Closed yuxinPenny closed 2 years ago
Hi @yuxinPenny ,
you can use the python snippets such as E.g. Epinano_sumErr.py, Epinano_make_delta.py
and Epinano_delta_sumErr.py
to generate combined features and then run Epinano_DiffErr.R
on the results.
btw, you should use Epinano_DiffErr.R for small RNAs. if you want to apply it to long reference sequences, you'd better do it in a sliding window manner. Otherwise, it has reduced sensitivity.
When I ran the DiffErr script to detect modified based, I came up with the following error:
Error in
[.data.frame
(input, , c("X.Ref", "pos", "position", "base", : undefined columns selected Calls: cleanup -> [ -> [.data.frame Execution haltedThe command line used to run is: Rscript /home/share/yuxin/EpiNano/Epinano_DiffErr.R \ -k /home/share/yuxin/2021Fall/DATA/hm_tmp/HMEC_ALKBH5/bam/HMEC_ALKBH5_g.minus_strand.per.site.csv \ -w /home/share/yuxin/2021Fall/DATA/hm_tmp/HMEC_WT/bam/HMEC_WT_g.minus_strand.per.site.csv \ -t 10 -o HMEC_minus_DiffErr -f cov,q_mean,q_median,q_std,mis,ins,del
the head of the epinano_variant output is:
Ref,pos,base,strand,cov,q_mean,q_median,q_std,mis,ins,del
0,24738,C,-,1,11.00000,11.00000,0.00000,1.00000,0.00000,0.00000 0,24739,T,-,1,11.00000,11.00000,0.00000,1.00000,0.00000,0.00000 0,24740,G,-,1,12.00000,12.00000,0.00000,1.00000,0.00000,0.00000 0,24741,C,-,1,6.00000,6.00000,0.00000,1.00000,0.00000,0.00000 0,24742,T,-,1,6.00000,6.00000,0.00000,1.00000,0.00000,0.00000 0,24743,G,-,1,6.00000,6.00000,0.00000,1.00000,0.00000,0.00000 0,24744,A,-,1,12.00000,12.00000,0.00000,1.00000,0.00000,0.00000
Can we only use one feature to predict? PS. the #Ref number in the epinano_variant output csv file seem weird as well. Can you give some clues?