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Rmd ccb3f28 reneeisnowhere 2024-01-30 first updates 
library(tidyverse)
# library(ggsignif)
# library(cowplot)
# library(ggpubr)
# library(scales)
# library(sjmisc)
library(kableExtra)
# library(broom)
# library(biomaRt)
library(RColorBrewer)
# library(gprofiler2)
# library(qvalue)

This code takes the multiqc fastqc output file and: splits by rows to trimmed and non trimmed, then separates the trimmed file names into catagories I want, then adds back in the non trimmed data rows (while also splitting file name like the trimmed file name). after rbind, I split treatmenttime by position, fix the names of the time column, remove numbers from trt column, add a new column called “trimmed” where I add in a vector that lets me group by trimmed file verses non trimmed file, the select only those columns containing the columns I want to keep.

multiqc_fastqc2 <- read_csv("data/multiqc_fastqc_run2.txt")
multiqc_general_stats2 <- read_csv("data/multiqc_genestat_run2.txt")


fastqc_full <- multiqc_fastqc2 %>% 
  slice_tail(n=144) %>% 
  separate(Filename, into = c(NA,"ind","treatmenttime",NA,"read")) %>% 
  rbind(., (multiqc_fastqc2 %>% slice_head(n=144) %>% separate(Filename, into = c("ind","treatmenttime",NA,"read")))) %>% 
  separate_wider_position(., col =treatmenttime,c(2,trt=2,time=3),too_few = "align_start") %>% 
  mutate(time=case_match(trt,"E2"~"24h","E3"~"3h","M2"~"24h", "M3"~"3h","T2"~"24h","T3"~"3h","V2"~"24h","V3"~"3h",.default = time)) %>% 
  mutate(trt=gsub("[[:digit:]]", "", trt) ) %>% 
  mutate(trimmed = if_else(grepl(pattern ="^trim", x = Sample)==TRUE, "yes","no")) %>% 
  select(Sample:read, trimmed,`Total Sequences`:avg_sequence_length) %>% 
  full_join(., multiqc_general_stats2, join_by(Sample)) %>% 
   rename("percent_gc"="FastQC_mqc-generalstats-fastqc-percent_gc",
         "avg_seq_len"= "FastQC_mqc-generalstats-fastqc-avg_sequence_length",
         "percent_dup"= "FastQC_mqc-generalstats-fastqc-percent_duplicates",
         "percent_fails"= "FastQC_mqc-generalstats-fastqc-percent_fails",
         "total_sequences"= "FastQC_mqc-generalstats-fastqc-total_sequences") %>% 
  mutate(ind = factor(ind, levels = c("Ind1", "Ind2", "Ind3", "Ind4", "Ind5", "Ind6"))) %>%
  mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>% 
  mutate(trt = factor(trt, levels = c("DX","E", "DA","M", "T", "V"), labels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")))

(in this case, Sample, ind, trt, time read, trimmed, Total sequences, Flagged poor quality, sequence length, %GC,total deduplicated %,and avg sequence length) I also then addin the gen_stats file and rename the columns to normal things.

fastqc_full
# A tibble: 288 × 17
   Sample                     ind   trt   time   read  trimmed `Total Sequences`
   <chr>                      <fct> <fct> <fct>  <chr> <chr>               <dbl>
 1 trimmed_Ind1_75DA24h_S7_R1 Ind1  DNR   24 ho… R1    yes              42551223
 2 trimmed_Ind1_75DA24h_S7_R2 Ind1  DNR   24 ho… R2    yes              42551223
 3 trimmed_Ind1_75DA3h_S1_R1  Ind1  DNR   3 hou… R1    yes              48230311
 4 trimmed_Ind1_75DA3h_S1_R2  Ind1  DNR   3 hou… R2    yes              48230311
 5 trimmed_Ind1_75DX24h_S8_R1 Ind1  DOX   24 ho… R1    yes              43284466
 6 trimmed_Ind1_75DX24h_S8_R2 Ind1  DOX   24 ho… R2    yes              43284466
 7 trimmed_Ind1_75DX3h_S2_R1  Ind1  DOX   3 hou… R1    yes              44480840
 8 trimmed_Ind1_75DX3h_S2_R2  Ind1  DOX   3 hou… R2    yes              44480840
 9 trimmed_Ind1_75E24h_S9_R1  Ind1  EPI   24 ho… R1    yes              42757767
10 trimmed_Ind1_75E24h_S9_R2  Ind1  EPI   24 ho… R2    yes              42757767
# ℹ 278 more rows
# ℹ 10 more variables: `Sequences flagged as poor quality` <dbl>,
#   `Sequence length` <chr>, `%GC` <dbl>, total_deduplicated_percentage <dbl>,
#   avg_sequence_length <dbl>, percent_dup <dbl>, percent_gc <dbl>,
#   avg_seq_len <dbl>, percent_fails <dbl>, total_sequences <dbl>
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
fastqc_full %>% 
  filter(trimmed=="no") %>%
  ggplot(., aes(x=trt, y= `Total Sequences`))+
  geom_col(aes(fill= trt))+
  facet_wrap(ind~time)+
  scale_fill_manual(values=drug_pal)+
  theme_bw()+
  ggtitle("Total Sequences, untrimmed")+
      # ylab(ylab)+
      xlab("")+
      theme(strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
          plot.title = element_text(size=14,hjust = 0.5,face="bold"),
          axis.title = element_text(size = 10, color = "black"),
          axis.ticks = element_line(linewidth = 0.5),
          axis.line = element_line(linewidth = 0.5),
          axis.text.x = element_blank(),
          strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))

