vmikk/metagmisc

Miscellaneous functions for metagenomic analysis.

metagmisc's Issues

Error in dimnames(x) when trying to normalize OTU table in a phyloseq object

Hello,
I am trying to run a cumulative sum scaling normalization on a phyloseq object and got an error:
sr4css <- phyloseq_transform_css(sr4, norm = TRUE, log = TRUE) # Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent
I am not quite sure what this means, but was able to successfully run this transformation on the Global Patterns phyloseq object:
gp.css <- phyloseq_transform_css(GlobalPatterns, norm = TRUE, log = TRUE)
I couldn't find any obvious differences between the layout of these objects except that my object had taxa arranged as columns. I transformed this matrix and created a new object:
uto_table <- t(otu_table(sr4)) # transformed my otu table in case they needed to be rows sr4tOTU <- phyloseq(tax_table(tax_table(sr4)), sample_data(sample_data(sr4)), otu_table(uto_table), taxa_are_rows=TRUE)
sr4css <- phyloseq_transform_css(sr4tOTU, norm = TRUE, log = TRUE) This returned the same error. Same result when I omit the norm = TRUE, log = TRUE. Any suggestions?
I am running R 3.6.2 with phyloseq version 1.30.0, metagMisc 0.0.4, and metagenomeSeq 1.28.0.
Thanks,
Scott

Prevalence function missing from install

Hi vmikk,

loving the package!

I just wanted to make you aware that using devtools::install_github("vmikk/metagMisc") somehow does not install the prevalence() function. I am not sure if I am doing something wrong or if it is a problem.

Best

Lukas

error in function phyloseq_filter_prevalence

Hello,

I'm using the function phyloseq_filter_prevalence to filter a phyloseq object with R version 4.2.3 and it's working correctly with me, when I upgraded R version to v. 4.3.1 I got an error "OTU abundance data must have non-zero dimensions" with the same data,
R version was switched to the old version 4.2.3 to check again and it worked
the command:

physeq3 <- phyloseq_filter_prevalence(physeq.gen, prev.trh = 0.5, abund.trh = 10, threshold_condition = "OR")
Error in validObject(.Object) : invalid class “otu_table” object:
OTU abundance data must have non-zero dimensions.

thanks for your help in advance

Rewrite `get_max_taxonomic_rank` using `data.table`

phyloseq_mult_raref_avg: Perform rarefaction and average relative OTU abundance issue

Hi all

I've an issue with the phyloseq_mult_raref_avg function; it works on this phyloseq object.
phyloseq_summary(ps, cols = NULL, more_stats = FALSE,
+ long = FALSE)

Parameter Phys1

1 Number of samples 108.0000

2 Number of OTUs 7981.0000

3 Total number of reads 4781965.0000

4 Average number of reads per OTU 599.1687

5 Average number of reads per sample 44277.4537

works<-phyloseq_mult_raref_avg(ps,replace = T, SampSize = 10000, iter = 3)
..Multiple rarefaction
|=====================================================================================| 100%
..Sample renaming
..Rarefied data merging
..Splitting by sample
..OTU abundance averaging within rarefaction iterations
|=====================================================================================| 100%
..Re-create phyloseq object

But not this phyloseq object;

phyloseq_summary(p.b.a.m.s.lab, cols = NULL, more_stats = FALSE,
long = FALSE)

Parameter Phys1

1 Number of samples 36.0000

2 Number of OTUs 7981.0000

3 Total number of reads 4781965.0000

4 Average number of reads per OTU 599.1687

5 Average number of reads per sample 132832.3611

fails<-phyloseq_mult_raref_avg(p.b.a.m.s.lab,,replace = T, SampSize = 10000, iter = 3)
..Multiple rarefaction
|=====================================================================================| 100%
..Sample renaming
..Rarefied data merging
..Splitting by sample
Error in validObject(.Object) : invalid class “otu_table” object:
OTU abundance data must have non-zero dimensions.

validotu_table(otu_table(p.b.a.m.s.lab))
[1] TRUE
sum(is.na(otu_table(p.b.a.m.s.lab)))
[1] 0

I've psmelted it etc and all looks good no irregularities. Makes zero sense. phyloseq_mult_raref works on both...

Regards Cameron

Error in validObject(.Object) : invalid class “otu_table” object: OTU abundance data must have non-zero dimensions

Dear All,

I am trying to split the phyloseq after merged by genus.

I executed the following steps:

amgut_genus <- phyloseq::tax_glom(physeq, taxrank = "Genus")
taxtab <- amgut_genus@[email protected]

# Find undefined taxa (in this data set, unknowns occur only up to Family)
miss_f <- which(taxtab[, "Family"] == "f__")
miss_g <- which(taxtab[, "Genus"] == "g__")

# Number unspecified genera
taxtab[miss_f, "Family"] <- paste0("f__", 1:length(miss_f))
taxtab[miss_g, "Genus"] <- paste0("g__", 1:length(miss_g))

# Find duplicate genera
dupl_g <- which(duplicated(taxtab[, "Genus"]) |
                  duplicated(taxtab[, "Genus"], fromLast = TRUE))

for(i in seq_along(taxtab)){
  # The next higher non-missing rank is assigned to unspecified genera
  if(i %in% miss_f && i %in% miss_g){
    taxtab[i, "Genus"] <- paste0(taxtab[i, "Genus"], "(", taxtab[i, "Order"], ")")
  } else if(i %in% miss_g){
    taxtab[i, "Genus"] <- paste0(taxtab[i, "Genus"], "(", taxtab[i, "Family"], ")")
  }
  
  # Family names are added to duplicate genera
  if(i %in% dupl_g){
    taxtab[i, "Genus"] <- paste0(taxtab[i, "Genus"], "(", taxtab[i, "Family"], ")")
  }
}

amgut_genus@[email protected] <- taxtab
rownames(amgut_genus@[email protected]) <- taxtab[, "Genus"]

Then I tried to split the phyloseq data as follows:

amgut_split_genus <- metagMisc::phyloseq_sep_variable(amgut_genus, 
                                                "Intervention")

But got the error as:

Error in validObject(.Object) : invalid class “otu_table” object:
OTU abundance data must have non-zero dimensions.

Kindly help me

Rewrite `prevalence` function using `data.table`

The current implementation of the prevalence function utilizes base R functionalities for data manipulation and processing.
While this works fine for small datasets, it becomes significantly slower when handling larger, more complex datasets.
To improve the function's performance, it should be rewritten using the magnificent data.table package which offers fast and memory-efficient methods for data manipulation.

Related with #26

Error in if (class(dis) %in% "phylo") { : the condition has length > 1

My Code:

# Merge seq and tree
OTU <- otu_table(species_otu_table, taxa_are_rows = TRUE)

physeq <- phyloseq(OTU, otu_tree)

# Count
phy_SES <- phyloseq_phylo_ses(
  physeq,
  measures = c("PD", "MPD", "VPD"),
  null_model = "richness",
  package = "picante",
  abundance_weighted = FALSE,
  nsim = 1000,
  swapiter = 1000,
)

Error information:

SES analysis started at  10:06:44 
Error in if (class(dis) %in% "phylo") { : the condition has length > 1

I can used this function to calculate "PD" and "MPD", But not "VPD" parameter.

> phy_SES <- phyloseq_phylo_ses(
+   physeq,
+   measures = c("PD", "MPD"),
+   null_model = "richness",
+   package = "picante",
+   abundance_weighted = FALSE,
+   nsim = 1000,
+   swapiter = 1000,
+ )
SES analysis started at  10:12:25 
..Randomizing data with 'richness' algorithm
  |===================================================================================================| 100%
..Estimating phylogenetic diversity for the randomized data
  |===================================================================================================| 100%
..Estimating effect size
..Done
Analysis finished at  10:12:37

I guess the "VPD" calculate got wrong.

Show "OTU" content:

