Filter samples
Phyloseq package
already propose a function to select samples
(subset_samples()), but in some case, the subset internal
function is painful. MiscMetabar propose a complementary
function ([subset_samples_pq()]) which is more versatile but need to be
used with caution because the order of the condition must match the
orders of the samples.
data(data_fungi)
cond_samp <- grepl("A1", data_fungi@sam_data[["Sample_names"]])
subset_samples_pq(data_fungi, cond_samp)
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 1420 taxa and 9 samples ]
#> sample_data() Sample Data: [ 9 samples by 7 sample variables ]
#> tax_table() Taxonomy Table: [ 1420 taxa by 12 taxonomic ranks ]
#> refseq() DNAStringSet: [ 1420 reference sequences ]Filter Taxa
Filter taxa using condition(s)
Phyloseq package
already propose a function to select samples
(subset_taxa()), but in some case, the subset internal
function is painful. MiscMetabar propose a complementary
function ([subset_taxa_pq()]) which is more versatile and is based on
the names of the taxa to match the condition and the taxa in the
phyloseq object. In the example code above, we filter fungi from the
Phylum “Basidiomycota” using phyloseq::subset_taxa() and
then select only taxa with more than 1000 nb_sequences using
subset_taxa_pq().
df_basidio <- subset_taxa(data_fungi, Phylum == "Basidiomycota")
df_basidio_abundant <- subset_taxa_pq(
df_basidio,
colSums(df_basidio@otu_table) > 1000
)Filter taxa using blast score against a database
In some cases, we want to select only a given clade of taxon. One
solution is to select only taxa with information given by taxonomic
assignment (e.g. using the function
dada2::assignTaxonomy()). However, in some cases, this
information lead to false positive and false negative selection.
MiscMetabar implement an other method relying on blast software (Altschul
et al. 1990 & 1997) and database. The idea is to set a
cutoff in four parameters to select only taxa which are close enough to
sequences in the database :
- id_filter (default: 90) cut of in identity percent to keep result.
- bit_score_filter (default: 50) cut of in bit score to keep result. The higher the bit-score, the better the sequence similarity. The bit-score is the requires size of a sequence database in which the current match could be found just by chance. The bit-score is a log2 scaled and normalized raw-score. Each increase by one doubles the required database size (2bit-score).
- min_cover_filter (default: 50) cut of in query cover (%) to keep result.
- e_value_filter (default: 1e-30) cut of in e-value (%) to keep result. The BLAST E-value is the number of expected hits of similar quality (score) that could be found just by chance.
path_db <- system.file("extdata",
"100_sp_UNITE_sh_general_release_dynamic.fasta",
package = "MiscMetabar", mustWork = TRUE
)
suppressWarnings(blast_error_or_not <-
try(system("blastn 2>&1", intern = TRUE), silent = TRUE))
if (!is(blast_error_or_not, "try-error")) {
df_blast_80 <- filter_asv_blast(df_basidio, fasta_for_db = path_db)
df_blast_50 <- filter_asv_blast(df_basidio,
fasta_for_db = path_db,
id_filter = 50, e_value_filter = 10,
bit_score_filter = 20, min_cover_filter = 20
)
track_formattable <-
track_wkflow(
list(
"raw data" = df_basidio,
"id_filter = 80" = df_blast_80,
"id_filter = 50" = df_blast_50
)
)
}
if (!is(blast_error_or_not, "try-error")) {
formattable(
track_formattable,
list(
area(col = nb_sequences) ~ color_bar("cyan", na.rm = TRUE),
area(col = nb_clusters) ~ normalize_bar("yellowgreen",
na.rm = TRUE, min = 0.3
),
area(col = nb_samples) ~ normalize_bar("lightpink",
na.rm = TRUE, min = 0.3
)
)
)
}| nb_sequences | nb_clusters | nb_samples | |
|---|---|---|---|
| raw data | 784054 | 345 | 185 |
| id_filter = 80 | 249494 | 45 | 127 |
| id_filter = 50 | 780242 | 326 | 172 |
Filter taxa using a known taxa for control
To filter out contamination, one solution is to add a proportion of a known taxa which is not present in the environment of the study. In that case we can define some threshold for each sample to discard taxon based on pseudo-abundance. In the example code above, we select taxon using the ASV_50 as control through 6 different algorithms.
