Estimation statistics for categorical comparisons on a list_phyloseq
Source:R/estim_lpq.R
estim_diff_lpq.RdApplies estim_diff_pq() to each phyloseq object in a list_phyloseq
and combines the results. This allows comparing estimation statistics
across different bioinformatic pipelines or parameter settings.
Arguments
- x
(required) A list_phyloseq object.
- fact
(character, required) The name of a categorical column in
sample_datato use as the grouping factor. Must be present in all phyloseq objects.- ...
Additional arguments passed to
estim_diff_pq().- verbose
(logical, default TRUE) If TRUE, print progress messages.
Value
A list of class "estim_diff_lpq_result" with components:
- results
A named list of
estim_diff_pq_resultobjects (one per phyloseq)- summary
A tibble combining all summaries with an additional
namecolumn identifying the source phyloseq
Examples
lpq <- list_phyloseq(
list(
fungi = data_fungi,
fungi_clust = postcluster_pq(data_fungi),
fungi_rarefy = rarefy_even_depth(data_fungi),
fungi_with_less_otu_in_High = multiply_counts_pq(data_fungi,
fact = "Height", prop=0.8,
conditions = "High",
multipliers = 0)
),
same_bioinfo_pipeline = FALSE
)
#> Partitioning sequences by 6-mer similarity:
#>
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#>
#> Time difference of 0.38 secs
#>
#> Sorting by relatedness within 779 groups:
#>
iteration 1 of up to 15 (100.0% stability)
iteration 2 of up to 15 (100.0% stability)
iteration 3 of up to 15 (100.0% stability)
iteration 4 of up to 15 (100.0% stability)
iteration 4 of up to 15 (100.0% stability)
#>
#> Time difference of 0.3 secs
#>
#> Clustering sequences by 9-mer similarity:
#>
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#>
#> Time difference of 4.57 secs
#>
#> Clusters via relatedness sorting: 100% (0% exclusively)
#> Clusters via rare 6-mers: 100% (0% exclusively)
#> Estimated clustering effectiveness: 100%
#>
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#> the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 1032OTUs were removed because they are no longer
#> present in any sample after random subsampling
#> ...
#> Modified 1136 taxa in 41 matched samples
#> ℹ Building summary table for 4 phyloseq objects...
#> ℹ Computing comparison characteristics...
#> ℹ Checking sample and taxa overlap...
#> ℹ Detected comparison type: ROBUSTNESS
#> ℹ 185 common samples, 223 common taxa
#> ✔ list_phyloseq created (ROBUSTNESS)
results <- estim_diff_lpq(lpq, fact = "Height")
#> Running estimation statistics (categorical) on 4 phyloseq objects
#> Factor: Height
#> → Processing: fungi
#> → Processing: fungi_clust
#> → Processing: fungi_rarefy
#> → Processing: fungi_with_less_otu_in_High
#> Warning: Error processing 'fungi_with_less_otu_in_High': estimated adjustment 'w' is infinite
results$summary
#> # A tibble: 18 × 9
#> name metric comparison effect_size ci_lower ci_upper pvalue_permtest
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fungi Hill_0 High vs Low -0.120 -0.560 0.309 0.577
#> 2 fungi Hill_0 High vs Mi… -0.187 -0.617 0.232 0.386
#> 3 fungi Hill_1 High vs Low -0.0158 -0.434 0.418 0.942
#> 4 fungi Hill_1 High vs Mi… 0.163 -0.254 0.585 0.454
#> 5 fungi Hill_2 High vs Low -0.0387 -0.470 0.387 0.869
#> 6 fungi Hill_2 High vs Mi… 0.187 -0.230 0.592 0.396
#> 7 fungi_clust Hill_0 High vs Low -0.137 -0.580 0.289 0.520
#> 8 fungi_clust Hill_0 High vs Mi… -0.180 -0.612 0.230 0.410
#> 9 fungi_clust Hill_1 High vs Low 0.0433 -0.364 0.462 0.837
#> 10 fungi_clust Hill_1 High vs Mi… 0.221 -0.193 0.622 0.314
#> 11 fungi_clust Hill_2 High vs Low 0.0353 -0.380 0.456 0.873
#> 12 fungi_clust Hill_2 High vs Mi… 0.239 -0.171 0.640 0.276
#> 13 fungi_rarefy Hill_0 High vs Low -0.0724 -0.497 0.349 0.694
#> 14 fungi_rarefy Hill_0 High vs Mi… -0.0217 -0.434 0.413 0.877
#> 15 fungi_rarefy Hill_1 High vs Low -0.0647 -0.491 0.360 0.766
#> 16 fungi_rarefy Hill_1 High vs Mi… -0.00864 -0.429 0.426 0.971
#> 17 fungi_rarefy Hill_2 High vs Low -0.0631 -0.495 0.363 0.770
#> 18 fungi_rarefy Hill_2 High vs Mi… 0.00665 -0.418 0.435 0.976
#> # ℹ 2 more variables: pvalue_welch <dbl>, pvalue_mann_whitney <dbl>
# Plot results for two phyloseq objects
ggplot(results$summary, aes(x = metric, y = effect_size, color=name)) +
facet_wrap(~comparison) +
geom_point(position=position_dodge(width=0.5)) +
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.2, position=position_dodge(width=0.5 ))