> OTU
OTU Table:          [17 taxa and 32 samples]
                     taxa are rows
                              LYG1  LYG2 LYG3  LYG4 LYG5  LYG6 LYG7 LYG8  LYG9 LYG10 LYG11 LYG12 LYG13 LYG14
Amblychaeturichthys_hexanema     0     0    0     0    0     0    0    0     0     0     0     0     0     0
Chaeturichthys_stigmatias     7509 13980 5698 36438 2463 31835 2985 4479  8514 11374 13725  3886 43904 44243
Coilia_nasus                     0     0    0     0    0     0    0    0     0    31     0    33     0     0
Collichthys_lucidus           1339   119   70    71  189  3421 2064 3032  1273  6879  2141  2081     0     0
Ctenotrypauchen_chinensis        0     0    0     0    0     0    0    0     0     0     0     0     0     0
Cynoglossus_joyneri          10944   442 3136  5513 4444  4426 4067 1640  1909  2166  5682  1345  5810  4196
Engraulis_japonicus           2882   274  201 19840  530   921 5837 8303 18928  6489    28   502    21   402
Hexagrammos_otakii              69    12    0    12   37   718    7  453  1152    38    14  2264  1089  1808
Konosirus_punctatus             40     0    0    15    0     0    0    0     0     0    18  1280  3551    19
Larimichthys_polyactis        1002     0    0     0  704    53   12   15    13    14    27  1198     0     0
Liparis_tanakae               1026   105    0     0   52   577    0    0  1304  9149  1754     0   275  1161
Lophius_litulon                901  7363 1295   132   45   803   33 1578   222    62     8  1222  1402   823
Odontamblyopus_lacepedii         0     0    0     0    0     0    0    0     0     0     0     0    53   508
Pholis_fangi                   971    35 1865    68 3233   235 6094 2609 12732   188  1050  2014   747   387
Scomber_japonicus                0     0    0     0    0     0    0    0     0     0     0     0     0     0
Setipinna_taty                 106     0   16    53   63    14  767 1903  1574    23  3796    25     8     0
Thryssa_kammalensis            603 11153  880    41 1014  2611 4213   24  2874  3058  3038  4066    22  2424
                             LYG15 LYG16  ZH1  ZH2 ZH3  ZH4  ZH5  ZH6  ZH7  ZH8  ZH9  ZH12 ZH13 ZH15 ZH16
Amblychaeturichthys_hexanema    47    28    0    0   0    0    0    0    0    0    0     0    0    0    0
Chaeturichthys_stigmatias    32321 27175 1160    9   8    0    0    0    0 2529    0    36    0  256    0
Coilia_nasus                     0     0    0    0   0    0    0    0    0    0 1853   246    7    0  105
Collichthys_lucidus             58    40    0    0   0    0    0    0    9    0    0     0  540  587    9
Ctenotrypauchen_chinensis        0     0    0    0   0    0 1148    0    0  864    0    14    0  270 1479
Cynoglossus_joyneri           5682  5531    0    0   0    0    0    0    0    0    0     0    0    0    0
Engraulis_japonicus           2395  3425 6344 4096 949 2462  187 2594 2363 7442 4109   269 2084  448  128
Hexagrammos_otakii               7    70    0    0   0    0    0    0    0    0    0     0    0    0    0
Konosirus_punctatus            923  4595    0    0   0    0    0    0    0    0    0     0    0    0    0
Larimichthys_polyactis           0    19    0    0   0    0    0    0    0    0    0     0    0    0    0
Liparis_tanakae                 44     0  662   67  10   24   13    0 6061 1400 1229 11768   20  465    0
Lophius_litulon                 11    48    0    0   0    0    0    0    0    0    0     0    0    0    0
Odontamblyopus_lacepedii       568    19    0    0   0    0    0    0    0    0    0     0    0    0    0
Pholis_fangi                  3256   101    0    0   0    0    0    0  351    0    0     0    0    8    0
Scomber_japonicus                0     0    0    0   0 3093    0   13    0    0    0     0    0    0    0
Setipinna_taty                 828  1910    0    0   0    0    0    0    0    0    0     0    0    0    0
Thryssa_kammalensis           4896   152    0    0   0    0    0    0    0    0    0     0    0    0    0
                              ZH17  ZH18 ZH19
Amblychaeturichthys_hexanema     0     0    0
Chaeturichthys_stigmatias        6     0   21
Coilia_nasus                    24    12    0
Collichthys_lucidus              0     0    7
Ctenotrypauchen_chinensis       14    12    0
Cynoglossus_joyneri              0     0    0
Engraulis_japonicus          16132 10753 7846
Hexagrammos_otakii               0     0    0
Konosirus_punctatus              0     0    0
Larimichthys_polyactis           0     0    0
Liparis_tanakae                 26  1209 2092
Lophius_litulon                  0     0    0
Odontamblyopus_lacepedii         0     0    0
Pholis_fangi                     0     0 1956
Scomber_japonicus                0     0    0
Setipinna_taty                   0     0    0
Thryssa_kammalensis              0     0    0

Show "otu_tree" content:

> otu_tree

Phylogenetic tree with 17 tips and 15 internal nodes.

