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In [1]:



    


source("http://bioconductor.org/biocLite.R")
biocLite("rhdf5")



    


















    






Bioconductor version 3.3 (BiocInstaller 1.22.3), ?biocLite for help
BioC_mirror: https://bioconductor.org
Using Bioconductor 3.3 (BiocInstaller 1.22.3), R 3.3.1 (2016-06-21).
Installing package(s) ‘rhdf5’
Old packages: 'BiocParallel', 'curl', 'DBI', 'DESeq2', 'knitr', 'limma', 'R6',
  'S4Vectors', 'stringr'

















In [2]:



    


devtools::install_github("pachterlab/sleuth")



    


















    






Downloading GitHub repo pachterlab/sleuth@master
from URL https://api.github.com/repos/pachterlab/sleuth/zipball/master
Installing sleuth
Installing data.table
Installing chron
'/usr/lib/R/bin/R' --no-site-file --no-environ --no-save --no-restore --quiet  \
  CMD INSTALL '/tmp/RtmpwKIUHz/devtools23da42049686/chron'  \
  --library='/home/ilya/R/x86_64-pc-linux-gnu-library/3.3' --install-tests 

'/usr/lib/R/bin/R' --no-site-file --no-environ --no-save --no-restore --quiet  \
  CMD INSTALL '/tmp/RtmpwKIUHz/devtools23da51f5a0bf/data.table'  \
  --library='/home/ilya/R/x86_64-pc-linux-gnu-library/3.3' --install-tests 

Installing dplyr
Installing assertthat
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Skipping install of 'lazyeval' from a cran remote, the SHA1 (0.2.0) has not changed since last install.
  Use `force = TRUE` to force installation
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  --library='/home/ilya/R/x86_64-pc-linux-gnu-library/3.3' --install-tests 


















In [3]:



    


library("sleuth")



    


















    






Loading required package: ggplot2
Loading required package: dplyr

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union


















In [18]:



    


base_dir <- "../results/kallisto"
sample_id <- dir(file.path(base_dir), pattern='^lo')
sample_id



    


















    







    
	
  1. 'lo01'
    2. 
	
  2. 'lo02'
    3. 
	
  3. 'lo03'
    4. 
	
  4. 'lo04'
    5. 
	
  5. 'lo05'
    6. 
	
  6. 'lo06'
    7. 
	
  7. 'lo07'
    8. 
	
  8. 'lo08'
    9. 
	
  9. 'lo09'
    10. 
	
  10. 'lo10'
    11. 
	
  11. 'lo11'
    12. 
	
  12. 'lo12'
    13. 



















In [33]:



    


kal_dirs <- sapply(sample_id, function(id) file.path(base_dir, id))
kal_dirs



    


















    








	
lo01

		
'../results/kallisto/lo01'

	
lo02

		
'../results/kallisto/lo02'

	
lo03

		
'../results/kallisto/lo03'

	
lo04

		
'../results/kallisto/lo04'

	
lo05

		
'../results/kallisto/lo05'

	
lo06

		
'../results/kallisto/lo06'

	
lo07

		
'../results/kallisto/lo07'

	
lo08

		
'../results/kallisto/lo08'

	
lo09

		
'../results/kallisto/lo09'

	
lo10

		
'../results/kallisto/lo10'

	
lo11

		
'../results/kallisto/lo11'

	
lo12

		
'../results/kallisto/lo12'




















In [27]:



    


dir(kal_dirs)



    


















    







    
	
  1. 'abundance.h5'
    2. 
	
  2. 'abundance.h5'
    3. 
	
  3. 'abundance.h5'
    4. 
	
  4. 'abundance.h5'
    5. 
	
  5. 'abundance.h5'
    6. 
	
  6. 'abundance.h5'
    7. 
	
  7. 'abundance.h5'
    8. 
	
  8. 'abundance.h5'
    9. 
	
  9. 'abundance.h5'
    10. 
	
  10. 'abundance.h5'
    11. 
	
  11. 'abundance.h5'
    12. 
	
  12. 'abundance.h5'
    13. 
	
  13. 'abundance.tsv'
    14. 
	
  14. 'abundance.tsv'
    15. 
	
  15. 'abundance.tsv'
    16. 
	
  16. 'abundance.tsv'
    17. 
	
  17. 'abundance.tsv'
    18. 
	
  18. 'abundance.tsv'
    19. 
	
  19. 'abundance.tsv'
    20. 
	
  20. 'abundance.tsv'
    21. 
	
  21. 'abundance.tsv'
    22. 
	
  22. 'abundance.tsv'
    23. 
	
  23. 'abundance.tsv'
    24. 
	
  24. 'abundance.tsv'
    25. 
	
  25. 'run_info.json'
    26. 
	
  26. 'run_info.json'
    27. 
	
  27. 'run_info.json'
    28. 
	
  28. 'run_info.json'
    29. 
	
  29. 'run_info.json'
    30. 
	
  30. 'run_info.json'
    31. 
	
  31. 'run_info.json'
    32. 
	
  32. 'run_info.json'
    33. 
	
  33. 'run_info.json'
    34. 
	
  34. 'run_info.json'
    35. 
	
  35. 'run_info.json'
    36. 
	
  36. 'run_info.json'
    37. 



