res_seq <-
suppressWarnings(
subset_taxa_tax_control(data_fungi, as.numeric(data_fungi@otu_table[, 50]),
method = "cutoff_seq"
)
)
res_mixt <-
suppressWarnings(
subset_taxa_tax_control(data_fungi, as.numeric(data_fungi@otu_table[, 50]),
method = "cutoff_mixt"
)
)
res_diff <- suppressWarnings(
subset_taxa_tax_control(
data_fungi,
as.numeric(data_fungi@otu_table[, 50]),
method = "cutoff_diff",
min_diff_for_cutoff = 2
)
)
res_min <-
suppressWarnings(
subset_taxa_tax_control(
data_fungi,
as.numeric(data_fungi@otu_table[, 50]),
method = "min",
min_diff_for_cutoff = 2
)
)
res_max <-
suppressWarnings(
subset_taxa_tax_control(
data_fungi,
as.numeric(data_fungi@otu_table[, 50]),
method = "max",
min_diff_for_cutoff = 2
)
)
res_mean <-
suppressWarnings(
subset_taxa_tax_control(
data_fungi,
as.numeric(data_fungi@otu_table[, 50]),
method = "mean",
min_diff_for_cutoff = 2
)
)
track_formattable <- track_wkflow(list(
"raw data" = data_fungi,
"cutoff_seq" = res_seq,
"cutoff_mixt" = res_mixt,
"cutoff_diff" = res_diff,
"min" = res_min,
"max" = res_max,
"mean" = res_mean
))
formattable(
track_formattable,
list(
area(col = nb_sequences) ~ color_bar("cyan"),
area(col = nb_clusters) ~ normalize_bar("yellowgreen",
na.rm = TRUE,
min = 0.3
),
area(col = nb_samples) ~ normalize_bar("lightpink", na.rm = TRUE)
)
)| nb_sequences | nb_clusters | nb_samples | |
|---|---|---|---|
| raw data | 1839124 | 1420 | 185 |
| cutoff_seq | 1817543 | 1402 | 185 |
| cutoff_mixt | 1771585 | 1281 | 185 |
| cutoff_diff | 1805274 | 1378 | 185 |
| min | 1835949 | 1417 | 185 |
| max | 1757372 | 1229 | 185 |
| mean | 1791685 | 1329 | 185 |
Session information
sessionInfo()
#> R version 4.3.3 (2024-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Debian GNU/Linux 11 (bullseye)
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.13.so; LAPACK version 3.9.0
#>
#> locale:
#> [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
#> [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
#> [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Europe/Paris
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] formattable_0.2.1 MiscMetabar_0.9.1 purrr_1.0.2 dplyr_1.1.4
#> [5] dada2_1.30.0 Rcpp_1.0.12 ggplot2_3.5.0 phyloseq_1.46.0
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-7 pbapply_1.7-2
#> [3] deldir_2.0-4 permute_0.9-7
#> [5] rlang_1.1.3 magrittr_2.0.3
#> [7] ade4_1.7-22 matrixStats_1.3.0
#> [9] compiler_4.3.3 mgcv_1.9-1
#> [11] png_0.1-8 systemfonts_1.0.6
#> [13] vctrs_0.6.5 reshape2_1.4.4
#> [15] stringr_1.5.1 pkgconfig_2.0.3
#> [17] crayon_1.5.2 fastmap_1.1.1
#> [19] XVector_0.42.0 utf8_1.2.4
#> [21] Rsamtools_2.18.0 rmarkdown_2.26
#> [23] ragg_1.3.0 xfun_0.43
#> [25] zlibbioc_1.48.2 cachem_1.0.8
#> [27] GenomeInfoDb_1.38.8 jsonlite_1.8.8
#> [29] biomformat_1.30.0 rhdf5filters_1.14.1
#> [31] DelayedArray_0.28.0 Rhdf5lib_1.24.2
#> [33] BiocParallel_1.36.0 jpeg_0.1-10
#> [35] parallel_4.3.3 cluster_2.1.6
#> [37] R6_2.5.1 bslib_0.7.0
#> [39] stringi_1.8.3 RColorBrewer_1.1-3
#> [41] GenomicRanges_1.54.1 jquerylib_0.1.4
#> [43] SummarizedExperiment_1.32.0 iterators_1.0.14
#> [45] knitr_1.46 mixtools_2.0.0
#> [47] IRanges_2.36.0 Matrix_1.6-5
#> [49] splines_4.3.3 igraph_2.0.3
#> [51] tidyselect_1.2.1 rstudioapi_0.16.0
#> [53] abind_1.4-5 yaml_2.3.8
#> [55] vegan_2.6-4 codetools_0.2-19
#> [57] hwriter_1.3.2.1 lattice_0.22-6
#> [59] tibble_3.2.1 plyr_1.8.9
#> [61] Biobase_2.62.0 withr_3.0.0
#> [63] ShortRead_1.60.0 evaluate_0.23
#> [65] desc_1.4.3 survival_3.5-8
#> [67] RcppParallel_5.1.7 kernlab_0.9-32
#> [69] Biostrings_2.70.3 pillar_1.9.0
#> [71] MatrixGenerics_1.14.0 foreach_1.5.2
#> [73] stats4_4.3.3 plotly_4.10.4
#> [75] generics_0.1.3 RCurl_1.98-1.14
#> [77] S4Vectors_0.40.2 munsell_0.5.1
#> [79] scales_1.3.0 glue_1.7.0
#> [81] lazyeval_0.2.2 tools_4.3.3
#> [83] interp_1.1-6 data.table_1.15.4
#> [85] GenomicAlignments_1.38.2 fs_1.6.3
#> [87] rhdf5_2.46.1 grid_4.3.3
#> [89] tidyr_1.3.1 ape_5.8
#> [91] latticeExtra_0.6-30 colorspace_2.1-0
#> [93] nlme_3.1-164 GenomeInfoDbData_1.2.11
#> [95] cli_3.6.2 textshaping_0.3.7
#> [97] fansi_1.0.6 segmented_2.0-3
#> [99] viridisLite_0.4.2 S4Arrays_1.2.1
#> [101] gtable_0.3.4 sass_0.4.9
#> [103] digest_0.6.35 BiocGenerics_0.48.1
#> [105] SparseArray_1.2.4 htmlwidgets_1.6.4
#> [107] memoise_2.0.1 htmltools_0.5.8.1
#> [109] pkgdown_2.0.7 multtest_2.58.0
#> [111] lifecycle_1.0.4 httr_1.4.7
#> [113] MASS_7.3-60.0.1