FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.41 fork

Version History

The Forsyth Institute, Cambridge, MA, USA
January 14, 2023

Project ID: FOMC0000


I. Project Summary

Project FOMC0000 services do not include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please download this report. These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.

Full Bioinformatics analysis service was requested.

We provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.

For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.

If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.

 

II. Workflow Checklist

1.Sample Received
2.Sample Quality Evaluated
3.Sample Prepared for Sequencing
4.Next-Gen Sequencing
5.Sequence Quality Check
6.Absolute Abundance
7.Report and Raw Sequence Data Available for Download
8.Bioinformatics Analysis - Reads Processing (DADA2 Quality Trimming, Denoising, Paired Reads Merging)
9.Bioinformatics Analysis - Reads Taxonomy Assignment
10.Bioinformatics Analysis - Alpha Diversity Analysis
11.Bioinformatics Analysis - Beta Diversity Analysis
12.Bioinformatics Analysis - Differential Abundance Analysis
13.Bioinformatics Analysis - Heatmap Profile
14.Bioinformatics Analysis - Network Association
 

III. NGS Sequencing

Not available
 

IV. Complete Report Download

The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.

Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.

Complete report download link:

To view the report, please follow the following steps:
1.Download the .zip file from the report link above.
2.Extract all the contents of the downloaded .zip file to your desktop.
3.Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the report, there are links to view all the analyses performed for the project.

 

V. Raw Sequence Data Download

Not available
 

VI. Analysis - DADA2 Read Processing

Not available
 

Sample Meta Information

#SampleIDGroupGroup1
H1HealthHealth_Control
H2HealthHealth_Control
H3HealthHealth_Control
H4HealthHealth_0.025_mM
H5HealthHealth_0.025_mM
H6HealthHealth_0.025_mM
H7HealthHealth_0.05_mM
H8HealthHealth_0.05_mM
H9HealthHealth_0.05_mM
H10HealthHealth_0.1_mM
H11HealthHealth_0.1_mM
H12HealthHealth_0.1_mM
H13HealthHealth_0.2_mM
H14HealthHealth_0.2_mM
H15HealthHealth_0.2_mM
H16HealthHealth_0.4_mM
H17HealthHealth_0.4_mM
H18HealthHealth_0.4_mM
H19HealthHealth_0.8_mM
H20HealthHealth_0.8_mM
H21HealthHealth_0.8_mM
H22HealthHealth_1.6_mM
H23HealthHealth_1.6_mM
H24HealthHealth_1.6_mM
P1PeriodontitisPeriodontitis_Control
P2PeriodontitisPeriodontitis_Control
P3PeriodontitisPeriodontitis_Control
P4PeriodontitisPeriodontitis_0.025_mM
P5PeriodontitisPeriodontitis_0.025_mM
P6PeriodontitisPeriodontitis_0.025_mM
P7PeriodontitisPeriodontitis_0.05_mM
P8PeriodontitisPeriodontitis_0.05_mM
P9PeriodontitisPeriodontitis_0.05_mM
P10PeriodontitisPeriodontitis_0.1_mM
P11PeriodontitisPeriodontitis_0.1_mM
P12PeriodontitisPeriodontitis_0.1_mM
P13PeriodontitisPeriodontitis_0.2_mM
P14PeriodontitisPeriodontitis_0.2_mM
P15PeriodontitisPeriodontitis_0.2_mM
P16PeriodontitisPeriodontitis_0.4_mM
P17PeriodontitisPeriodontitis_0.4_mM
P18PeriodontitisPeriodontitis_0.4_mM
P19PeriodontitisPeriodontitis_0.8_mM
P20PeriodontitisPeriodontitis_0.8_mM
P21PeriodontitisPeriodontitis_0.8_mM
P22PeriodontitisPeriodontitis_1.6_mM
P23PeriodontitisPeriodontitis_1.6_mM
P24PeriodontitisPeriodontitis_1.6_mM
 
 

ASV Read Counts by Samples

#Sample IDRead Count
H2485
P8644
H13799
H76,224
H156,381
H26,486
H146,770
P77,301
P127,414
P27,535
P177,652
P67,775
P47,868
H38,141
P18,306
P98,514
H188,783
P58,957
P39,139
P149,621
P109,846
P119,948
H810,469
H1211,306
H111,372
H911,675
P1511,725
H511,744
H1711,978
P1312,045
H412,305
H2012,983
H2113,202
H1113,819
H2314,624
P1815,700
H1915,950
H1016,368
H616,469
P2018,203
P2219,669
P1621,448
H2222,990
P2327,921
P2131,749
P1932,551
H1640,506
P2448,306
 
 
 

VII. Analysis - Read Taxonomy Assignment

Read Taxonomy Assignment - Methods

 

