Project FOMC8838_Caries services include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please
download this report, as well as the sequence raw data from the download links provided below.
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 analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the
DADA2 denosing algorithm and pipeline.
We also 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.
The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted
Metagenomic Sequencing (Zymo Research, Irvine, CA).
DNA Extraction: If DNA extraction was performed, one of three different DNA
extraction kits was used depending on the sample type and sample volume and were
used according to the manufacturer’s instructions, unless otherwise stated. The kit used
in this project is marked below:
☐
ZymoBIOMICS® DNA Miniprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS® DNA Microprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☑
N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA
Targeted Library Preparation: The DNA samples were prepared for targeted
sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA).
These primers were custom designed by Zymo Research to provide the best coverage
of the 16S gene while maintaining high sensitivity. The primer sets used in this project
are marked below:
☐
Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑
Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☐
Other: NA
Additional Notes: NA
The sequencing library was prepared using an innovative library preparation process in
which PCR reactions were performed in real-time PCR machines to control cycles and
therefore limit PCR chimera formation. The final PCR products were quantified with
qPCR fluorescence readings and pooled together based on equal molarity. The final
pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™
(Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies,
Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).
Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo
Research, Irvine, CA) was used as a positive control for each DNA extraction, if
performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research,
Irvine, CA) was used as a positive control for each targeted library preparation.
Negative controls (i.e. blank extraction control, blank library preparation control) were
included to assess the level of bioburden carried by the wet-lab process.
Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit
(600 cycles). The sequencing was performed with 10% PhiX spike-in.
Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a
standard curve. The standard curve was made with plasmid DNA containing one copy
of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial
dilutions. The primers used were the same as those used in Targeted Library
Preparation. The equation generated by the plasmid DNA standard curve was used to
calculate the number of gene copies in the reaction for each sample. The PCR input
volume (2 µl) was used to calculate the number of gene copies per microliter in each
DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing
the gene copy number by an assumed number of gene copies per genome. The value
used for 16S copies per genome is 4. The value used for ITS copies per genome is 200.
The amount of DNA per microliter DNA sample was calculated using an assumed
genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or
an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces
cerevisiae, for ITS samples. This calculation is shown below:
Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)
* Absolute Abundance Quantification is only available for 16S and ITS analyses.
The absolute abundance standard curve data can be viewed in Excel here:
The absolute abundance standard curve is shown below:
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.
The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files.
Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1,
with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”.
The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence
and its corresponding quality scores. Most sequence analysis software will be able to open them.
The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:
Sample ID
Original Sample ID
Read 1 File Name
Read 2 File Name
F8838.S10
original sample ID here
zr8838_10V1V3_R1.fastq.gz
zr8838_10V1V3_R2.fastq.gz
F8838.S11
original sample ID here
zr8838_11V1V3_R1.fastq.gz
zr8838_11V1V3_R2.fastq.gz
F8838.S12
original sample ID here
zr8838_12V1V3_R1.fastq.gz
zr8838_12V1V3_R2.fastq.gz
F8838.S13
original sample ID here
zr8838_13V1V3_R1.fastq.gz
zr8838_13V1V3_R2.fastq.gz
F8838.S14
original sample ID here
zr8838_14V1V3_R1.fastq.gz
zr8838_14V1V3_R2.fastq.gz
F8838.S15
original sample ID here
zr8838_15V1V3_R1.fastq.gz
zr8838_15V1V3_R2.fastq.gz
F8838.S16
original sample ID here
zr8838_16V1V3_R1.fastq.gz
zr8838_16V1V3_R2.fastq.gz
F8838.S17
original sample ID here
zr8838_17V1V3_R1.fastq.gz
zr8838_17V1V3_R2.fastq.gz
F8838.S18
original sample ID here
zr8838_18V1V3_R1.fastq.gz
zr8838_18V1V3_R2.fastq.gz
F8838.S19
original sample ID here
zr8838_19V1V3_R1.fastq.gz
zr8838_19V1V3_R2.fastq.gz
F8838.S01
original sample ID here
zr8838_1V1V3_R1.fastq.gz
zr8838_1V1V3_R2.fastq.gz
F8838.S20
original sample ID here
zr8838_20V1V3_R1.fastq.gz
zr8838_20V1V3_R2.fastq.gz
F8838.S21
original sample ID here
zr8838_21V1V3_R1.fastq.gz
zr8838_21V1V3_R2.fastq.gz
F8838.S22
original sample ID here
zr8838_22V1V3_R1.fastq.gz
zr8838_22V1V3_R2.fastq.gz
F8838.S23
original sample ID here
zr8838_23V1V3_R1.fastq.gz
zr8838_23V1V3_R2.fastq.gz
F8838.S24
original sample ID here
zr8838_24V1V3_R1.fastq.gz
zr8838_24V1V3_R2.fastq.gz
F8838.S25
original sample ID here
zr8838_25V1V3_R1.fastq.gz
zr8838_25V1V3_R2.fastq.gz
F8838.S26
original sample ID here
zr8838_26V1V3_R1.fastq.gz
zr8838_26V1V3_R2.fastq.gz
F8838.S27
original sample ID here
zr8838_27V1V3_R1.fastq.gz
zr8838_27V1V3_R2.fastq.gz
F8838.S28
original sample ID here
zr8838_28V1V3_R1.fastq.gz
zr8838_28V1V3_R2.fastq.gz
F8838.S29
original sample ID here
zr8838_29V1V3_R1.fastq.gz
zr8838_29V1V3_R2.fastq.gz
F8838.S02
original sample ID here
zr8838_2V1V3_R1.fastq.gz
zr8838_2V1V3_R2.fastq.gz
F8838.S30
original sample ID here
zr8838_30V1V3_R1.fastq.gz
zr8838_30V1V3_R2.fastq.gz