fastqc_full %>% 
  filter(trimmed=="yes") %>% 
  ggplot(., aes(x=trt, y= `Total Sequences`))+
  geom_col(aes(fill= trt))+
  facet_wrap(ind~time)+
  scale_fill_manual(values=drug_pal)+
  theme_bw()+
  ggtitle("Total Sequences, trimmed")+
      # ylab(ylab)+
      xlab("")+
      theme(strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
          plot.title = element_text(size=14,hjust = 0.5,face="bold"),
          axis.title = element_text(size = 10, color = "black"),
          axis.ticks = element_line(linewidth = 0.5),
          axis.line = element_line(linewidth = 0.5),
          axis.text.x = element_blank(),
          strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))

totseq <- fastqc_full %>% 
  dplyr::filter(read =='R1') %>% 
  # group_by(ind,trt,time) %>% 
 select(Sample, ind, trt, time, trimmed, `Total Sequences`) %>% 
  pivot_wider(id_cols = c(ind,trt,time), names_from = trimmed, values_from = `Total Sequences`) %>% 
  mutate(perc_removed=(no-yes)/no*100) #%>% 
 kable(list(totseq[1:36,], totseq[37:72,]),caption= "Summary of Total sequences before and after trimming, with percentage of removed sequences") %>%
  kable_paper("striped", full_width = FALSE) %>%
  kable_styling(full_width = FALSE,font_size = 18) #%>%
Summary of Total sequences before and after trimming, with percentage of removed sequences
ind trt time yes no perc_removed
Ind1 DNR 24 hours 42551223 42552417 0.0028060
Ind1 DNR 3 hours 48230311 48236407 0.0126378
Ind1 DOX 24 hours 43284466 43287755 0.0075980
Ind1 DOX 3 hours 44480840 44487109 0.0140917
Ind1 EPI 24 hours 42757767 42760898 0.0073221
Ind1 EPI 3 hours 43425217 43436091 0.0250345
Ind1 MTX 24 hours 42126655 42128326 0.0039665
Ind1 MTX 3 hours 34347823 34353188 0.0156172
Ind1 TRZ 24 hours 39592932 39618391 0.0642606
Ind1 TRZ 3 hours 32503524 32515737 0.0375603
Ind1 VEH 24 hours 43814361 43825945 0.0264318
Ind1 VEH 3 hours 33684259 33702857 0.0551823
Ind2 DNR 24 hours 45642245 45642976 0.0016016
Ind2 DNR 3 hours 49765031 49766630 0.0032130
Ind2 DOX 24 hours 47963129 47964028 0.0018743
Ind2 DOX 3 hours 43218795 43219481 0.0015872
Ind2 EPI 24 hours 37876743 37877549 0.0021279
Ind2 EPI 3 hours 46509493 46509868 0.0008063
Ind2 MTX 24 hours 47565953 47566199 0.0005172
Ind2 MTX 3 hours 46547872 46548086 0.0004597
Ind2 TRZ 24 hours 42224852 42225791 0.0022238
Ind2 TRZ 3 hours 38631467 38632152 0.0017731
Ind2 VEH 24 hours 38128833 38135521 0.0175375
Ind2 VEH 3 hours 39053023 39053395 0.0009525
Ind3 DNR 24 hours 37681759 37682029 0.0007165
Ind3 DNR 3 hours 71147689 71149112 0.0020000
Ind3 DOX 24 hours 39048735 39049130 0.0010115
Ind3 DOX 3 hours 43756089 43756875 0.0017963
Ind3 EPI 24 hours 41488932 41489927 0.0023982
Ind3 EPI 3 hours 50138075 50139561 0.0029637
Ind3 MTX 24 hours 36498742 36498973 0.0006329
Ind3 MTX 3 hours 49431453 49431897 0.0008982
Ind3 TRZ 24 hours 40927652 40928915 0.0030858
Ind3 TRZ 3 hours 38780291 38781957 0.0042958
Ind3 VEH 24 hours 31324889 31327676 0.0088963
Ind3 VEH 3 hours 44853273 44855052 0.0039661
ind trt time yes no perc_removed
Ind4 DNR 24 hours 41965134 41969945 0.0114630
Ind4 DNR 3 hours 57305036 57308058 0.0052733
Ind4 DOX 24 hours 39197691 39206235 0.0217925
Ind4 DOX 3 hours 38800646 38806358 0.0147192
Ind4 EPI 24 hours 43019600 43031730 0.0281885
Ind4 EPI 3 hours 43496611 43500262 0.0083931
Ind4 MTX 24 hours 41030501 41031137 0.0015500
Ind4 MTX 3 hours 41964607 41971195 0.0156965
Ind4 TRZ 24 hours 45437929 45444700 0.0148994
Ind4 TRZ 3 hours 53428222 53433439 0.0097635
Ind4 VEH 24 hours 38506744 38516586 0.0255526
Ind4 VEH 3 hours 37431664 37434854 0.0085215
Ind5 DNR 24 hours 46899322 46905257 0.0126532
Ind5 DNR 3 hours 49843467 49867445 0.0480835
Ind5 DOX 24 hours 36866406 36868936 0.0068621
Ind5 DOX 3 hours 50184488 50205715 0.0422800
Ind5 EPI 24 hours 40050758 40060480 0.0242683
Ind5 EPI 3 hours 45761710 45805846 0.0963545
Ind5 MTX 24 hours 47153857 47154510 0.0013848
Ind5 MTX 3 hours 35648456 35648586 0.0003647
Ind5 TRZ 24 hours 44126954 44145364 0.0417031
Ind5 TRZ 3 hours 38314546 38349375 0.0908203
Ind5 VEH 24 hours 49541600 49581967 0.0814147
Ind5 VEH 3 hours 41343052 41487846 0.3490034
Ind6 DNR 24 hours 41059389 41059920 0.0012932
Ind6 DNR 3 hours 44095342 44095825 0.0010953
Ind6 DOX 24 hours 37577690 37578841 0.0030629
Ind6 DOX 3 hours 45117924 45118756 0.0018440
Ind6 EPI 24 hours 43365041 43366727 0.0038878
Ind6 EPI 3 hours 43646213 43647011 0.0018283
Ind6 MTX 24 hours 41005624 41006798 0.0028629
Ind6 MTX 3 hours 45458055 45458459 0.0008887
Ind6 TRZ 24 hours 35700533 35701361 0.0023192
Ind6 TRZ 3 hours 35875024 35875416 0.0010927
Ind6 VEH 24 hours 39816658 39820062 0.0085485
Ind6 VEH 3 hours 42608440 42608980 0.0012673 
   # scroll_box(width = "100%", height = "400px")
  