The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline

Version 20210310
 

1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences. It consists of MOMD (version 0.1), the HOMD (version 15.2 http://www.homd.org/index.php?name=seqDownload&file&type=R ), HOMD 16S rRNA RefSeq Extended Version 1.1 (EXT), GreenGene Gold (GG) (http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_data_files/gold_strains_gg16S_aligned.fasta.gz) , and the NCBI 16S rRNA reference sequence set (https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz). These sequences were screened and combined to remove short sequences (<1000nt), chimera, duplicated and sub-sequences, as well as sequences with poor taxonomy annotation (e.g., without species information). This process resulted in 1,015 from HOMD V15.22, 495 from EXT, 3,940 from GG and 18,044 from NCBI, a total of 25,120 sequences. Altogether these sequence represent a total of 15,601 oral and non-oral microbial species.

The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) was used with the default parameters. Reads with ≥ 98% sequence identity to the matched reference and ≥ 90% alignment length (i.e., ≥ 90% of the read length that was aligned to the reference and was used to calculate the sequence percent identity) were classified based on the taxonomy of the reference sequence with highest sequence identity. If a read matched with reference sequences representing more than one species with equal percent identity and alignment length, it was subject to chimera checking with USEARCH program version v8.1.1861 (Edgar 2010). Non-chimeric reads with multi-species best hits were considered valid and were assigned with a unique species notation (e.g., spp) denoting unresolvable multiple species.

2. Unassigned reads (i.e., reads with < 98% identity or < 90% alignment length) were pooled together and reads < 200 bases were removed. The remaining reads were subject to the de novo operational taxonomy unit (OTU) calling and chimera checking using the USEARCH program version v8.1.1861 (Edgar 2010). The de novo OTU calling and chimera checking was done using 98% as the sequence identity cutoff, i.e., the species-level OTU. The output of this step produced species-level de novo clustered OTUs with 98% identity. Representative reads from each of the OTUs/species were then BLASTN-searched against the same reference sequence set again to determine the closest species for these potential novel species. These potential novel species were pooled together with the reads that were signed to specie-level in the previous step, for down-stream analyses.

Reference:
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12. PubMed PMID: 20709691.

3. Designations used in the taxonomy:

	1) Taxonomy levels are indicated by these prefixes:
	
	   k__: domain/kingdom
	   p__: phylum
	   c__: class
	   o__: order
	   f__: family
	   g__: genus  
	   s__: species
	
	   Example: 
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__faecis
		
	2) Unique level identified – known species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__hominis
	
	   The above example shows some reads match to a single species (all levels are unique)
	
	3) Non-unique level identified – known species:

	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__multispecies_spp123_3
	   
	   The above example “s__multispecies_spp123_3” indicates certain reads equally match to 3 species of the 
	   genus Roseburia; the “spp123” is a temporally assigned species ID.
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__multigenus;s__multispecies_spp234_5
	   
	   The above example indicates certain reads match equally to 5 different species, which belong to multiple genera.; 
	   the “spp234” is a temporally assigned species ID.
	
	4) Unique level identified – unknown species, potential novel species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ hominis_nov_97%
	   
	   The above example indicates that some reads have no match to any of the reference sequences with 
	   sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. However this groups 
	   of reads (actually the representative read from a de novo  OTU) has 96% percent identity to 
	   Roseburia hominis, thus this is a potential novel species, closest to Roseburia hominis. 
	   (But they are not the same species).
	
	5) Multiple level identified – unknown species, potential novel species:
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ multispecies_sppn123_3_nov_96%
	
	   The above example indicates that some reads have no match to any of the reference sequences 
	   with sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. 
	   However this groups of reads (actually the representative read from a de novo  OTU) 
	   has 96% percent identity equally to 3 species in Roseburia. Thus this is no single 
	   closest species, instead this group of reads match equally to multiple species at 96%. 
	   Since they have passed chimera check so they represent a novel species. “sppn123” is a 
	   temporary ID for this potential novel species. 

 
4. The taxonomy assignment algorithm is illustrated in this flow char below:
 
 
 
 

Read Taxonomy Assignment - Result Summary *

CodeCategoryMPC=0% (>=1 read)MPC=0.01%(>=62 reads)
ATotal reads641,266641,266
BTotal assigned reads620,238620,238
CAssigned reads in species with read count < MPC02,896
DAssigned reads in samples with read count < 5008155
ETotal samples4848
FSamples with reads >= 5004747
GSamples with reads < 50011
HTotal assigned reads used for analysis (B-C-D)620,157617,287
IReads assigned to single species608,142606,455
JReads assigned to multiple species1,9821,807
KReads assigned to novel species10,0339,025
LTotal number of species40980
MNumber of single species23575
NNumber of multi-species143
ONumber of novel species1482
PTotal unassigned reads21,02821,028
QChimeric reads7676
RReads without BLASTN hits268268
SOthers: short, low quality, singletons, etc.20,68420,684
A=B+P=C+D+H+Q+R+S
E=F+G
B=C+D+H
H=I+J+K
L=M+N+O
P=Q+R+S
* MPC = Minimal percent (of all assigned reads) read count per species, species with read count < MPC were removed.
* Samples with reads < 500 were removed from downstream analyses.
* The assignment result from MPC=0.1% was used in the downstream analyses.
 
