include.lowest = FALSE in mp_filter_taxa seems not work
Hua-CM opened this issue · comments
When I use the mp_filter_taxa function, I found that include.lowest seems not work, which always return the include.lowest=False reuslt. However, when try it using example dataset mouse.time.mpse, it works fine. I could not figure out why but I think this is very important, because many users may not notice this.
My dataset is unpublic, so if you need it to test, welcome to contact me.
> mp_filter_taxa(mp_raw, .abundance = Abundance, min.abun = 1, min.prop = 0.1, iclude.lowest=FALSE)
# A MPSE-tibble (MPSE object) abstraction: 825,086 × 15
# OTU=6763 | Samples=122 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class, Order, Family, Genus, Species
OTU Sample Abundance origin suborigin bioreptype biotype oritype Kingdom Phylum Class Order Family Genus Species
<chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 OTU_1 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__E… f__Ye… g__S… s__Ser…
2 OTU_2 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__Par…
3 OTU_3 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
4 OTU_4 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__S… f__Sp… g__S… s__un_…
5 OTU_5 JLBX1E 11 JL BX 1 E JLE k__Bacteria p__Bactero… c__B… o__F… f__We… g__C… s__Chr…
6 OTU_6 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__R… f__Rh… g__P… s__Phy…
7 OTU_7 JLBX1E 4 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__un_…
8 OTU_8 JLBX1E 27 JL BX 1 E JLE k__Bacteria p__Actinob… c__A… o__M… f__Mi… g__M… s__un_…
9 OTU_9 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
10 OTU_10 JLBX1E 2 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Ox… g__C… s__Col…
# ℹ 825,076 more rows
# ℹ Use `print(n = ...)` to see more rows
>
> mp_filter_taxa(mp_raw, .abundance = Abundance, min.abun = 1, min.prop = 0.1, iclude.lowest=TRUE)
# A MPSE-tibble (MPSE object) abstraction: 825,086 × 15
# OTU=6763 | Samples=122 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class, Order, Family, Genus, Species
OTU Sample Abundance origin suborigin bioreptype biotype oritype Kingdom Phylum Class Order Family Genus Species
<chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 OTU_1 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__E… f__Ye… g__S… s__Ser…
2 OTU_2 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__Par…
3 OTU_3 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
4 OTU_4 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__S… f__Sp… g__S… s__un_…
5 OTU_5 JLBX1E 11 JL BX 1 E JLE k__Bacteria p__Bactero… c__B… o__F… f__We… g__C… s__Chr…
6 OTU_6 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__R… f__Rh… g__P… s__Phy…
7 OTU_7 JLBX1E 4 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__un_…
8 OTU_8 JLBX1E 27 JL BX 1 E JLE k__Bacteria p__Actinob… c__A… o__M… f__Mi… g__M… s__un_…
9 OTU_9 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
10 OTU_10 JLBX1E 2 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Ox… g__C… s__Col…
# ℹ 825,076 more rows
# ℹ Use `print(n = ...)` to see more rows
> mp_filter_taxa(mp_raw, .abundance = Abundance, min.abun = 2, min.prop = 0.1, iclude.lowest=TRUE)
# A MPSE-tibble (MPSE object) abstraction: 545,462 × 15
# OTU=4471 | Samples=122 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class, Order, Family, Genus, Species
OTU Sample Abundance origin suborigin bioreptype biotype oritype Kingdom Phylum Class Order Family Genus Species
<chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 OTU_1 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__E… f__Ye… g__S… s__Ser…
2 OTU_2 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__Par…
3 OTU_3 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
4 OTU_4 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__S… f__Sp… g__S… s__un_…
5 OTU_5 JLBX1E 11 JL BX 1 E JLE k__Bacteria p__Bactero… c__B… o__F… f__We… g__C… s__Chr…
6 OTU_6 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__R… f__Rh… g__P… s__Phy…
7 OTU_7 JLBX1E 4 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__un_…
8 OTU_8 JLBX1E 27 JL BX 1 E JLE k__Bacteria p__Actinob… c__A… o__M… f__Mi… g__M… s__un_…
9 OTU_9 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
10 OTU_10 JLBX1E 2 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Ox… g__C… s__Col…
# ℹ 545,452 more rows
# ℹ Use `print(n = ...)` to see more rows
> mp_filter_taxa(mp_raw, .abundance = Abundance, min.abun = 2, min.prop = 0.1, iclude.lowest=FALSE)
# A MPSE-tibble (MPSE object) abstraction: 545,462 × 15
# OTU=4471 | Samples=122 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class, Order, Family, Genus, Species
OTU Sample Abundance origin suborigin bioreptype biotype oritype Kingdom Phylum Class Order Family Genus Species
<chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 OTU_1 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__E… f__Ye… g__S… s__Ser…
2 OTU_2 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__Par…
3 OTU_3 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
4 OTU_4 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__S… f__Sp… g__S… s__un_…
5 OTU_5 JLBX1E 11 JL BX 1 E JLE k__Bacteria p__Bactero… c__B… o__F… f__We… g__C… s__Chr…
6 OTU_6 JLBX1E 0 JL BX 1 E JLE k__Bacteria p__Proteob… c__A… o__R… f__Rh… g__P… s__Phy…
7 OTU_7 JLBX1E 4 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Bu… g__B… s__un_…
8 OTU_8 JLBX1E 27 JL BX 1 E JLE k__Bacteria p__Actinob… c__A… o__M… f__Mi… g__M… s__un_…
9 OTU_9 JLBX1E 1 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__X… f__Rh… g__u… s__un_…
10 OTU_10 JLBX1E 2 JL BX 1 E JLE k__Bacteria p__Proteob… c__G… o__B… f__Ox… g__C… s__Col…
# ℹ 545,452 more rows
# ℹ Use `print(n = ...)` to see more rows
You can try a larger min.abun value and include.lowest=T (meaning >= min.abun) or include.lowest=F (meaning >min.abun), and you can extract the specified assay to make statistics.
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>`(1) %>% table()
.
FALSE TRUE
2521 1621
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>=`(1) %>% table()
.
FALSE TRUE
2521 1621
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>`(2) %>% table()
.
FALSE TRUE
2535 1607
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>=`(2) %>% table()
.
FALSE TRUE
2521 1621
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>`(3) %>% table()
.
FALSE TRUE
2570 1572
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>=`(3) %>% table()
.
FALSE TRUE
2535 1607
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>`(4) %>% table()
.
FALSE TRUE
2609 1533
> mouse.time.mpse %>% mp_extract_assays(.abundance=Abundance) %>% `>=`(4) %>% table()
.
FALSE TRUE
2570 1572