Last updated: 2019-09-04

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Modified:   docs/figure/signalsiteanalysis.Rmd/figure1bMain-1.pdf
    Deleted:    reads_graphs.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
html	1b7353b	brimittleman	2019-04-26	Build site.
Rmd	111979f	brimittleman	2019-04-26	add seq correlations
html	3557545	brimittleman	2019-04-25	Build site.
html	0f7ad72	brimittleman	2019-04-25	Build site.
Rmd	48b2ec1	brimittleman	2019-04-25	add map befroe mp filter
html	be227e7	brimittleman	2019-04-25	Build site.
Rmd	6cb0a99	brimittleman	2019-04-25	add seq meta pltos

In this analysis I want to compare the sequencing depth between batches.

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Load metadata:

metadata=read.table("../data/MetaDataSequencing.txt",header = T)
meta_T=metadata %>% filter(grepl("T", Sample_ID)) %>% mutate(samp=paste("X", Sample_ID, sep=""))
meta_N=metadata %>% filter(grepl("N", Sample_ID)) %>%  mutate(samp=paste("X", Sample_ID, sep=""))

Read count

metadata$batch=as.factor(metadata$batch)
ggplot(metadata, aes(x=batch, group=batch, y=reads, fill=batch)) + geom_boxplot() + geom_jitter() + facet_grid(~fraction) + labs(title="Read count by batch")

Version	Author	Date
be227e7	brimittleman	2019-04-25

Mapped reads

ggplot(metadata, aes(x=batch, group=batch, y=Mapped_noMP, fill=batch)) + geom_boxplot() + geom_jitter() + facet_grid(~fraction) + labs(title="Mapped reads by batch")

Version	Author	Date
3557545	brimittleman	2019-04-25
be227e7	brimittleman	2019-04-25

Map prop

ggplot(metadata, aes(x=batch, group=batch, y=prop_MappedwithoutMP, fill=batch)) + geom_boxplot() + geom_jitter() + facet_grid(~fraction) + labs(title="Proportion Mapped reads by batch")

Version	Author	Date
be227e7	brimittleman	2019-04-25

Library concentration

ggplot(metadata, aes(x=batch, group=batch, y=library_conc, fill=batch)) + geom_boxplot() + geom_jitter() + facet_grid(~fraction) + labs(title="Library concentrations by batch")

Version	Author	Date
be227e7	brimittleman	2019-04-25

before mp

ggplot(metadata, aes(x=batch, group=batch, y=mapped, fill=batch)) + geom_boxplot() + geom_jitter() + facet_grid(~fraction) + labs(title="Mapped reads before MP filter by batch")

Version	Author	Date
0f7ad72	brimittleman	2019-04-25

alive perc

ggplot(metadata, aes(y=Mapped_noMP, col=batch, x=alive_avg)) + geom_point()

Version	Author	Date
1b7353b	brimittleman	2019-04-26

Cq

ggplot(metadata, aes(y=Mapped_noMP, col=batch, x=library_conc)) + geom_point()

Version	Author	Date
1b7353b	brimittleman	2019-04-26

Mapped v concentration

ggplot(metadata, aes(y=Mapped_noMP, col=batch, x=Conentration)) + geom_point() + facet_grid(~fraction)

Version	Author	Date
1b7353b	brimittleman	2019-04-26

metadata_T=metadata %>% filter(fraction=="total")
summary(lm(data=metadata_T, Mapped_noMP ~ Conentration))


Call:
lm(formula = Mapped_noMP ~ Conentration, data = metadata_T)

Residuals:
     Min       1Q   Median       3Q      Max 
-6812387 -1945324   123268  1699261  6642846 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  6695774.3  1258810.3   5.319 2.45e-06 ***
Conentration    4050.9      755.6   5.362 2.11e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2832000 on 50 degrees of freedom
Multiple R-squared:  0.365, Adjusted R-squared:  0.3523 
F-statistic: 28.75 on 1 and 50 DF,  p-value: 2.109e-06

metadata_N=metadata %>% filter(fraction=="nuclear")
summary(lm(data=metadata_N, Mapped_noMP~ Conentration))


Call:
lm(formula = Mapped_noMP ~ Conentration, data = metadata_N)

Residuals:
     Min       1Q   Median       3Q      Max 
-4721643  -782295  -170617   625555  8609300 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   6872496     418511  16.421   <2e-16 ***
Conentration     1532       1289   1.188     0.24    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1752000 on 50 degrees of freedom
Multiple R-squared:  0.02746,   Adjusted R-squared:  0.008012 
F-statistic: 1.412 on 1 and 50 DF,  p-value: 0.2404

RNA quality

ggplot(metadata, aes(y=Mapped_noMP, col=batch, x=ratio260_280)) + geom_point() + facet_grid(~fraction)

Version	Author	Date
1b7353b	brimittleman	2019-04-26

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
[5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
[9] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        workflowr_1.4.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] reshape2_1.4.3   modelr_0.1.2     magrittr_1.5     whisker_0.3-2   
[41] backports_1.1.2  scales_1.0.0     htmltools_0.3.6  rvest_0.3.2     
[45] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Sequencing meta data

Briana Mittleman

4/25/2019