 
 

Read Taxonomy Assignment - ASV Species-Level Read Counts Table

This table shows the read counts for each sample (columns) and each species identified based on the ASV sequences. The downstream analyses were based on this table.
SPIDTaxonomyH1H10H11H12H13H14H15H16H17H18H19H2H20H21H22H23H24H3H4H5H6H7H8H9P1P10P11P12P13P14P15P16P17P18P19P2P20P21P22P23P24P3P4P5P6P7P8P9
SP1Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._polymorphum44633765397960804520832395165643497703011010403948533623834988323146095743448779032958292591258128883885120818502331174318061022043393852306327982193362811
SP10Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;periodonticum2682244800000000000010754593046000011643625148558192151200107000003671027873849222
SP103Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;gracilis00020001000000000000100023013232764418152330110101
SP11Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sinus8613400800551291731051701101283101956860034845628114820313413015687542731092663439126111618739527213212150
SP12Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sp._oral_taxon_32629592340000000280000010577151018000100000000000000000000
SP128Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;multiformis000000000000000000000000107208464000180000026424906
SP13Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;cristatus852503911217070036000004041170500207013422401007411071062702
SP131Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oralis000000000000000000000000010000081138010330000000000
SP138Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-2];sp._oral_taxon_091000020000000000000000000016410845100080000078104907
SP14Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Atopobium;sp._oral_taxon_1990000200000000001000100001855211942841991911449702494822302260213438099603494544019011621616016011162
SP145Bacteria;Bacteroidetes;Bacteroidetes_[C-1];Bacteroidetes_[O-1];Bacteroidetes_[F-1];Bacteroidetes_[G-5];sp._oral_taxon_511000000000000000001100000716106311210008000006442206
SP15Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;micra000120000000000000020800001893553871823762193084156431737131109152742151432241540377
SP16Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Slackia;exigua21611528140511401931095100000040214223120407355526441274231424362214252833099821237115117851838166110163777507394309431
SP17Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Catonella;morbi0000500000000000000000002272036714021112212281383002043120001894915984122148
SP18Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Atopobium;parvulum13463213010118091352760300062315619612066168363015152021701030141404031170101722216118
SP19Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Solobacterium;moorei4307251000236749200101300823419304392815748942201723413221632022121271475266127
SP2Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;concisus5408238190090666584352628913333113780350114111141421731134245792400284072996511270211439011334861098117010941656758937551061028895249137952199918215235484156822633055654041172525
SP20Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;tigurinus05222201252100301000001272019581416022088420435743272610212525632801048559101126720100
SP21Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oulorum91155720173000370000033716201261413105673471005000004359447
SP22Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidales_[F-2];Bacteroidales_[G-2];sp._oral_taxon_27401000000000000000000000012213541715910030031000008017392629523
SP23Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;melaninogenica860013315361000000000014461114001801010200103050000001010264
SP24Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Cryptobacterium;curtum000000015258600000000000000010000020000000000250000
SP25Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;salivae4729007031640200000011020198177368185541504000001166910
SP26Bacteria;Synergistetes;Synergistia;Synergistales;Synergistaceae;Fretibacterium;sp._oral_taxon_36000002000000100000000000010911635672361831279953116572293200092172147945126
SP27Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp._oral_taxon_3080201400501010000000110517200300001000000100000001000
SP28Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;leadbetteri00064000000000000000000009640010200003000003114103
SP29Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;adiacens31015309217720220100013216245513020000821000004010302
SP3Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacteriales;Enterobacteriaceae;Klebsiella;sp._Oral_Taxon_A36136461212531314018110171774888609649060811251916187000000000000000000000000
SP30Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;endodontalis020000000000000000000000141217414226411210130200296000003451312782602173103
SP31Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Mogibacterium;diversum58000110001100001001000000004034256726164549248380514703442521172021650240244945246525542
SP32Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Catonella;sp._oral_taxon_16400000000000000000000000032391628167122360011524003151021140122
SP33Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Eikenella;corrodens931142001412142524405311400015254367817333422552724738151004132200
SP34Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;dentisani200020010760601100006831212182414462316713292111818188182908018285921131882173
SP35Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;rava0000100000000000000000001148844373810100583801352615192520119
SP36Bacteria;Synergistetes;Synergistia;Synergistales;Synergistaceae;Fretibacterium;fastidiosum00004000000000020000000041141248689675767212517430100346614923316322
SP37Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;tannerae0000100000000100033500151130011225953651911501263511225010304020179125961041154143
SP38Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;sp._oral_taxon_1100090684000018000000000199004582017943229897239130233101306119111109973196
SP39Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis113191859232128531521631127000190174572437000000000000000000000000
SP4Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._HOT_2049481868131272540853937893244062200000716112860950771492588602601915842430019220002032634012051
SP40Bacteria;Bacteroidetes;Bacteroidetes_[C-1];Bacteroidetes_[O-1];Bacteroidetes_[F-1];Bacteroidetes_[G-3];sp._oral_taxon_280000000000000000000000000013015789055353170038000002914473522021
