Alpha, Beta Diversity Erros; Qiime2 2020.08.

Hi Dear admins and members,
Please help me! I got stuck in these steps! I tried hard to solve these errors but I was unsuccessful.
After I got the errors in diversities! Then I tried to filter samples then follow same steps! I have three groups which each one has four samples. I separated samples (based on weeks, e.i. week1, week2, week3 and week4) by filtration. Now, I have the same error when I wanted to get alpha and beta diversities plots.

They are error:

qiime diversity alpha-group-significance \

--i-alpha-diversity faith_pd_vector.qza
--m-metadata-file 1.tsv
--o-visualization faith-pd-significance-WeekOnes.qzv
Plugin error from diversity:

Metadata does not contain any columns that satisfy this visualizer's requirements. There must be at least one metadata column that contains categorical data, isn't empty, doesn't consist of unique values, and doesn't consist of exactly one value.

Debug info has been saved to /tmp/qiime2-q2cli-err-yyjwo_i1.log

qiime diversity beta-group-significance \

--i-distance-matrix unweighted_unifrac_distance_matrix.qza
--m-metadata-file 1.tsv
--m-metadata-column group
--o-visualization unweighted-unifrac-group-significance-BetaDiversity.qzv
Plugin error from diversity:

All values in the grouping vector are unique. This method cannot operate on a grouping vector with only unique values (e.g., there are no 'within' distances because each group of objects contains only a single object).

Debug info has been saved to /tmp/qiime2-q2cli-err-2beacemk.log

The initial metadata file:
Screenshot from 2021-01-14 17-23-56

The filtered metadata file:
Screenshot from 2021-01-14 17-25-13

I do not know how to solve these errors!
As you see I have at least two columns provided info.

Thanks a lot

Hi @TurboQiimer,
My suggestion is to use the metadata including all the samples, but with few corrections.
In your metadata, each sample belong to its own group, and the statistic beyond the scene complains that there is at least one metadata category including only one sample.

You should use something:

sampleid          | type          | group
#q2:types         | categorical   | numeric
16SDNANacl1       | NaCl          | 1
16SDNANacl2       | NaCl          | 1
16SDNAControl1    | Contol        | 2
16SDNAControl2    | Control       | 2
16SDNASeasalt1    | Seasalt        | 3
16SDNASeasalt2    | Seasalt       | 3

With this changes you could use the column ‘type’ for the beta diversity analysis, this column should also included into the alpha-diversity results with the alpha-group-significance command.

The column group (which is now ‘numeric’), won’t be used in these analysis, if you want compare the alpha diversity vs numeric values, you could use ‘diversity alpha-correlation’ plug in.

Hope it helps

1 Like

Thank you very much for the reply!
I have a confusion with table design that I need to ask. As you wrote the sample, you should know 16SDNANaCl1 is not same sample with 16SDNANaCl2 (they are not replica- they are independent sample) and 16SDNASeaSalt1 is not same with 16SDNASeaSalt2 (they are independent samples too)! and so forth on. But we group them in same group for example 1 (in numeric column). By regarding the explanation, can I use your table model or design?

I supposed they are at different weeks of treatment (?), however from the point of view of the used ‘treatment’ (eg. salt), they are in the same group. If this answer our biological question is up to you really. The main point is that the diversity analysis could work only if there are replicates for all category you analysing. Sure, you can use that as template, but I basically removed the numbers in your ‘type’ column!

Hope it helps

1 Like

I think you are right! I used grouped rep-seq and feature table files! that is why I told you there are no replicas and the samples are independent! I think I have to start with de-grouped files!
I have two treated groups (NaCl and seasalt treated samples plus control [three groups] for week one, week two, week four and week six!
If you more suggestion please let me know.
Thanks a lot.

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