Tip labels:
  Amblychaeturichthys_hexanema, Chaeturichthys_stigmatias, Ctenotrypauchen_chinensis, Odontamblyopus_lacepedii, Hexagrammos_otakii, Pholis_fangi, ...

Unrooted; includes branch lengths.

How can I fix this error?
Many Thanks!

[adonis_pairwise function] Deprecation notice: Replace vegan::adonis with vegan::adonis2

Dear @vmikk,

I suggest to substitute vegan::adonis with vegan::adonis2 because:

• According to the vegan manual, "adonis2 replaces adonis with extended functionality and completely new internal design"
• vegan::adonis is now deprecated and could be retired entirely in the next vegan version.

I can prepare a PR if needed.

Thanks.

Average rarified OTU/ASV table after multiple rarefaction

Hi @vmikk. I am finding this package very useful. I am currently trying to perform multiple rarefaction using phyloseq_mult_raref function. What I need to achieve is an average rarified table of all the iterations. I have used the other function i.e. phyloseq_mult_raref_avg, however the output is a relative abundance matrix. I would really appreciate it if you could help me out in obtaining an averaged rarified table with absolute abundance, instead of relative abundance.

Thanks!

Logo proposal

I want to design a logo for the readme. What dou you say?

Parallelize plyr-dependent functions

It should be quite easy to parallelize plyr-dependent functions to speed up processing of data.

Functions that could be modified to accept parallel option:

• phyloseq_mult_raref
• phyloseq_sep_tax
• parse_silva_tax_batch
• parse_taxonomy_amptk_batch
• parse_amplicon_table - just add ... as argument to silva_tax_parse_batch

shared otus function problem

Hi,

I am trying to see the shared OTUs from my phyloseq object.

I am running the function:

shared <- phyloseq_extract_shared_otus(physeqr, samp_names = sample_names(physeqr))
And then I obtain the following error:

Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘prune_samples’ for signature ‘"array", "phyloseq"’

Any idea what is wrong?

Cheers,
Pablo

`dissimilarity_to_distance` - error if number of OTUs > number of samples

It seems that dissimilarity_to_distance function (which transforms non-metric dissimilarity matrix to a weighted Euclidean distance) works only with data where number of samples > number of species.
Otherwise, smacof::smacofConstraint returns error "For C diagonal the number of dimensions needs to match the number of covariates!".

This function is based on code from Greenacre 2017 (DOI:10.1002/ecy.1937)

phyloseq_to_df error: Sample names

Hello!

I had performed several analysis using phyloseq_to_df without problem, but now I am having an error:
"Error: Sample names in 'physeq' could not be automatically converted to the syntactically valid column names in data.frame (see 'make.names'). Consider renaming with 'sample_names'.

The sample names appear to have no ivalid character (they are just numbers).

sample_names(ps)
[1] "1" "10" "11" "13" "14" "15" "2" "20" "27" "28" "3" "5" "7" "8" "9"

Any suggestions?
Thank you so much!

phyloseq_inext -- error in rbind

I believe dependencies in phyloseq_inext may be compromised. The function was working for me beautifully maybe a week ago, and now the example code associated with this function is no longer working.

R.Version() = 4.2.2 (2022-10-31)
packageVersion("phyloseq") = ‘1.42.0’
packageVersion("metagMisc") = ‘0.5.0’

See the reproducible example below:

library(phyloseq)
library(metagMisc)
data("esophagus")
phyloseq_inext(esophagus)
Error in rbind(deparse.level, ...): numbers of columns of arguments do not match
phyloseq_inext(esophagus, curve_type = "coverage")
Error in rbind(deparse.level, ...): numbers of columns of arguments do not match
phyloseq_inext(esophagus, justDF = T)
Error in rbind(deparse.level, ...): numbers of columns of arguments do not match

phyloseq_replace_zero and phyloseq_transform_aldex_clr

Hello, I can see documentation for the functions phyloseq_replace_zero and phyloseq_transform_aldex_clr, however these are not available in the R package. Is it that they don't work?
Thanks a lot!
https://rdrr.io/github/vmikk/metagMisc/src/R/phyloseq_transform.R#sym-phyloseq_transform_aldex_clr

phyloseq_filter_prevalence, non zero dimension

I'm using the function phyloseq_filter_prevalence to filter a phyloseq data.
I got an error OTU abundance data must have non-zero dimensions...

df<-phyloseq_filter_prevalence(physeq = myphylo, prev.trh = 0.3, abund.trh = NULL)
Error in validObject(.Object) : 잘못된 클래스 “otu_table” 객체입니다:
OTU abundance data must have non-zero dimensions.

