Last updated: 2019-04-26

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Knit directory: apaQTL/analysis/

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File Version Author Date Message
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 3.1.0       ✔ 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()

#Cq

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

Mapped v concentration

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

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 
-6858140 -1918197   151332  1688547  6691716 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  6525031.1  1219768.8   5.349 2.01e-06 ***
Conentration    4149.8      722.4   5.745 4.86e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2805000 on 52 degrees of freedom
Multiple R-squared:  0.3882,    Adjusted R-squared:  0.3765 
F-statistic:    33 on 1 and 52 DF,  p-value: 4.86e-07
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 
-4831820  -875807  -215509   558782  8499292 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   6972552     446484  15.617   <2e-16 ***
Conentration     1577       1389   1.136    0.261    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1895000 on 52 degrees of freedom
Multiple R-squared:  0.02422,   Adjusted R-squared:  0.005452 
F-statistic: 1.291 on 1 and 52 DF,  p-value: 0.2612

RNA quality

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


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_3.1.0  
[9] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.23.0     plyr_1.8.4       workflowr_1.3.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.3.1      cli_1.0.1        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.2.6        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   reshape2_1.4.3  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 labeling_0.3     stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4