Comments (3)
Thanks so much @jorondo1 -- I have some suspicions about what's happening. Two questions:
- How are you producing your count matrix? Are you filtering singletons?
- Could you please provide some example frequency count tables?
I think I can diagnose given the above information.
from breakaway.
The counts matrix is a Sourmash output. Here is a subset of the phyloseq object I am using as input. I have not done any filtering (except here for this subset), the counts are as-is. It looks like this:
GQ1 GQ2 GQ32 GQ33 GQ3 GQ34 GQ35 GQ4 GQ36 GQ37 GQ5 GQ6 GQ11 GQ12 GQ40
1 [Eubac… 130 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 [Eubac… 1115 10007 0 0 7426 0 109 251 0 0 12743 9898 0 80 0
3 [Eubac… 169 111 0 0 0 0 0 0 0 0 0 0 0 0 0
4 Actino… 287 214 0 0 198 0 0 0 0 0 0 211 137 857 0
5 Actino… 323 182 0 0 0 0 0 0 0 0 0 0 0 304 0
6 Actino… 75 0 0 0 0 0 0 0 0 0 0 0 0 359 0
7 Actino… 2333 28694 0 0 8682 0 0 2403 0 0 7657 9792 141 64 0
8 Actino… 562 171 0 0 196 0 0 0 0 0 213 0 70 215 0
9 Actino… 3796 1403 0 0 3390 0 0 559 0 0 157 124 1215 1414 0
10 Actino… 860 2216 0 0 3446 0 0 73 0 0 1970 967 1106 1233 0
Thanks a lot for your time!
from breakaway.
Interesting -- it looks like you have very few biological units that are observed infrequently. What breakaway is (reasonably) inferring from this is that there are few unobserved biological units, which is why breakaway's estimates are the same as your plugin estimates. Basically if your data structure doesn't suggest that you have anything that's rare, it will predict that you have nothing missing! This is definitely the intended behavior of breakaway, so I'm going to close it as an issue. I hope that helps answer your question!
That said, given the extremely strong correlation between depth and observed richness, if you wanted to fit a model for richness adjusting for depth, you might consider fitting a model like lm(sample_richness ~ your_covariates + depth)
. I don't recommend this often but given that you don't have the ability to estimate the # of rare units (min hash sketches?), this might be the best you can do in this setting.
Next time I chat with Taylor and Titus I might ask them about whether sourmash can detect rare/low abundance min hash sketches, since I'm ignorant on this.
from breakaway.
Related Issues (20)
- Replace devtools::install_github() with remotes::install_github() in vignettes HOT 1
- Interpretation of betta coefficient table significance HOT 4
- Error and warning message when using breakaway() and betta() HOT 7
- population richness estimates are lower than sample richness estimates HOT 1
- error when using the function build_frequency_count_tables () HOT 3
- rename branch `master` to `main` HOT 1
- Betta: Standard errors same for almost all factor levels HOT 2
- breakaway::breakaway for deep-sequencing results having an evenly distributed frequency table HOT 1
- kicked off CRAN, grrrr HOT 5
- CRAN issues: internals from lme4 HOT 1
- checking for unstated dependencies in vignettes ... NOTE HOT 1
- warnings in test-betta.R HOT 1
- Request: Adjust p-value digits HOT 3
- Parameter descriptions for objective Bayes functions
- R session crashes when fitting betta model HOT 6
- Include new tests to increase code coverage to 90%
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- General questions: Estimated richness lower than observed richness HOT 3
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from breakaway.