Could you please provide me with a solution to my problem? Thank you.

phyloseq_inext: merge fail

this error:
Errore in sort.list(bx[m$xi]) :
'x' dev'essere atomico per 'sort.list', metodo "shell" e "quick"
Si è chiamato 'sort' su una lista?
In aggiunta: Messaggi di avvertimento:
1: : ... may be used in an incorrect context: ‘.fun(piece, ...)’

2: : ... may be used in an incorrect context: ‘.fun(piece, ...)’

sorry for the Italian in the error, but so it is.
we try to run part of your function and the error born from: res <- merge(res, mtd, by = "SampleID")

I try to reinstall your package, R, downgrade the R version, change the environment, and so on, but nothing changed.
I build the phyloseq file with qiime2R package.

Efficient rarefaction

Need to implement efficient rarefaction function based on RTK.

Error in validObject(.Object) : invalid class “otu_table” object: OTU abundance data must have non-zero dimensions.

Hi Vladimir,

I am trying to use your two functions to extract shared or non-shared otus, but I am getting an error about OTU abundance having non-zero dimensions.

To make my phyloseq object, I did the following:

taxa_tab <- tax_table(tax_tab)
samp_dat <- sample_data(metadata)
otu_tab_norm <- otu_table(otu_tab_10knorm, taxa_are_rows = T)
ps <- phyloseq(otu_tab_norm, samp_dat, taxa_tab)

Then I ran your shared otu function and received the error:

shared_esvs<-phyloseq_extract_shared_otus(ps)

"Error in validObject(.Object) : invalid class “otu_table” object:
OTU abundance data must have non-zero dimensions."

I also double checked that the OTU table did not have NAs and double checked my OTU table has reads:

 table(is.na(otu_table(ps)))

   FALSE 
43955446

sum(colSums(otu_table(ps)))
[1] 4916230

I have used this phyloseq object for my other analyses with no problem, so I don't think it has to do with manipulation as an issue #19. Any thoughts would be helpful, thank you!

Jordan

how to install 0.0.4 version

i got a problem using prevalence
how to install 0.0.4 version?
i try

devtools::install_version("metagMisc", verstion = 0.0.4)
Error: unexpected numeric constant in "devtools::install_version("metagMisc", verstion = 0.0.4"

and

devtools::install_version("metagMisc", 0.0.4)
Error: unexpected numeric constant in "devtools::install_version("metagMisc", 0.0.4"

remotes::install_version("metagMisc", version = 0.0.4)
Error: unexpected numeric constant in "remotes::install_version("metagMisc", version = 0.0.4"

remotes::install_github("metagMisc", 0.0.4)
Error: unexpected numeric constant in "remotes::install_github("metagMisc", 0.0.4"

devtools::install_github("metagMisc",0.0.4)
Error: unexpected numeric constant in "devtools::install_github("metagMisc",0.0.4"

please any suggestion

Tests needed

Currently, no tests other than examples in the manual are part of the package.
See testthat package (+test workflow example).

information about phyloseq_mult_raref

I was planning to do multiple rarefactions, but I am unable to get the details about why rarefaction will be made for the depth equal to 0.9 * minimal observed sample size?

What is the significance behind this?

Purrr vs Plyr

Should we switch from plyr to purrr?
Check also multidplyr (a backend for dplyr) to parallelize the code.

• see the comprehensive tutorial on this topic by Jenny Bryan and plyrToDplyr side-by-side examples.

