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=""))
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 |
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")
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 |
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 |
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 |
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()
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
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):
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[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
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[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
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