F8838.S31
original sample ID here
zr8838_31V1V3_R1.fastq.gz
zr8838_31V1V3_R2.fastq.gz
F8838.S32
original sample ID here
zr8838_32V1V3_R1.fastq.gz
zr8838_32V1V3_R2.fastq.gz
F8838.S33
original sample ID here
zr8838_33V1V3_R1.fastq.gz
zr8838_33V1V3_R2.fastq.gz
F8838.S34
original sample ID here
zr8838_34V1V3_R1.fastq.gz
zr8838_34V1V3_R2.fastq.gz
F8838.S35
original sample ID here
zr8838_35V1V3_R1.fastq.gz
zr8838_35V1V3_R2.fastq.gz
F8838.S36
original sample ID here
zr8838_36V1V3_R1.fastq.gz
zr8838_36V1V3_R2.fastq.gz
F8838.S37
original sample ID here
zr8838_37V1V3_R1.fastq.gz
zr8838_37V1V3_R2.fastq.gz
F8838.S38
original sample ID here
zr8838_38V1V3_R1.fastq.gz
zr8838_38V1V3_R2.fastq.gz
F8838.S39
original sample ID here
zr8838_39V1V3_R1.fastq.gz
zr8838_39V1V3_R2.fastq.gz
F8838.S03
original sample ID here
zr8838_3V1V3_R1.fastq.gz
zr8838_3V1V3_R2.fastq.gz
F8838.S40
original sample ID here
zr8838_40V1V3_R1.fastq.gz
zr8838_40V1V3_R2.fastq.gz
F8838.S41
original sample ID here
zr8838_41V1V3_R1.fastq.gz
zr8838_41V1V3_R2.fastq.gz
F8838.S42
original sample ID here
zr8838_42V1V3_R1.fastq.gz
zr8838_42V1V3_R2.fastq.gz
F8838.S43
original sample ID here
zr8838_43V1V3_R1.fastq.gz
zr8838_43V1V3_R2.fastq.gz
F8838.S44
original sample ID here
zr8838_44V1V3_R1.fastq.gz
zr8838_44V1V3_R2.fastq.gz
F8838.S45
original sample ID here
zr8838_45V1V3_R1.fastq.gz
zr8838_45V1V3_R2.fastq.gz
F8838.S46
original sample ID here
zr8838_46V1V3_R1.fastq.gz
zr8838_46V1V3_R2.fastq.gz
F8838.S47
original sample ID here
zr8838_47V1V3_R1.fastq.gz
zr8838_47V1V3_R2.fastq.gz
F8838.S48
original sample ID here
zr8838_48V1V3_R1.fastq.gz
zr8838_48V1V3_R2.fastq.gz
F8838.S49
original sample ID here
zr8838_49V1V3_R1.fastq.gz
zr8838_49V1V3_R2.fastq.gz
F8838.S04
original sample ID here
zr8838_4V1V3_R1.fastq.gz
zr8838_4V1V3_R2.fastq.gz
F8838.S50
original sample ID here
zr8838_50V1V3_R1.fastq.gz
zr8838_50V1V3_R2.fastq.gz
F8838.S51
original sample ID here
zr8838_51V1V3_R1.fastq.gz
zr8838_51V1V3_R2.fastq.gz
F8838.S52
original sample ID here
zr8838_52V1V3_R1.fastq.gz
zr8838_52V1V3_R2.fastq.gz
F8838.S53
original sample ID here
zr8838_53V1V3_R1.fastq.gz
zr8838_53V1V3_R2.fastq.gz
F8838.S54
original sample ID here
zr8838_54V1V3_R1.fastq.gz
zr8838_54V1V3_R2.fastq.gz
F8838.S55
original sample ID here
zr8838_55V1V3_R1.fastq.gz
zr8838_55V1V3_R2.fastq.gz
F8838.S56
original sample ID here
zr8838_56V1V3_R1.fastq.gz
zr8838_56V1V3_R2.fastq.gz
F8838.S57
original sample ID here
zr8838_57V1V3_R1.fastq.gz
zr8838_57V1V3_R2.fastq.gz
F8838.S58
original sample ID here
zr8838_58V1V3_R1.fastq.gz
zr8838_58V1V3_R2.fastq.gz
F8838.S59
original sample ID here
zr8838_59V1V3_R1.fastq.gz
zr8838_59V1V3_R2.fastq.gz
F8838.S05
original sample ID here
zr8838_5V1V3_R1.fastq.gz
zr8838_5V1V3_R2.fastq.gz
F8838.S60
original sample ID here
zr8838_60V1V3_R1.fastq.gz
zr8838_60V1V3_R2.fastq.gz
F8838.S61
original sample ID here
zr8838_61V1V3_R1.fastq.gz
zr8838_61V1V3_R2.fastq.gz
F8838.S62
original sample ID here
zr8838_62V1V3_R1.fastq.gz
zr8838_62V1V3_R2.fastq.gz
F8838.S63
original sample ID here
zr8838_63V1V3_R1.fastq.gz
zr8838_63V1V3_R2.fastq.gz
F8838.S64
original sample ID here
zr8838_64V1V3_R1.fastq.gz