Problem installing vmikk/metagMisc

Hi there,

I am trying to install vmikk/metagMisc as follows:

devtools::install_github("vmikk/metagMisc")

However, I get the following error:

Downloading GitHub repo vmikk/metagMisc@master
Skipping 1 packages not available: phyloseq
✓ checking for file ‘/private/var/folders/st/y2p98lbd0y58731wvyg2rgpc0000gp/T/Rtmp2soaSi/remotese111f52b72d/vmikk-metagMisc-d25b92f/DESCRIPTION’ (338ms)
─ preparing ‘metagMisc’:
✓ checking DESCRIPTION meta-information ...
─ checking for LF line-endings in source and make files and shell scripts
─ checking for empty or unneeded directories
─ building ‘metagMisc_0.0.4.tar.gz’

• installing source package ‘metagMisc’ ...
  ** using staged installation
  ** R
  ** byte-compile and prepare package for lazy loading
  Error in phy_tree(physeq, errorIfNULL = F) :
  could not find function "phy_tree"
  Error: unable to load R code in package ‘metagMisc’
  Execution halted
  ERROR: lazy loading failed for package ‘metagMisc’
• removing ‘/Library/Frameworks/R.framework/Versions/3.6/Resources/library/metagMisc’
  Error: Failed to install 'metagMisc' from GitHub:
  (converted from warning) installation of package ‘/var/folders/st/y2p98lbd0y58731wvyg2rgpc0000gp/T//Rtmp2soaSi/filee116f77fb6b/metagMisc_0.0.4.tar.gz’ had non-zero exit status

Any help will be appreciated

---

Session information:
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] remotes_2.1.1 phyloseq_1.30.0

loaded via a namespace (and not attached):
[1] Rcpp_1.0.4 ape_5.3 lattice_0.20-38
[4] prettyunits_1.1.1 ps_1.3.2 Biostrings_2.54.0
[7] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25
[10] foreach_1.4.8 R6_2.4.1 plyr_1.8.6
[13] backports_1.1.5 stats4_3.6.2 ggplot2_3.3.0
[16] pillar_1.4.3 zlibbioc_1.32.0 rlang_0.4.5
[19] curl_4.3 rstudioapi_0.11 data.table_1.12.8
[22] vegan_2.5-6 callr_3.4.2 S4Vectors_0.24.3
[25] Matrix_1.2-18 desc_1.2.0 devtools_2.2.2
[28] splines_3.6.2 stringr_1.4.0 igraph_1.2.4.2
[31] munsell_0.5.0 compiler_3.6.2 pkgconfig_2.0.3
[34] BiocGenerics_0.32.0 multtest_2.42.0 pkgbuild_1.0.6
[37] mgcv_1.8-31 biomformat_1.14.0 tidyselect_1.0.0
[40] tibble_2.1.3 IRanges_2.20.2 codetools_0.2-16
[43] fansi_0.4.1 permute_0.9-5 crayon_1.3.4
[46] dplyr_0.8.5 withr_2.1.2 MASS_7.3-51.4
[49] grid_3.6.2 nlme_3.1-142 jsonlite_1.6.1
[52] gtable_0.3.0 lifecycle_0.2.0 magrittr_1.5
[55] scales_1.1.0 cli_2.0.2 stringi_1.4.6
[58] XVector_0.26.0 reshape2_1.4.3 fs_1.3.1
[61] testthat_2.3.2 ellipsis_0.3.0 Rhdf5lib_1.8.0
[64] iterators_1.0.12 tools_3.6.2 ade4_1.7-13
[67] Biobase_2.46.0 glue_1.3.2 purrr_0.3.3
[70] pkgload_1.0.2 processx_3.4.2 parallel_3.6.2
[73] survival_3.1-8 colorspace_1.4-1 rhdf5_2.30.1
[76] cluster_2.1.0 sessioninfo_1.1.1 memoise_1.1.0
[79] usethis_1.5.1

Implement robust alternative to SES

• Implement Knijnenburg et al. 2009 approach (as a robust alternative to SES) in phylogenetic diversity estimation.

See:

• Botta-Dukát Z. 2018: https://akademiai.com/doi/abs/10.1556/168.2018.19.1.8
• Knijnenburg et al. 2009: https://doi.org/10.1093/bioinformatics/btp211

Prevalence plots (total OTU abundance vs OTU prevalence)

Hi, could you share how you created the Prevalence plots (total OTU abundance vs OTU prevalence)?
Thanks!

phyloseq_filter_prevalence error

Runnung the command test <-metagMisc::phyloseq_filter_prevalence(ASVnoSingletons, prev.trh = 0.10, abund.trh = NULL)

looking to filter the OTUs that appear in >10% I receive this error
Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent

What it can be>