  totseq %>% 
    ggplot(.,aes(x=trt,y=perc_removed) )+
    geom_col(aes(fill= trt))+
  facet_wrap(ind~time)+
  scale_fill_manual(values=drug_pal)+
  theme_bw()+
  ggtitle("Total Sequences, percent removed")+
      # ylab(ylab)+
      xlab("")+
      theme(strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
          plot.title = element_text(size=14,hjust = 0.5,face="bold"),
          axis.title = element_text(size = 10, color = "black"),
          axis.ticks = element_line(linewidth = 0.5),
          axis.line = element_line(linewidth = 0.5),
          axis.text.x = element_blank(),
          strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))

Percenttrimmed


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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RColorBrewer_1.1-3 kableExtra_1.3.4   lubridate_1.9.3    forcats_1.0.0     
 [5] stringr_1.5.0      dplyr_1.1.3        purrr_1.0.2        readr_2.1.4       
 [9] tidyr_1.3.0        tibble_3.2.1       ggplot2_3.4.4      tidyverse_2.0.0   
[13] workflowr_1.7.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.4      xfun_0.41         bslib_0.5.1       processx_3.8.2   
 [5] callr_3.7.3       tzdb_0.4.0        vctrs_0.6.4       tools_4.3.1      
 [9] ps_1.7.5          generics_0.1.3    parallel_4.3.1    fansi_1.0.5      
[13] highr_0.10        pkgconfig_2.0.3   webshot_0.5.5     lifecycle_1.0.4  
[17] farver_2.1.1      compiler_4.3.1    git2r_0.32.0      munsell_0.5.0    
[21] getPass_0.2-2     httpuv_1.6.12     htmltools_0.5.7   sass_0.4.7       
[25] yaml_2.3.7        crayon_1.5.2      later_1.3.1       pillar_1.9.0     
[29] jquerylib_0.1.4   whisker_0.4.1     cachem_1.0.8      tidyselect_1.2.0 
[33] rvest_1.0.3       digest_0.6.33     stringi_1.7.12    labeling_0.4.3   
[37] rprojroot_2.0.4   fastmap_1.1.1     grid_4.3.1        colorspace_2.1-0 
[41] cli_3.6.1         magrittr_2.0.3    utf8_1.2.4        withr_2.5.2      
[45] scales_1.2.1      promises_1.2.1    bit64_4.0.5       timechange_0.2.0 
[49] rmarkdown_2.25    httr_1.4.7        bit_4.0.5         hms_1.1.3        
[53] evaluate_0.23     knitr_1.45        viridisLite_0.4.2 rlang_1.1.2      
[57] Rcpp_1.0.11       glue_1.6.2        xml2_1.3.5        svglite_2.1.2    
[61] rstudioapi_0.15.0 vroom_1.6.4       jsonlite_1.8.7    R6_2.5.1         
[65] systemfonts_1.0.5 fs_1.6.3