
[11] ChIPseeker_1.36.0
[12] RColorBrewer_1.1-3
[13] kableExtra_1.4.0
[14] lubridate_1.9.3
[15] forcats_1.0.0
[16] stringr_1.5.1
[17] dplyr_1.1.4
[18] purrr_1.0.2
[19] readr_2.1.5
[20] tidyr_1.3.1
[21] tibble_3.2.1
[22] ggplot2_3.4.4
[23] tidyverse_2.0.0
[24] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.3.1
[2] later_1.3.2
[3] BiocIO_1.10.0
[4] bitops_1.0-7
[5] ggplotify_0.1.2
[6] filelock_1.0.3
[7] polyclip_1.10-6
[8] XML_3.99-0.16.1
[9] lifecycle_1.0.4
[10] rprojroot_2.0.4
[11] processx_3.8.3
[12] lattice_0.22-5
[13] MASS_7.3-60.0.1
[14] magrittr_2.0.3
[15] sass_0.4.8
[16] rmarkdown_2.25
[17] plotrix_3.8-4
[18] jquerylib_0.1.4
[19] yaml_2.3.8
[20] httpuv_1.6.14
[21] cowplot_1.1.3
[22] DBI_1.2.2
[23] abind_1.4-5
[24] zlibbioc_1.46.0
[25] ggraph_2.1.0
[26] RCurl_1.98-1.14
[27] yulab.utils_0.1.4
[28] tweenr_2.0.2
[29] rappdirs_0.3.3
[30] git2r_0.33.0
[31] GenomeInfoDbData_1.2.10
[32] enrichplot_1.20.3
[33] ggrepel_0.9.5
[34] tidytree_0.4.6
[35] svglite_2.1.3
[36] codetools_0.2-19
[37] DelayedArray_0.26.7
[38] DOSE_3.26.2
[39] xml2_1.3.6
[40] ggforce_0.4.2
[41] tidyselect_1.2.0
[42] aplot_0.2.2
[43] farver_2.1.1
[44] viridis_0.6.5
[45] matrixStats_1.2.0
[46] BiocFileCache_2.8.0
[47] GenomicAlignments_1.36.0
[48] jsonlite_1.8.8
[49] tidygraph_1.3.1
[50] systemfonts_1.0.5
[51] tools_4.3.1
[52] progress_1.2.3
[53] treeio_1.24.3
[54] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[55] Rcpp_1.0.12
[56] glue_1.7.0
[57] gridExtra_2.3
[58] xfun_0.42
[59] qvalue_2.32.0
[60] MatrixGenerics_1.12.3
[61] withr_3.0.0
[62] fastmap_1.1.1
[63] boot_1.3-29
[64] fansi_1.0.6
[65] caTools_1.18.2
[66] callr_3.7.5
[67] digest_0.6.34
[68] timechange_0.3.0
[69] R6_2.5.1
[70] gridGraphics_0.5-1
[71] colorspace_2.1-0
[72] GO.db_3.17.0
[73] gtools_3.9.5
[74] biomaRt_2.56.1
[75] RSQLite_2.3.5
[76] utf8_1.2.4
[77] generics_0.1.3
[78] data.table_1.15.0
[79] rtracklayer_1.60.1
[80] prettyunits_1.2.0
[81] graphlayouts_1.1.0
[82] httr_1.4.7
[83] S4Arrays_1.0.6
[84] scatterpie_0.2.1
[85] whisker_0.4.1
[86] pkgconfig_2.0.3
[87] gtable_0.3.4
[88] blob_1.2.4
[89] XVector_0.40.0
[90] shadowtext_0.1.3
[91] htmltools_0.5.7
[92] fgsea_1.26.0
[93] scales_1.3.0
[94] png_0.1-8
[95] ggfun_0.1.4
[96] knitr_1.45
[97] rstudioapi_0.15.0
[98] tzdb_0.4.0
[99] reshape2_1.4.4
[100] rjson_0.2.21
[101] nlme_3.1-164
[102] curl_5.2.0
[103] cachem_1.0.8
[104] KernSmooth_2.23-22
[105] parallel_4.3.1
[106] HDO.db_0.99.1
[107] restfulr_0.0.15
[108] pillar_1.9.0
[109] grid_4.3.1
[110] vctrs_0.6.5
[111] gplots_3.1.3.1
[112] promises_1.2.1
[113] dbplyr_2.4.0
[114] evaluate_0.23
[115] cli_3.6.2
[116] compiler_4.3.1
[117] Rsamtools_2.16.0
[118] rlang_1.1.3
[119] crayon_1.5.2
[120] labeling_0.4.3
[121] ps_1.7.6
[122] getPass_0.2-4
[123] plyr_1.8.9
[124] fs_1.6.3
[125] stringi_1.8.3
[126] viridisLite_0.4.2
[127] BiocParallel_1.34.2
[128] munsell_0.5.0
[129] Biostrings_2.68.1
[130] lazyeval_0.2.2
[131] GOSemSim_2.26.1
[132] Matrix_1.6-5
[133] hms_1.1.3
[134] patchwork_1.2.0
[135] bit64_4.0.5
[136] KEGGREST_1.40.1
[137] highr_0.10
[138] SummarizedExperiment_1.30.2
[139] igraph_2.0.2
[140] memoise_2.0.1
[141] bslib_0.6.1
[142] ggtree_3.8.2
[143] fastmatch_1.1-4
[144] bit_4.0.5
[145] ape_5.7-1