Atacama Soil Microbiome

These chapters demonstrate the QIIME2 plugins for high-dimensional statistics on the Atacama soil microbiome dataset [D1]. q2-gglasso solves a range of graphical lasso problems to identify microbial associations, and q2-classo assesses those associations by fitting sparse log-contrast models. The sequence data were processed under QIIME 2 version 2026.7 [S1]: the q2-demux plugin demultiplexed and quality-filtered the raw reads, DADA2 [D2] denoised them through the q2-dada2 plugin, and the q2-feature-classifier [S4] assigned taxonomy to the amplicon sequence variants (ASVs) with a naive Bayes classifier trained on the Silva database [D3].

Data preprocessing pipeline

Preprocessing follows the published Atacama tutorial. To give these ASVs meaningful names, this book renames the ASV keys as follows:

ASV label	Real ASV ID
ASV-1	89cb1ddf89dcf11d86f725dbcaa9a5ce
ASV-2	cdb9c0ee3bba4c3d8b9292eb575bd9e3
ASV-3	4c8ff0ea98d2c0ebb486e33bd96c61f9
ASV-4	a56b903521b8ab33a7878b35582803a8
ASV-5	5c78314ff92e6fec9aa07acc1fa0dc24
ASV-6	dc8a2f47b3d1dc2e1f5f805891976b29
ASV-7	6b780e361cfc5f06def718518324bdcb
ASV-8	f6c10a04d57159c0d64d6bc30c677471
ASV-9	ffd60d684f32e6fd5b47fe90095f9d34
ASV-10	ef3fdbe1dcde754d91130cde6a4b4d61
ASV-11	a36b38f754f6abd278aeb9dbc7696343
ASV-12	a7b877ae6d2f079a15b6b192a4425620
ASV-13	409faa5f5353e543bf6d99125c7c0e83

Data for downstream analysis

The Atacama soil microbiome dataset [D1] supplies:

• 50 samples from Atacama Desert soil

• 13 microbial taxa (ASVs)

• Environmental covariates: pH, elevation, temperature, humidity and vegetation

The original data is available through the European Nucleotide Archive under accession ERP019482.

Sample ID	ASV-1	ASV-2	ASV-3	…	ASV-11	ASV-12	ASV-13	Elevation	pH	Avg Soil RH	Avg Soil Temp	Vegetation
BAQ2420.1.1	0.0	11.0	0.0	…	115.0	0.0	0.0	2420	9.33	82.54	22.45	no
BAQ2420.1.2	0.0	0.0	0.0	…	0.0	0.0	0.0	2420	9.36	82.54	22.45	no
BAQ2420.1.3	0.0	0.0	0.0	…	0.0	0.0	0.0	2420	8.90	82.54	22.45	no
…	…	…	…	…	…	…	…	…	…	…	…	…
YUN3856.2	6.0	13.0	26.0	…	0.0	0.0	104.0	3856	7.43	99.44	9.51	yes
YUN3856.3	21.0	36.0	23.0	…	33.0	0.0	0.0	3856	7.43	99.44	9.51	yes

The table above is a snapshot of the counts. Download the QIIME2 artifact and the matching metadata.

Covariates and missing values

Four numeric covariates accompany these samples — pH, elevation, average soil relative humidity and average soil temperature. They are the outcomes and the adjustment variables in the log-contrast regression chapters, and the latent components in Latent Components & Covariates are tested against them.

Fig. 2 Soil pH across the 75 samples, before and after scaling. The bulk of the distribution sits between 6 and 9 — alkaline, as expected for this desert. The bar at zero holds the samples whose pH was never recorded.

Important

Some covariate values are missing and coded as 0, not as blanks. Counted directly from atacama-selected-covariates-veg.tsv (75 samples):

covariate	zeros	plausible as a real value?
ph	8	No — soil pH of 0 is not physically possible
average-soil-relative-humidity	3	Implausible
average-soil-temperature	3	Possible at altitude, but suspicious
elevation	0	— (range 895–4700 m)

The zeros do not co-occur: none of the humidity-zero or temperature-zero samples is also a pH-zero sample, so this is per-measurement missingness rather than eight incomplete records. The eight pH-zero samples are BAQ1370.3, BAQ1552.2, BAQ895.2, BAQ895.3, YUN1005.2, YUN3008.2, YUN3008.3 and YUN3184.2.

A zero is not neutral. Scaled, those eight samples land at \(-2.80\) — the minimum of the distribution — so a regression that takes them at face value is told that eight sites are far more acidic than any other, when their pH was never recorded. Decide whether to drop those samples, impute them, or exclude pH as a covariate, and state which you did. The tutorials that follow pass the file through unchanged, which demonstrates the commands rather than a treatment of missing data.