zr8838_64V1V3_R2.fastq.gz
F8838.S65
original sample ID here
zr8838_65V1V3_R1.fastq.gz
zr8838_65V1V3_R2.fastq.gz
F8838.S66
original sample ID here
zr8838_66V1V3_R1.fastq.gz
zr8838_66V1V3_R2.fastq.gz
F8838.S67
original sample ID here
zr8838_67V1V3_R1.fastq.gz
zr8838_67V1V3_R2.fastq.gz
F8838.S68
original sample ID here
zr8838_68V1V3_R1.fastq.gz
zr8838_68V1V3_R2.fastq.gz
F8838.S69
original sample ID here
zr8838_69V1V3_R1.fastq.gz
zr8838_69V1V3_R2.fastq.gz
F8838.S06
original sample ID here
zr8838_6V1V3_R1.fastq.gz
zr8838_6V1V3_R2.fastq.gz
F8838.S70
original sample ID here
zr8838_70V1V3_R1.fastq.gz
zr8838_70V1V3_R2.fastq.gz
F8838.S71
original sample ID here
zr8838_71V1V3_R1.fastq.gz
zr8838_71V1V3_R2.fastq.gz
F8838.S72
original sample ID here
zr8838_72V1V3_R1.fastq.gz
zr8838_72V1V3_R2.fastq.gz
F8838.S73
original sample ID here
zr8838_73V1V3_R1.fastq.gz
zr8838_73V1V3_R2.fastq.gz
F8838.S74
original sample ID here
zr8838_74V1V3_R1.fastq.gz
zr8838_74V1V3_R2.fastq.gz
F8838.S75
original sample ID here
zr8838_75V1V3_R1.fastq.gz
zr8838_75V1V3_R2.fastq.gz
F8838.S76
original sample ID here
zr8838_76V1V3_R1.fastq.gz
zr8838_76V1V3_R2.fastq.gz
F8838.S77
original sample ID here
zr8838_77V1V3_R1.fastq.gz
zr8838_77V1V3_R2.fastq.gz
F8838.S78
original sample ID here
zr8838_78V1V3_R1.fastq.gz
zr8838_78V1V3_R2.fastq.gz
F8838.S79
original sample ID here
zr8838_79V1V3_R1.fastq.gz
zr8838_79V1V3_R2.fastq.gz
F8838.S07
original sample ID here
zr8838_7V1V3_R1.fastq.gz
zr8838_7V1V3_R2.fastq.gz
F8838.S80
original sample ID here
zr8838_80V1V3_R1.fastq.gz
zr8838_80V1V3_R2.fastq.gz
F8838.S81
original sample ID here
zr8838_81V1V3_R1.fastq.gz
zr8838_81V1V3_R2.fastq.gz
F8838.S82
original sample ID here
zr8838_82V1V3_R1.fastq.gz
zr8838_82V1V3_R2.fastq.gz
F8838.S83
original sample ID here
zr8838_83V1V3_R1.fastq.gz
zr8838_83V1V3_R2.fastq.gz
F8838.S84
original sample ID here
zr8838_84V1V3_R1.fastq.gz
zr8838_84V1V3_R2.fastq.gz
F8838.S85
original sample ID here
zr8838_85V1V3_R1.fastq.gz
zr8838_85V1V3_R2.fastq.gz
F8838.S86
original sample ID here
zr8838_86V1V3_R1.fastq.gz
zr8838_86V1V3_R2.fastq.gz
F8838.S87
original sample ID here
zr8838_87V1V3_R1.fastq.gz
zr8838_87V1V3_R2.fastq.gz
F8838.S88
original sample ID here
zr8838_88V1V3_R1.fastq.gz
zr8838_88V1V3_R2.fastq.gz
F8838.S89
original sample ID here
zr8838_89V1V3_R1.fastq.gz
zr8838_89V1V3_R2.fastq.gz
F8838.S08
original sample ID here
zr8838_8V1V3_R1.fastq.gz
zr8838_8V1V3_R2.fastq.gz
F8838.S90
original sample ID here
zr8838_90V1V3_R1.fastq.gz
zr8838_90V1V3_R2.fastq.gz
F8838.S91
original sample ID here
zr8838_91V1V3_R1.fastq.gz
zr8838_91V1V3_R2.fastq.gz
F8838.S92
original sample ID here
zr8838_92V1V3_R1.fastq.gz
zr8838_92V1V3_R2.fastq.gz
F8838.S09
original sample ID here
zr8838_9V1V3_R1.fastq.gz
zr8838_9V1V3_R2.fastq.gz
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.
DADA2 is a software package that models and corrects Illumina-sequenced amplicon errors.
DADA2 infers sample sequences exactly, without coarse-graining into OTUs,
and resolves differences of as little as one nucleotide. DADA2 identified more real variants
and output fewer spurious sequences than other methods.
DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information,
which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances,
whereas most other methods use abundance ranks if they use abundance at all.
The DADA2 error model identifies the differences between sequences, eg. A->C,
whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself,
rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.
DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:
Step 1. Read trimming based on sequence quality
The quality of NGS Illumina sequences often decreases toward the end of the reads.
DADA2 allows to trim off the poor quality read ends in order to improve the error
model building and pair mergicing performance.
Step 2. Learn the Error Rates
The DADA2 algorithm makes use of a parametric error model (err) and every
amplicon dataset has a different set of error rates. The learnErrors method
learns this error model from the data, by alternating estimation of the error
rates and inference of sample composition until they converge on a jointly
consistent solution. As in many machine-learning problems, the algorithm must
begin with an initial guess, for which the maximum possible error rates in
this data are used (the error rates if only the most abundant sequence is
correct and all the rest are errors).
Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising".
The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.
Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences.
Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding
denoised reverse reads, and then constructing the merged “contig” sequences.
By default, merged sequences are only output if the forward and reverse reads overlap by
at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).
Step 5. Remove chimera.
The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants
after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs.
Chimeric sequences are identified if they can be exactly reconstructed by
combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially
from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.
Results
1. Read Quality Plots NGS sequence analaysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first
sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean
quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines.
The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors
that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical
approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next
section “Optimal trim length for ASVs”.
2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline.
In order to achieve highest number of ASVs, an empirical approach was used -
Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are
subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data
Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):
R1/R2
281
271
261
251
241
231
321
25.17%
39.78%
40.45%
40.62%
41.53%
35.67%
311
35.22%
49.37%
50.05%
49.55%
45.38%
36.88%
301
34.58%
49.07%
49.47%
43.92%
37.02%
22.12%
291
34.83%
48.88%
44.06%
36.08%
22.38%
17.08%
281
35.08%
43.70%
36.33%
21.16%
17.14%
11.99%
271
31.15%
36.48%
21.48%
16.25%
11.89%
5.91%
Based on the above result, the trim length combination of R1 = 311 bases and R2 = 261 bases (highlighted red above), was chosen for generating final ASVs for all sequences.
This combination generated highest number of merged non-chimeric ASVs and was used for downstream analyses, if requested.
3. Error plots from learning the error rates
After DADA2 building the error model for the set of data, it is always worthwhile, as a sanity check if nothing else, to visualize the estimated error rates.
The error rates for each possible transition (A→C, A→G, …) are shown below. Points are the observed error rates for each consensus quality score.
The black line shows the estimated error rates after convergence of the machine-learning algorithm.
The red line shows the error rates expected under the nominal definition of the Q-score.
The ideal result would be the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop
with increased quality as expected.
Forward Read R1 Error Plot
Reverse Read R2 Error Plot
The PDF version of these plots are available here:
4. DADA2 Result Summary The table below shows the summary of the DADA2 analysis,
tracking paired read counts of each samples for all the steps during DADA2 denoising process -
including end-trimming (filtered), denoising (denoisedF, denoisedF), pair merging (merged) and chimera removal (nonchim).