In [20]:



    


gsy_samples <- c(sample_id[seq(1, length(sample_id), 4)], sample_id[seq(3, length(sample_id), 4)])
gsy_samples



    


















    







    
	
  1. 'lo01'
    2. 
	
  2. 'lo05'
    3. 
	
  3. 'lo09'
    4. 
	
  4. 'lo03'
    5. 
	
  5. 'lo07'
    6. 
	
  6. 'lo11'
    7. 



















In [25]:



    


gsy_dirs <- c(kal_dirs[seq(1, length(kal_dirs), 4)], kal_dirs[seq(3, length(kal_dirs), 4)])
gsy_dirs



    


















    








	
lo01

		
'../results/kallisto/lo01'

	
lo05

		
'../results/kallisto/lo05'

	
lo09

		
'../results/kallisto/lo09'

	
lo03

		
'../results/kallisto/lo03'

	
lo07

		
'../results/kallisto/lo07'

	
lo11

		
'../results/kallisto/lo11'




















In [34]:



    


s2c <- data.frame(sample=gsy_samples, condition=c('wt', 'wt', 'wt', 'gsy', 'gsy', 'gsy'))
s2c <- dplyr::mutate(s2c, path = gsy_dirs)
s2c



    


















    









sampleconditionpath


	
1lo01                    wt                      ../results/kallisto/lo01

	
2lo05                    wt                      ../results/kallisto/lo05

	
3lo09                    wt                      ../results/kallisto/lo09

	
4lo03                    gsy                     ../results/kallisto/lo03

	
5lo07                    gsy                     ../results/kallisto/lo07

	
6lo11                    gsy                     ../results/kallisto/lo11





















In [35]:



    


so <- sleuth_prep(s2c, ~ condition)



    


















    






reading in kallisto results
......
normalizing est_counts
19296 targets passed the filter
normalizing tpm
merging in metadata
normalizing bootstrap samples
summarizing bootstraps

















In [36]:



    


so <- sleuth_fit(so)



    


















    






fitting measurement error models
shrinkage estimation
Adding missing grouping variables: `x_group`
computing variance of betas

















In [37]:



    


so <- sleuth_fit(so, ~1, 'reduced')



    


















    






fitting measurement error models
shrinkage estimation
Adding missing grouping variables: `x_group`
computing variance of betas

















In [38]:



    


#so <- sleuth_test(so, which_beta='conditionwt')
so <- sleuth_lrt(so, 'reduced', 'full')



    











In [39]:



    


models(so)



    


















    






[  full  ]
formula:  ~condition 
coefficients:
	(Intercept)
 	conditionwt
[  reduced  ]
formula:  ~1 
coefficients:
	(Intercept)

















In [41]:



    


source("http://bioconductor.org/biocLite.R")
biocLite("biomaRt")



    


















    






Bioconductor version 3.3 (BiocInstaller 1.22.3), ?biocLite for help
BioC_mirror: https://bioconductor.org
Using Bioconductor 3.3 (BiocInstaller 1.22.3), R 3.3.1 (2016-06-21).
Installing package(s) ‘biomaRt’
also installing the dependencies ‘BiocGenerics’, ‘Biobase’, ‘IRanges’, ‘RSQLite’, ‘S4Vectors’, ‘XML’, ‘AnnotationDbi’

Old packages: 'boot', 'MASS', 'spatial'

















In [42]:



    


mart <- biomaRt::useMart(biomart = "ENSEMBL_MART_ENSEMBL",
  dataset = "celegans_gene_ensembl",
  host = 'ensembl.org')



    











In [43]:



    


t2g <- biomaRt::getBM(attributes = c("ensembl_transcript_id", "ensembl_gene_id",
    "external_gene_name"), mart = mart)
t2g <- dplyr::rename(t2g, target_id = ensembl_transcript_id,
  ens_gene = ensembl_gene_id, ext_gene = external_gene_name)
so <- sleuth_prep(s2c, ~ condition, target_mapping = t2g)



    


















    






reading in kallisto results
......
normalizing est_counts
19296 targets passed the filter
normalizing tpm
merging in metadata
normalizing bootstrap samples
summarizing bootstraps

















In [44]:



    


so <- sleuth_fit(so)



    


















    






fitting measurement error models
shrinkage estimation
Adding missing grouping variables: `x_group`
computing variance of betas

















In [46]:



    


so <- sleuth_fit(so, ~1, 'reduced')



    


















    






fitting measurement error models
shrinkage estimation
Adding missing grouping variables: `x_group`
computing variance of betas

















In [47]:



    


so <- sleuth_lrt(so, 'reduced', 'full')



    











In [51]:



    


so <- sleuth_wt(so, which_beta = 'conditionwt')



    











In [52]:



    


sleuth_live_settings(test_type='wt')



    


















    






$test_type = 'wt'
















In [ ]:



    


sleuth_live(so)



    


















    






Listening on http://127.0.0.1:42427
Warning message:
: Removed 8 rows containing missing values (geom_point).Warning message:
: Removed 8 rows containing missing values (geom_point).
















In [10]:



    


write.table(res,file='../kallisto/results.tsv', sep='\t')



    











In [ ]:



    


sleuth_live(so)



    


















    






Loading required package: shiny

Listening on http://127.0.0.1:7861

















In [ ]:



    


res <- sleuth_results(so, 'conditionwt')



    











In [ ]:

Content source: eco32i/biodata

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