SP41Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._animalis212200000000054000001430228955730145734134531616040106114037000001196102461204123
SP42Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sputigena0040000000000000000107065000002000000000001030001
SP43Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;pallens1570483066000000000000181226702910000000012110000000000100
SP44Bacteria;Actinobacteria;Actinobacteria;Propionibacteriales;Propionibacteriaceae;Propionibacterium;acnes00013710001000000190001000000010000000000000000050
SP45Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._oral_taxon_2792222571944343410118123000004820178139283712103122518231216511301200000310162528121
SP46Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp._oral_taxon_317013340025050045000001210041303622734672951119029000003026174712153
SP47Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-9];[Eubacterium]_brachy0000000000000000000000000116521495915300710000011587011076148
SP48Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._oral_taxon_278670450000000200100499816881344200256641811021461202994571251550000019120109452006214
SP49Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Stomatobaculum;longum00000000000000000000000001700003609111264371524640200005
SP5Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._vincentii4000000000000000070501204638230015420111517665898121089780193020142428022823831912308
SP50Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._oral_taxon_20300000000000000000120144220336093430004000001923706
SP51Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-7];sp._oral_taxon_081000000000000000000000000531502211282565002006800000832310251251142
SP52Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_0560000000001020000000000004592124931513000004876901
SP53Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Burkholderia;cepacia00005600001000001310001000000000000000000000000080
SP55Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._oral_taxon_28422701410000000050000086314272205293527000010000000000000000000
SP56Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-7];[Eubacterium]_yurii_subsp._yurii_&_margaretiae0000000000000000000000000220181325251400120000021514610010
SP59Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_058029101111845820080010325000136075554002307051441005121005502309
SP6Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_423754511085122692941213816105701541261211270190042433258627275232596005100040102120001000200
SP60Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Atopobium;rimae0300000037701900010001160004481114565473011631112100691004
SP61Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sp._oral_taxon_078000000000800110000000000004831371113381101106321550201
SP63Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;gingivalis13292161112696720650000033944419171418111112111413165170111000017976116
SP66Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_070000000000000000000000000276390704664921011113844812227381727263315133
SP67Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Veillonella;parvula_group0111681372144471916561401752850022130293271231590922502073379106144802
SP69Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Lautropia;mirabilis000000011500602004000000100003000000100010000000000
SP7Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;mitis11402210228040000020320611200000030000010000000111
SP71Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sp._oral_taxon_338291263915010006400000123910911202081919435451100400000421011432
SP72Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp._oral_taxon_472000700000000000003401050000310300250000000000107200
SP73Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sanguinis997227063187341292430001532378152162000002100010000001000100
SP76Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnospiraceae_[G-8];sp._oral_taxon_50000000000000000000000000003251219141241001110000145229806
SP8Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sp._oral_taxon_1020000100000000000000000001303123132961185198001512140003141111753371032249
SP80Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Eikenella;sp._oral_taxon_0110000000000000000000000002011100303120023013500000000
SP82Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_06401376414148103052106000904553226000010010010020000000000
SP9Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;showae1251601008631002111000007901716800454381273614334004304563061595096126371164811911577844315712100
SP91Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sp._oral_taxon_8643702011720008000001118178224110000000000000000000000
SP98Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Stomatobaculum;sp._oral_taxon_097000000000000000000000000002000050125081294220000001
SPN126Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnospiraceae_[G-8];sp._oral_taxon_500_nov_97.912%000000000000000000000000173556540005000001285600
SPP15Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp15_200002000000000000000000004812221014683901165865071115202139054
SPP8Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp8_232018800100010000000188124500014866872192842003100000942212048058
SPP9Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp9_21001100000000000021000100643345106010000003172000
SPPN3Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;multispecies_sppn3_2_nov_96.868%695129531211224000000000000010781000152002700000000000000001000000
 
 
Download OTU Tables at Different Taxonomy Levels
PhylumCount*: Relative**: CLR***:
ClassCount*: Relative**: CLR***:
OrderCount*: Relative**: CLR***:
FamilyCount*: Relative**: CLR***:
GenusCount*: Relative**: CLR***:
SpeciesCount*: Relative**: CLR***:
* Read count
** Relative abundance (count/total sample count)
*** Centered log ratio transformed abundance
;
 
The species listed in the table has full taxonomy and a dynamically assigned species ID specific to this report. When some reads match with the reference sequences of more than one species equally (i.e., same percent identiy and alignmnet coverage), they can't be assigned to a particular species. Instead, they are assigned to multiple species with the species notaton "s__multispecies_spp2_2". In this notation, spp2 is the dynamic ID assigned to these reads that hit multiple sequences and the "_2" at the end of the notation means there are two species in the spp2.

