xiangpin
commented
2 years ago I think you want to perform the rda or cca analysis. You can use mp_cal_rda or mp_cal_cca to do it, then use mp_plot_ord to visualize the result.
library(MicrobiotaProcess)
data(mouse.time.mpse)
genus.abun <- mouse.time.mpse %>% mp_rrarefy() %>% mp_cal_abundance(.abundance=RareAbundance) %>% mp_extract_abundance(taxa.class=Genus, rmun=T)
# In this example, I just randomly select some genus
> genus.abun %>% dplyr::filter(label %in% c("g__Turicibacter", "g__Neisseria","g__Anaeroplasma", "g__Oscillibacter")) %>% tidyr::unnest(RareAbundanceBySample)
# A tibble: 76 × 6
label nodeClass Sample RareAbundance RelRareAbundanceBySam…¹ time
<fct> <chr> <chr> <int> <dbl> <chr>
1 g__Oscillibacter Genus F3D0 57 2.26 Early
2 g__Oscillibacter Genus F3D1 76 3.02 Early
3 g__Oscillibacter Genus F3D141 6 0.238 Late
4 g__Oscillibacter Genus F3D142 0 0 Late
5 g__Oscillibacter Genus F3D143 14 0.556 Late
6 g__Oscillibacter Genus F3D144 14 0.556 Late
7 g__Oscillibacter Genus F3D145 12 0.477 Late
8 g__Oscillibacter Genus F3D146 54 2.14 Late
9 g__Oscillibacter Genus F3D147 13 0.516 Late
10 g__Oscillibacter Genus F3D148 8 0.318 Late
# … with 66 more rows, and abbreviated variable name ¹RelRareAbundanceBySample
# ℹ Use `print(n = ...)` to see more rows
genus.abun %<>% dplyr::filter(label %in% c("g__Turicibacter", "g__Neisseria","g__Anaeroplasma", "g__Oscillibacter")) %>% tidyr::unnest(RareAbundanceBySample)
> genus.abun %>% dplyr::select(label, Sample, RelRareAbundanceBySample) %>% tidyr::pivot_wider(id_cols=Sample, names_from=label, values_from=RelRareAbundanceBySample)
# A tibble: 19 × 5
Sample g__Oscillibacter g__Turicibacter g__Neisseria g__Anaeroplasma
<chr> <dbl> <dbl> <dbl> <dbl>
1 F3D0 2.26 1.43 0 1.23
2 F3D1 3.02 0 0 1.03
3 F3D141 0.238 2.10 0 0
4 F3D142 0 2.07 0 0
5 F3D143 0.556 1.59 0 0
6 F3D144 0.556 3.26 0.0794 0
7 F3D145 0.477 1.95 0.0397 0.0397
8 F3D146 2.14 0.834 0 0
9 F3D147 0.516 2.54 0 0.199
10 F3D148 0.318 2.42 0 0
11 F3D149 0.318 1.47 0 0
12 F3D150 0.913 0.635 0 0
13 F3D2 1.91 0 0 0.318
14 F3D3 0.715 0.278 0 0.318
15 F3D5 2.98 0.874 0 0
16 F3D6 2.38 0 0 0.0397
17 F3D7 1.35 0 0 0.318
18 F3D8 1.71 0.119 0 0.318
19 F3D9 1.59 0 0 0.596
mouse.time.mpse %>% mp_rrarefy() %>% dplyr::left_join(genus.abun, by='Sample') %>% mp_cal_rda(.abundance=RareAbundance, .formula=~g__Oscillibacter + g__Turicibacter + g__Neisseria + g__Anaeroplasma) %>% mp_plot_ord(.group=time, bg.colour='white')
marwa38
Hi .. This is not an issue but sort of discussion or know-how question if you don't mind.
Any guidance on how to create PCoA while taking into account the genus level of microbiota along with other environmental factors?
Something similar to this figure: