summary_metabarlist is a generic function used to produce summary statistics of a metabarlist object.

summary_metabarlist(metabarlist, method = "dataset", groups = NULL)

Arguments

metabarlist

a metabarlist object

method

type of summary to provide. Should match with `dataset`, `motus` or `pcrs`. Default is `dataset`

groups

a grouping vector or factor of same number of rows than `motus` or `pcrs` for which the summary should be done. Default is NULL

Value

The summary_metabarlist function returns basic summary statistics (nb of elements, reads and MOTUs in total or on average per pcrs) of a metabarlist object. The format of the value returned depends on the method used.

Details

summary_metabarlist returns basic summary statistics of a metabarlist object. The summary returned depends on the `method` used :

  • `dataset`returns a list of two data.frames: dataset_dimension contains the dimensions of the full metabarlist object, dataset_statistics contains the number of reads, motus in pcrs and samples, as well as average and sd values of reads and motus per sample

  • `motus` or `pcrs`returns a data.frame similar to the dataset_statistics described above according to a grouping factor/vector for MOTUs or pcrs

See also

Author

Lucie Zinger

Examples


data(soil_euk)

## Dataset summary
summary_metabarlist(soil_euk, method = "dataset")
#> $dataset_dimension
#>         n_row n_col
#> reads     384 12647
#> motus   12647    15
#> pcrs      384    11
#> samples    64     8
#> 
#> $dataset_statistics
#>         nb_reads nb_motus avg_reads sd_reads avg_motus sd_motus
#> pcrs     3538913    12647  9215.919 10283.45  333.6849  295.440
#> samples  2797294    12382 10926.930 10346.66  489.5117  239.685
#> 

## Data summary per control type (NA = samples)
summary_metabarlist(soil_euk, method = "pcrs",
    groups = soil_euk$pcrs$control_type)
#>            nb_elements nb_reads nb_motus  avg_reads  sd_reads avg_motus
#> NA                 256  2797294   125315 10926.9297 10346.656 489.51172
#> extraction          16    73393      200  4587.0625  5738.397  12.50000
#> pcr                 32   323516      323 10109.8750 12491.037  10.09375
#> positive            32   342205     1772 10693.9062  9250.060  55.37500
#> sequencing          48     2505      525    52.1875   191.229  10.93750
#>             sd_motus
#> NA         239.68502
#> extraction  10.93008
#> pcr         10.56580
#> positive    24.44711
#> sequencing  35.95411

## Data summary per phyla
summary_metabarlist(soil_euk, method = "motus",
    groups = soil_euk$motus$phylum_name)
#>                    nb_reads nb_motus  avg_reads     sd_reads avg_motus
#> Annelida             247553      338  732.40533  6595.131372 12.147929
#> Apicomplexa            1587       41   38.70732    77.264883  4.878049
#> Arthropoda           474922     1566  303.27075  2297.171727 12.102810
#> Ascomycota           205964      813  253.33825  1559.692496 14.842558
#> Bacillariophyta        9364        5 1872.80000  4179.882678  2.200000
#> Basidiomycota        796471      790 1008.19114 11283.635740 14.975949
#> Blastocladiomycota        8        1    8.00000           NA  2.000000
#> Bryozoa                 354        2  177.00000   244.658946 10.500000
#> Chlorophyta             262       13   20.15385    57.113697  3.923077
#> Chordata                747       13   57.46154   162.241905  2.307692
#> Chytridiomycota       14692      126  116.60317   463.843371  9.658730
#> Cnidaria                  9        1    9.00000           NA  4.000000
#> Euglenida                 3        1    3.00000           NA  2.000000
#> Euryarchaeota             4        2    2.00000     0.000000  1.000000
#> Eustigmatophyceae        17        1   17.00000           NA  1.000000
#> Firmicutes                3        1    3.00000           NA  1.000000
#> Gastrotricha          11810       31  380.96774  1057.149579 23.032258
#> Mollusca               3254        4  813.50000   653.168942 47.750000
#> Mucoromycota          24852      121  205.38843  1280.439816 15.966942
#> NA                  1425205     7825  182.13482  3882.495526  8.630543
#> Nematoda              40080      430   93.20930   774.259305  8.195349
#> Pinguiophyceae            6        1    6.00000           NA  1.000000
#> Platyhelminthes       21358      143  149.35664   630.796248 10.055944
#> Porifera                947       20   47.35000    71.198961  5.000000
#> Proteobacteria           12        4    3.00000     1.414214  1.000000
#> Rotifera               6919       63  109.82540   343.048315 16.047619
#> Streptophyta         249436      199 1253.44724  9105.991740 13.402010
#> Tardigrada              656       12   54.66667   112.436918 11.583333
#> Zoopagomycota          2418       80   30.22500    99.602244  4.762500
#>                     sd_motus
#> Annelida           30.444706
#> Apicomplexa         8.316234
#> Arthropoda         26.520213
#> Ascomycota         30.928088
#> Bacillariophyta     2.167948
#> Basidiomycota      32.861744
#> Blastocladiomycota        NA
#> Bryozoa            12.020815
#> Chlorophyta         7.931599
#> Chordata            2.323238
#> Chytridiomycota    14.639761
#> Cnidaria                  NA
#> Euglenida                 NA
#> Euryarchaeota       0.000000
#> Eustigmatophyceae         NA
#> Firmicutes                NA
#> Gastrotricha       43.688659
#> Mollusca           47.373516
#> Mucoromycota       34.475821
#> NA                 20.233437
#> Nematoda           20.349258
#> Pinguiophyceae            NA
#> Platyhelminthes    18.950185
#> Porifera            6.198472
#> Proteobacteria      0.000000
#> Rotifera           30.022265
#> Streptophyta       27.076196
#> Tardigrada         18.617969
#> Zoopagomycota      10.607191

## Data summary per Habitat (i.e. to get from soil_euk$samples).
# Here, NA values correspond to technical controls
summary_metabarlist(soil_euk, method = "pcrs",
    groups = soil_euk$samples$Habitat[match(soil_euk$pcrs$sample_id,
                                            rownames(soil_euk$samples))])
#>             nb_elements nb_reads nb_motus avg_reads  sd_reads avg_motus
#> NA                  128   741619     2820  5793.898  9287.649  22.03125
#> Terra firme         128  1410057    69372 11016.070 11067.993 541.96875
#> White sands         128  1387237    55943 10837.789  9614.209 437.05469
#>              sd_motus
#> NA           32.24364
#> Terra firme 244.95318
#> White sands 223.16204