You can look up which species are included in the multi-species assignment, in this table below:
 
 
 
 
Another type of notation is "s__multispecies_sppn2_2", in which the "n" in the sppn2 means it's a potential novel species because all the reads in this species have < 98% idenity to any of the reference sequences. They were grouped together based on de novo OTU clustering at 98% identity cutoff. And then a representative sequence was chosed to BLASTN search against the reference database to find the closest match (but will still be < 98%). This representative sequence also matched equally to more than one species, hence the "spp" was given in the label.
 
 

Taxonomy Bar Plots for All Samples

 
 

Taxonomy Bar Plots for Individual Comparison Groups

 
 
Comparison No.Comparison NameFamiliesGeneraSpecies
Comparison 1Health vs PeriodontitisPDFSVGPDFSVGPDFSVG
Comparison 2Health_Control vs Periodontitis_ControlPDFSVGPDFSVGPDFSVG
Comparison 3Health_0.025_mM vs Periodontitis_0.025_mMPDFSVGPDFSVGPDFSVG
Comparison 4Health_0.05_mM vs Periodontitis_0.05_mMPDFSVGPDFSVGPDFSVG
Comparison 5Health_0.1_mM vs Periodontitis_0.1_mMPDFSVGPDFSVGPDFSVG
Comparison 6Health_0.2_mM vs Periodontitis_0.2_mMPDFSVGPDFSVGPDFSVG
Comparison 7Health_0.4_mM vs Periodontitis_0.4_mMPDFSVGPDFSVGPDFSVG
Comparison 8Health_0.8_mM vs Periodontitis_0.8_mMPDFSVGPDFSVGPDFSVG
Comparison 9Health_1.6_mM vs Periodontitis_1.6_mMPDFSVGPDFSVGPDFSVG
 
 

VIII. Analysis - Alpha Diversity

 

In ecology, alpha diversity (α-diversity) is the mean species diversity in sites or habitats at a local scale. The term was introduced by R. H. Whittaker[1][2] together with the terms beta diversity (β-diversity) and gamma diversity (γ-diversity). Whittaker's idea was that the total species diversity in a landscape (gamma diversity) is determined by two different things, the mean species diversity in sites or habitats at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity).


References:
Whittaker, R. H. (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30, 279–338. doi:10.2307/1943563
Whittaker, R. H. (1972). Evolution and Measurement of Species Diversity. Taxon, 21, 213-251. doi:10.2307/1218190

 

Alpha Diversity Analysis by Rarefaction

Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows the calculation of species richness for a given number of individual samples, based on the construction of so-called rarefaction curves. This curve is a plot of the number of species as a function of the number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found, but the curves plateau as only the rarest species remain to be sampled.


References:
Willis AD. Rarefaction, Alpha Diversity, and Statistics. Front Microbiol. 2019 Oct 23;10:2407. doi: 10.3389/fmicb.2019.02407. PMID: 31708888; PMCID: PMC6819366.

 
 
 

Boxplot of Alpha-diversity Indices

The two main factors taken into account when measuring diversity are richness and evenness. Richness is a measure of the number of different kinds of organisms present in a particular area. Evenness compares the similarity of the population size of each of the species present. There are many different ways to measure the richness and evenness. These measurements are called "estimators" or "indices". Below is a diversity of 3 commonly used indices showing the values for all the samples (dots) and in groups (boxes).

 
Alpha Diversity Box Plots for All Groups
 
 
 
 
 
 
 
Alpha Diversity Box Plots for Individual Comparisons
 
Comparison 1Health vs PeriodontitisView in PDFView in SVG
Comparison 2Health_Control vs Periodontitis_ControlView in PDFView in SVG
Comparison 3Health_0.025_mM vs Periodontitis_0.025_mMView in PDFView in SVG
Comparison 4Health_0.05_mM vs Periodontitis_0.05_mMView in PDFView in SVG
Comparison 5Health_0.1_mM vs Periodontitis_0.1_mMView in PDFView in SVG
Comparison 6Health_0.2_mM vs Periodontitis_0.2_mMView in PDFView in SVG
Comparison 7Health_0.4_mM vs Periodontitis_0.4_mMView in PDFView in SVG
Comparison 8Health_0.8_mM vs Periodontitis_0.8_mMView in PDFView in SVG
Comparison 9Health_1.6_mM vs Periodontitis_1.6_mMView in PDFView in SVG
 
 
 

Group Significance of Alpha-diversity Indices

To test whether the alpha diversity among different comparison groups are different statisticall, we use the Kruskal Wallis H test provided the "alpha-group-significance" fucntion in the QIIME 2 diversity package. Kruskal Wallis H test is the non parametric alternative to the One Way ANOVA. Non parametric means that the test doesn’t assume your data comes from a particular distribution. The H test is used when the assumptions for ANOVA aren’t met (like the assumption of normality). It is sometimes called the one-way ANOVA on ranks, as the ranks of the data values are used in the test rather than the actual data points. The test determines whether the medians of two or more groups are different.

Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features), 2) Shannon index, and 3) Simpson index.

 
 
Comparison 1.Health vs PeriodontitisObserved FeaturesShannon IndexSimpson Index
Comparison 2.Health_Control vs Periodontitis_ControlObserved FeaturesShannon IndexSimpson Index
Comparison 3.Health_0.025_mM vs Periodontitis_0.025_mMObserved FeaturesShannon IndexSimpson Index
Comparison 4.Health_0.05_mM vs Periodontitis_0.05_mMObserved FeaturesShannon IndexSimpson Index
Comparison 5.Health_0.1_mM vs Periodontitis_0.1_mMObserved FeaturesShannon IndexSimpson Index
Comparison 6.Health_0.2_mM vs Periodontitis_0.2_mMObserved FeaturesShannon IndexSimpson Index
Comparison 7.Health_0.4_mM vs Periodontitis_0.4_mMObserved FeaturesShannon IndexSimpson Index
Comparison 8.Health_0.8_mM vs Periodontitis_0.8_mMObserved FeaturesShannon IndexSimpson Index
Comparison 9.Health_1.6_mM vs Periodontitis_1.6_mMObserved FeaturesShannon IndexSimpson Index
 
 

IX. Analysis - Beta Diversity

 

NMDS and PCoA Plots

Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different groups of samples. There are many different similarity/dissimilarity metrics. In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac) or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac). They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).

For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity, which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).

MDS/PCoA is a scaling or ordination method that starts with a matrix of similarities or dissimilarities between a set of samples and aims to produce a low-dimensional graphical plot of the data in such a way that distances between points in the plot are close to original dissimilarities.

NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into the ranks and use these ranks in the calculation.

In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and NMDS separately. Below are beta diveristy results for all groups together:

 
 
NMDS and PCoA Plots for All Groups
 
 
 
 
 

The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR) for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called Aitchison distance.

Below are the NMDS and PCoA plots of the Aitchison distances of the samples:

 
 
 
 
 
 
 
NMDS and PCoA Plots for Individual Comparisons
 
 
Comparison No.Comparison NameNMDAPCoA
Bray-CurtisCLR EuclideanBray-CurtisCLR Euclidean
Comparison 1Health vs PeriodontitisPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 2Health_Control vs Periodontitis_ControlPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 3Health_0.025_mM vs Periodontitis_0.025_mMPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 4Health_0.05_mM vs Periodontitis_0.05_mMPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 5Health_0.1_mM vs Periodontitis_0.1_mMPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 6Health_0.2_mM vs Periodontitis_0.2_mMPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 7Health_0.4_mM vs Periodontitis_0.4_mMPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 8Health_0.8_mM vs Periodontitis_0.8_mMPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 9Health_1.6_mM vs Periodontitis_1.6_mMPDFSVGPDFSVGPDFSVGPDFSVG
 
 
 
 
 

Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity

 
 
 

Interactive 3D PCoA Plots - Euclidean Distance

 
 
 

Interactive 3D PCoA Plots - Correlation Coefficients

 
 
 

Group Significance of Beta-diversity Indices

To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities, the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA (permutational multivariate analysis of variance) chosen s the group significan testing method.

Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance (Euclidean distance between clr-transformed compositions).

 
 
Comparison 1.Health vs PeriodontitisBray–CurtisCorrelationAitchison
Comparison 2.Health_Control vs Periodontitis_ControlBray–CurtisCorrelationAitchison
Comparison 3.Health_0.025_mM vs Periodontitis_0.025_mMBray–CurtisCorrelationAitchison
Comparison 4.Health_0.05_mM vs Periodontitis_0.05_mMBray–CurtisCorrelationAitchison
Comparison 5.Health_0.1_mM vs Periodontitis_0.1_mMBray–CurtisCorrelationAitchison
Comparison 6.Health_0.2_mM vs Periodontitis_0.2_mMBray–CurtisCorrelationAitchison
Comparison 7.Health_0.4_mM vs Periodontitis_0.4_mMBray–CurtisCorrelationAitchison
Comparison 8.Health_0.8_mM vs Periodontitis_0.8_mMBray–CurtisCorrelationAitchison
Comparison 9.Health_1.6_mM vs Periodontitis_1.6_mMBray–CurtisCorrelationAitchison
 
 
 

X. Analysis - Differential Abundance

16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different species in a sample, i.e., the relative abundance of species, instead of the absolute abundance. In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all be thought of as compositional data. This makes the microbiome read count data “compositional” (Gloor et al, 2017). In general, compositional data represent parts of a whole which only carry relative information (http://www.compositionaldata.com/).