Sample ID
F8838.S01
F8838.S02
F8838.S03
F8838.S04
F8838.S05
F8838.S06
F8838.S07
F8838.S08
F8838.S09
F8838.S10
F8838.S11
F8838.S12
F8838.S13
F8838.S14
F8838.S15
F8838.S16
F8838.S17
F8838.S18
F8838.S19
F8838.S20
F8838.S21
F8838.S22
F8838.S23
F8838.S24
F8838.S25
F8838.S26
F8838.S27
F8838.S28
F8838.S29
F8838.S30
F8838.S31
F8838.S32
F8838.S33
F8838.S34
F8838.S35
F8838.S36
F8838.S37
F8838.S38
F8838.S39
F8838.S40
F8838.S41
F8838.S42
F8838.S43
F8838.S44
F8838.S45
F8838.S46
F8838.S47
F8838.S48
F8838.S49
F8838.S50
F8838.S51
F8838.S52
F8838.S53
F8838.S54
F8838.S55
F8838.S56
F8838.S57
F8838.S58
F8838.S59
F8838.S60
F8838.S61
F8838.S62
F8838.S63
F8838.S64
F8838.S65
F8838.S66
F8838.S67
F8838.S68
F8838.S69
F8838.S70
F8838.S71
F8838.S72
F8838.S73
F8838.S74
F8838.S75
F8838.S76
F8838.S77
F8838.S78
F8838.S79
F8838.S80
F8838.S81
F8838.S82
F8838.S83
F8838.S84
F8838.S85
F8838.S86
F8838.S87
F8838.S88
F8838.S89
F8838.S90
F8838.S91
F8838.S92
Row Sum
Percentage
input
25,819
23,579
23,511
26,538
27,134
23,778
24,896
22,115
23,779
25,740
24,793
24,447
24,850
27,994
24,908
20,715
26,917
26,971
25,251
26,960
28,518
25,082
22,877
23,732
24,384
25,004
26,782
25,971
24,662
26,617
25,053
25,556
22,336
25,016
23,650
25,066
28,539
27,825
24,741
25,236
28,677
25,268
26,480
22,783
29,212
26,771
26,132
26,331
24,661
26,093
37,500
24,285
24,871
25,407
27,139
24,614
27,759
20,759
25,825
28,090
27,582
25,607
24,230
28,067
27,160
24,588
25,418
24,703
25,889
27,121
24,154
30,941
22,153
27,646
25,950
23,282
24,346
26,887
25,561
28,583
24,435
27,823
25,743
26,078
27,776
28,821
25,573
25,391
27,952
26,362
24,086
24,628
2,370,535
100.00%
filtered
25,752
23,509
23,443
26,465
27,047
23,717
24,825
22,063
23,717
25,685
24,729
24,367
24,782
27,917
24,840
20,660
26,852
26,881
25,175
26,881
28,439
25,008
22,811
23,668
24,314
24,933
26,693
25,892
24,586
26,537
24,978
25,493
22,276
24,942
23,583
24,996
28,461
27,754
24,675
25,181
28,582
25,190
26,407
22,729
29,124
26,687
26,070
26,261
24,596
26,008
37,400
24,209
24,801
25,321
27,069
24,551
27,684
20,704
25,745
28,028
27,502
25,544
24,168
28,007
27,098
24,521
25,339
24,640
25,830
27,064
24,082
30,848
22,095
27,553
25,874
23,224
24,258
26,809
25,501
28,500
24,376
27,753
25,685
25,999
27,688
28,746
25,501
25,332
27,851
26,276
24,019
24,569
2,363,945
99.72%
denoisedF
24,674
22,417
22,430
25,283
25,821
22,721
23,889
21,078
22,698
24,570
23,451
23,387
23,724
26,666
23,686
19,621
25,777
25,560
24,383
25,728
27,120
23,828
21,529
22,684
23,048
23,837
25,267
24,679
23,880
25,168
23,862
24,351
21,318
23,807
22,429
23,795
27,270
26,432
23,490
24,053
27,290
24,098
25,184
21,481
27,595
25,284
24,686
25,191
23,444
24,327
35,625
23,046
23,390
24,294
25,798
23,413
26,455
19,673
24,565
26,960
26,095
24,203
23,119
26,813
25,938
23,248
24,030
23,376
24,303
25,833
22,738
29,534
20,820
26,329
24,636
21,968
22,849
25,619
24,338
26,933
23,320
26,468
24,783
25,019
26,393
27,289
24,423
24,316
26,753
24,828
22,485
23,378
2,253,387
95.06%
denoisedR
24,132
21,942
21,875
24,949
25,374
22,305
23,131
20,592
22,123
24,195
23,026
22,869
23,248
26,432
23,064
19,171
25,089
25,202
23,901
25,298
26,639
23,243
21,248
22,173
22,490
23,277
24,835
24,041
23,300
24,532
23,392
23,787
20,604
23,247
21,924
23,429
26,464
25,964
23,010
23,516
26,702
23,535
24,931
21,054
27,276
24,663
24,139
24,376
22,809
24,149
35,022
22,512
23,030
22,999
25,393
22,785
26,229
19,449
24,035
26,416
25,426
24,017
22,358
26,369
25,642
22,794
23,558
23,168
24,140
25,172
22,293
28,807
20,588
25,580
24,690
21,452
22,692
25,231
23,901
26,773
22,555
26,116
24,239
24,371
25,890
26,896
23,997
23,858
26,113
