LSA_output; several LS for same OTU pairs
Hi, I have some issues to understand and analysis the output of an LSA analysis. I have run 'lsa_compute ' via 'par_ana' on a sequencing dataset as follow:
python ~/charade-elsa-80c7298487ce/lsa/par_ana.py Raw_abundance_OTUs_dom.txt Raw_abundance_OTUs_dom.lsa 'lsa_compute.py %s %s -e Raw_abundance_OTUs_dom.txt -r 1 -d 0 -s 7 -b 0 -n robustZ -p theo' $PWD
Here, I would like to analysis the OTUs cooccurrence in 7 sites (delay =0, then).
After filtering of the output files (based on p and q values), I haver noticed that for some OTU pairs, I have several LS scores. For instance, i have:
X Y LS lowCI upCI Xs Ys Len Delay P PCC Ppcc SPCC Pspcc Dspcc SCC Pscc SSCC Psscc Dsscc Q Qpcc Qspcc Qscc Qsscc Xi Yi
OTU_1380 OTU_12316 -1.083529 -1.083529 -1.083529 6 6 1 0 0.016419 -0.128134 0.80885 -0.128134 0.80885 0 -0.054772 0.917924 -0.054772 0.917924 0 0.005144 0.999432 0.999432 0.002234 0.002234 1 2826
OTU_1380 OTU_12316 1.069152 1.069152 1.069152 6 6 2 0 0.018619 0.620174 0.189003 0.620174 0.189003 0 0.602495 0.205611 0.602495 0.205611 0 0.005144 0.982799 0.982799 0.000564 0.000564 1 989
OTU_1380 OTU_12316 1.069152 1.069152 1.069152 6 6 2 0 0.018619 0.620174 0.189003 0.620174 0.189003 0 0.602495 0.205611 0.602495 0.205611 0 0.005144 0.982799 0.982799 0.000564 0.000564 1 98
OTU_1380 OTU_12316 1.069152 1.069152 1.069152 6 6 2 0 0.018619 0.620174 0.189003 0.620174 0.189003 0 0.602495 0.205611 0.602495 0.205611 0 0.005144 0.982799 0.982799 0.000564 0.000564 1 929
OTU_1380 OTU_12316 1.069152 1.069152 1.069152 6 6 2 0 0.018619 0.620174 0.189003 0.620174 0.189003 0 0.602495 0.205611 0.602495 0.205611 0 0.005144 0.982799 0.982799 0.000564 0.000564 1 920
OTU_1380 OTU_12316 1.069152 1.069152 1.069152 6 6 2 0 0.018619 0.620174 0.189003 0.620174 0.189003 0 0.602495 0.205611 0.602495 0.205611 0 0.005144 0.982799 0.982799 0.000564 0.000564 1 695
OTU_1380 OTU_12316 1.069152 1.069152 1.069152 6 6 2 0 0.018619 0.620174 0.189003 0.620174 0.189003 0 0.602495 0.205611 0.602495 0.205611 0 0.005144 0.982799 0.982799 0.000564 0.000564 1 664
OTU_1380 OTU_12316 1.069152 1.069152 1.069152 6 6 2 0 0.018619 0.620174 0.189003 0.620174 0.189003 0 0.602495 0.205611 0.602495 0.205611 0 0.005144 0.982799 0.982799 0.000564 0.000564 1 431
How should I interpret such results? In this example, all the results are ordered according to their p and q values. Here, the first line (best p and q values) has a negative LS score while the other ones are all positive. It is quite confusing. Best,
Comments (5)
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repo owner -
repo owner Hi. Thanks for raising the issue. Can you attach a minimal input to replicate the error. On the meanwhile, you can filter out the duplicated lines if they are just identical to proceed your project.
Thank you.
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Hi, And thank you for your reply. Here is the first lines of my input dataset:
#OTU_ID t1 t2 t3 t4 t5 t6 t7 OTU_12283 40 8 935 3 157 80 345 OTU_9775 390 76 11 86 487 513 2 OTU_751 4 38 33 18 49 3 1419 OTU_11718 0 8 0 0 1 0 1554 OTU_6787 12 83 607 523 3 170 158
Indeed, it seems that most of the lines are duplicated (solely the last output criteria (Yi) may varied between them). My question was concerning the case when I may have two different output results for the same OTUs pairs. I mean "different" when the LS scores can be either negative or positive for a relative similar p/q values. I have solely this "case" 15 times for a total of around 20000 edges but I wonder what values should I trust (which may change the biological meaning of such OTUs pairs). In each case, I have only one line with a negative LS score for a lot of duplicated lines with positive and identical LS score.
Thanks again for your help.
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repo owner lsa_compute ../test/multiLS.txt ../test/multiLS.lsa -r 1 -s 7 -d 3 -p theo -x 1000 -f none -n percentileZ -e ../test/multiLS.txt -m 0
tried your file with above command, cannot replicate error. Mark as invalid and close.
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repo owner - changed status to invalid
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#9was marked as a duplicate of this issue.