The problem of microbiome data being compositional arises when comparing two groups of samples for identifying “differentially abundant” species. A species with the same absolute abundance between two conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion in terms of differential abundance for microbial species in the samples.

When studying differential abundance (DA), the current better approach is to transform the read count data into log ratio data. The ratios are calculated between read counts of all species in a sample to a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA species without being affected by percentage bias mentioned above

In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes) was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of features that a single feature is tested to be significantly different against. Hence the higher the "W" the more statistical sifgnificane that a feature/species is differentially abundant.


References:

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol. 2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.

Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7. PMID: 32665548; PMCID: PMC7360769.

 
 

ANCOM Differential Abundance Analysis

 
ANCOM Results for Individual Comparisons
Comparison No.Comparison Name
Comparison 1.Health vs Periodontitis
Comparison 2.Health_Control vs Periodontitis_Control
Comparison 3.Health_0.025_mM vs Periodontitis_0.025_mM
Comparison 4.Health_0.05_mM vs Periodontitis_0.05_mM
Comparison 5.Health_0.1_mM vs Periodontitis_0.1_mM
Comparison 6.Health_0.2_mM vs Periodontitis_0.2_mM
Comparison 7.Health_0.4_mM vs Periodontitis_0.4_mM
Comparison 8.Health_0.8_mM vs Periodontitis_0.8_mM
Comparison 9.Health_1.6_mM vs Periodontitis_1.6_mM
 
 

ANCOM-BC Differential Abundance Analysis

 

Starting with version V1.2, we also include the results of ANCOM-BC (Analysis of Compositions of Microbiomes with Bias Correction) (Lin and Peddada 2020). ANCOM-BC is an updated version of "ANCOM" that: (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement. The bias correction (BC) addresses a challenging problem of the bias introduced by differences in the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data. ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework.

References:

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7. PMID: 32665548; PMCID: PMC7360769.

 
 
ANCOM-BC Results for Individual Comparisons
 
Comparison No.Comparison Name
Comparison 1.Health vs Periodontitis
Comparison 2.Health_Control vs Periodontitis_Control
Comparison 3.Health_0.025_mM vs Periodontitis_0.025_mM
Comparison 4.Health_0.05_mM vs Periodontitis_0.05_mM
Comparison 5.Health_0.1_mM vs Periodontitis_0.1_mM
Comparison 6.Health_0.2_mM vs Periodontitis_0.2_mM
Comparison 7.Health_0.4_mM vs Periodontitis_0.4_mM
Comparison 8.Health_0.8_mM vs Periodontitis_0.8_mM
Comparison 9.Health_1.6_mM vs Periodontitis_1.6_mM
 
 
 

LEfSe - Linear Discriminant Analysis Effect Size

LEfSe (Linear Discriminant Analysis Effect Size) is an alternative method to find "organisms, genes, or pathways that consistently explain the differences between two or more microbial communities" (Segata et al., 2011). Specifically, LEfSe uses rank-based Kruskal-Wallis (KW) sum-rank test to detect features with significant differential (relative) abundance with respect to the class of interest. Since it is rank-based, instead of proportional based, the differential species identified among the comparison groups is less biased (than percent abundance based).

Reference:

Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011 Jun 24;12(6):R60. doi: 10.1186/gb-2011-12-6-r60. PMID: 21702898; PMCID: PMC3218848.

 
Health vs Periodontitis
 
 
 
 
 
 
 
LEfSe Results for All Comparisons
 
Comparison No.Comparison Name
Comparison 1.Health vs Periodontitis
Comparison 2.Health_Control vs Periodontitis_Control
Comparison 3.Health_0.025_mM vs Periodontitis_0.025_mM
Comparison 4.Health_0.05_mM vs Periodontitis_0.05_mM
Comparison 5.Health_0.1_mM vs Periodontitis_0.1_mM
Comparison 6.Health_0.2_mM vs Periodontitis_0.2_mM
Comparison 7.Health_0.4_mM vs Periodontitis_0.4_mM
Comparison 8.Health_0.8_mM vs Periodontitis_0.8_mM
Comparison 9.Health_1.6_mM vs Periodontitis_1.6_mM
 
 

XI. Analysis - Heatmap Profile

 

Species vs Sample Abundance Heatmap for All Samples

 
 
 

