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About mp_rrarefy and mp_cal_rarecurve

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#90 4 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
r
Domain
data, performance

Research direction

Start by reproducing the supplied R workflow with treated_array.txt and adjmeta.txt, focusing on mp_rrarefy() and mp_cal_rarecurve(). Trace which operation dominates runtime and review the resulting object sizes and parameters. Done means the slowdown is explained and a validated fix or clear usage guidance is provided.

Written by the indexing model from the issue text.

Description

Hi, I want to analyze the results of metaphlan using MicrobiotaProcess, below is my code:

mpse2 <- mp_import_metaphlan(profile='metaphlan/treated_array.txt', mapfilename='metaphlan/adjmeta.txt')
mpse2
mpse2 %<>% mp_rrarefy() 
mpse2 %<>% mp_cal_rarecurve(.abundance = RareAbundance,chunks = 400) 

Treated_array.txt is a merged metaphlan result. I converted the relative abundance to absolute abundance according to the number of reads.
[treated_array.txt](https://
adjmeta.txt
github.com/YuLab-SMU/MicrobiotaProcess/files/11269250/treated_array.txt)

I found that the calculation of mp_rrarefy and mp_cal_rarecurve is very slow, mp_rrarefy took me about half an hour, as for mp_cal_rarecurve I waited for two hours and still didn't get the result (so I terminated the program). I don't quite understand why it's taking so much time, and how can I fix it?

Thanks,
LeeLee

Dominant language
R
Stars
196
Forks
36
PR merge metrics
No merged PRs in 30d

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