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

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,

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