Heatmaps for Individual Comparisons

 
A) Two-way clustering - clustered on both columns (Samples) and rows (organism)
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1Health vs PeriodontitisPDFSVGPDFSVGPDFSVG
Comparison 2Health_Control vs Periodontitis_ControlPDFSVGPDFSVGPDFSVG
Comparison 3Health_0.025_mM vs Periodontitis_0.025_mMPDFSVGPDFSVGPDFSVG
Comparison 4Health_0.05_mM vs Periodontitis_0.05_mMPDFSVGPDFSVGPDFSVG
Comparison 5Health_0.1_mM vs Periodontitis_0.1_mMPDFSVGPDFSVGPDFSVG
Comparison 6Health_0.2_mM vs Periodontitis_0.2_mMPDFSVGPDFSVGPDFSVG
Comparison 7Health_0.4_mM vs Periodontitis_0.4_mMPDFSVGPDFSVGPDFSVG
Comparison 8Health_0.8_mM vs Periodontitis_0.8_mMPDFSVGPDFSVGPDFSVG
Comparison 9Health_1.6_mM vs Periodontitis_1.6_mMPDFSVGPDFSVGPDFSVG
 
 
B) One-way clustering - clustered on rows (organism) only
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1Health vs PeriodontitisPDFSVGPDFSVGPDFSVG
Comparison 2Health_Control vs Periodontitis_ControlPDFSVGPDFSVGPDFSVG
Comparison 3Health_0.025_mM vs Periodontitis_0.025_mMPDFSVGPDFSVGPDFSVG
Comparison 4Health_0.05_mM vs Periodontitis_0.05_mMPDFSVGPDFSVGPDFSVG
Comparison 5Health_0.1_mM vs Periodontitis_0.1_mMPDFSVGPDFSVGPDFSVG
Comparison 6Health_0.2_mM vs Periodontitis_0.2_mMPDFSVGPDFSVGPDFSVG
Comparison 7Health_0.4_mM vs Periodontitis_0.4_mMPDFSVGPDFSVGPDFSVG
Comparison 8Health_0.8_mM vs Periodontitis_0.8_mMPDFSVGPDFSVGPDFSVG
Comparison 9Health_1.6_mM vs Periodontitis_1.6_mMPDFSVGPDFSVGPDFSVG
 
 
C) No clustering
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1Health vs PeriodontitisPDFSVGPDFSVGPDFSVG
Comparison 2Health_Control vs Periodontitis_ControlPDFSVGPDFSVGPDFSVG
Comparison 3Health_0.025_mM vs Periodontitis_0.025_mMPDFSVGPDFSVGPDFSVG
Comparison 4Health_0.05_mM vs Periodontitis_0.05_mMPDFSVGPDFSVGPDFSVG
Comparison 5Health_0.1_mM vs Periodontitis_0.1_mMPDFSVGPDFSVGPDFSVG
Comparison 6Health_0.2_mM vs Periodontitis_0.2_mMPDFSVGPDFSVGPDFSVG
Comparison 7Health_0.4_mM vs Periodontitis_0.4_mMPDFSVGPDFSVGPDFSVG
Comparison 8Health_0.8_mM vs Periodontitis_0.8_mMPDFSVGPDFSVGPDFSVG
Comparison 9Health_1.6_mM vs Periodontitis_1.6_mMPDFSVGPDFSVGPDFSVG
 
 

XII. Analysis - Network Association

To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation analysis tools are usually used for this purpose. However, microbiome count data are compositional. If count data are normalized to the total number of counts in the sample, the data become not independent and traditional statistical metrics (e.g., correlation) for the detection of specie-species relationships can lead to spurious results. In addition, sequencing-based studies typically measure hundreds of OTUs (species) on few samples; thus, inference of OTU-OTU association networks is severely under-powered. Here we use SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues (Kurtz et al., 2015). SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. SPIEC-EASI provides two algorithms for network inferencing – 1) Meinshausen-Bühlmann's neighborhood selection (MB method) and inverse covariance selection (GLASSO method, i.e., graphical least absolute shrinkage and selection operator). This is fundamentally distinct from SparCC, which essentially estimate pairwise correlations. In addition to these two methods, we provide the results of a third method - SparCC (Sparse Correlations for Compositional Data)(Friedman & Alm 2012), which is also a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between the log-transformed components.


References:

Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015 May 7;11(5):e1004226. doi: 10.1371/journal.pcbi.1004226. PMID: 25950956; PMCID: PMC4423992.

Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8(9):e1002687. doi: 10.1371/journal.pcbi.1002687. Epub 2012 Sep 20. PMID: 23028285; PMCID: PMC3447976.

 

SPIEC-EASI Network Inference by Neighborhood Selection (MB Method)

 

 

 

Association Network Inference by SparCC

 

 

 
 

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