24,508
22,056
22,645
2,208,432
93.16%
merged
21,084
18,479
18,659
21,523
22,009
18,634
19,764
17,259
18,490
21,066
19,627
19,389
19,185
23,181
19,344
16,178
21,675
20,867
22,325
21,706
21,982
19,459
17,230
19,214
18,603
19,754
20,873
20,213
21,577
20,369
20,075
20,874
17,482
19,859
18,046
18,667
22,972
20,853
19,402
19,373
22,656
20,122
21,649
17,477
22,666
20,166
19,636
20,969
18,927
20,573
29,965
19,448
19,847
19,978
21,470
19,464
23,961
16,855
20,671
22,949
20,782
19,903
19,331
23,241
22,364
18,718
20,027
19,787
19,684
21,256
18,086
24,622
17,356
21,718
21,903
18,403
18,104
22,021
20,053
22,487
19,508
22,373
21,068
21,220
22,175
22,572
20,888
20,956
22,064
20,297
17,084
19,398
1,876,219
79.15%
nonchim
12,970
11,708
9,308
11,175
10,226
10,005
11,068
8,968
10,319
12,381
10,797
10,117
10,913
10,575
10,426
7,344
13,694
11,346
12,906
10,706
12,069
10,906
9,752
12,237
10,462
10,334
11,167
12,201
7,624
11,529
12,496
10,422
9,844
12,193
11,138
11,807
11,762
11,804
10,447
9,737
13,089
12,564
10,735
9,462
13,251
11,088
10,700
13,501
11,023
11,225
14,254
10,579
14,676
12,598
13,092
10,434
11,508
10,134
10,842
10,998
10,658
12,305
11,019
10,184
11,519
10,521
11,560
10,396
10,876
12,331
9,970
12,910
9,772
10,951
13,073
12,132
10,042
13,223
9,419
11,828
11,308
12,406
11,817
10,429
9,969
11,267
10,773
12,635
14,551
12,251
8,012
11,614
1,034,357
43.63%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 16892 unique merged and chimera-free ASV sequences were identified, and their corresponding
read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for
microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.
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 *
Code
Category
MPC=0% (>=1 read)
MPC=0.01%(>=103 reads)
A
Total reads
1,034,357
1,034,357
B
Total assigned reads
1,030,934
1,030,934
C
Assigned reads in species with read count < MPC
0
6,485
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
92
92
F
Samples with reads >= 500
92
92
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
1,030,934
1,024,449
I
Reads assigned to single species
965,419
962,154
J
Reads assigned to multiple species
38,036
37,398
K
Reads assigned to novel species
27,479
24,897
L
Total number of species
460
303
M
Number of single species
305
244
N
Number of multi-species
23
8
O
Number of novel species
132
51
P
Total unassigned reads
3,423
3,423
Q
Chimeric reads
140
140
R
Reads without BLASTN hits
2
2
S
Others: short, low quality, singletons, etc.
3,281
3,281
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.
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.
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).
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.
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
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 H 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.
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:
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) as the group significant 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).
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.
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.
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.
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 InversECovariance 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.
The results of this analysis are for research purpose only. They are not intended to diagnose, treat, cure, or prevent any disease. Forsyth and FOMC
are not responsible for use of information provided in this report outside the research area.
