add_jervis_bardy() calculates the spearman correlation between
relative abundance and sample DNA concentration,
for each taxon and adds the correlation metric and p-value
to the taxa table under the column names "jb_cor" and "jb_p", respectively.
If taxa show a distribution that is negatively correlated with
DNA concentration, it indicates their potential as contaminants.
Arguments
- ta
A tidytacos object.
- dna_conc
A variable in the samples table that contains dna concetrations (unquoted).
- sample_condition
An optional extra condition that samples must pass before calculations.
- min_pres
The minimum number of samples a taxon has to be present in for its correlation to be calculated.
Details
See: J. Jervis-Bardy et al., “Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data,” Microbiome, vol. 3, no. 1, Art. no. 1, 2015, doi: 10.1186/s40168-015-0083-8.
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:tidytacos’:
#>
#> everything
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
# filter out blank samples
plants <- leaf %>%
filter_samples(Plant != "Blank")
# assume Leafweight is a proxy for DNA concentration of the sample
plants_jb <- plants %>%
add_jervis_bardy(dna_conc = Leafweight)
# we can do this in one step!
plants_jb <- leaf %>%
add_jervis_bardy(
dna_conc = Leafweight,
sample_condition = Plant != "Blank"
)
# show the negative correlations
plants_jb$taxa %>%
select(taxon_id, starts_with("jb_")) %>%
filter(jb_cor < 0) %>%
arrange(jb_p)
#> # A tibble: 409 × 3
#> taxon_id jb_cor jb_p
#> <chr> <dbl> <dbl>
#> 1 t256 -0.964 0.00139
#> 2 t133 -0.706 0.00664
#> 3 t21 -0.582 0.0100
#> 4 t212 -0.886 0.0167
#> 5 t471 -0.886 0.0167
#> 6 t30 -0.521 0.0295
#> 7 t195 -0.533 0.0321
#> 8 t111 -1 0.0417
#> 9 t199 -1 0.0417
#> 10 t279 -1 0.0417
#> # ℹ 399 more rows