Code sharing for microbiomics analysis is proposed through standardized R packages and GitHub. This facilitates reproducible, efficient and collaborative analysis. Examples of standardized preprocessing, diversity analysis and visualization tools are provided. The microbiome package and wiki provide ready-made analysis examples to build upon.

1. Code sharing for microbiomics
Leo, Wageningen 7.9.201 2

3. Challenges with computer code:
- Analyses not standardized -> confusion & non-optimal choices
- Poor documentation -> poor reproducibility & waste of time
- Reinventing the wheel -> waste of time & resources
Solution:
- Harmonized software libraries (e.g. R packages)
- Easier to share tools (GitHub)
-> more reliable & reproducible
-> more standardized
-> avoid repetitive coding
-> added value for publications
-> distributed version control; all changes automatically tracked
-> facilitates Helsinki-Wageningen collaboration

6. Wiki: various example analyses already implemented
- retrieve data from MySQL( H/M/PITChip)
- preprocessing (profiling & HITChip Atlas)
- analysis routines (diversities, tables,
Wilcoxon tests etc.)
- visualization
-> improving through time

7. Step­by­step examples with source code and
simulated data

8. Common core microbiota:
effect of analysis depth and prevalence
"Blanket analysis"
github.com/microbiome
Core size
Estimate the frequency of
belonging to the core for
each phylotype; confidence
intervals with bootstrap
Ab
un
dan
Salonen A, et al. (2012) The adult intestinal core
Prevalence
ce
microbiota is determined by analysis depth and health
status, Clinical Microbiology and Infection 18:16–20.

9. Compatible with HITChip Atlas of Human Gut
Microbiota (>3200 samples)
45 studies - Standardized Platform
>1 000 phylotypes
>3000 samples
-> Compare your own data to HITChip data collections?

10. Differences to the old profiling script?
-> Separate preprocessing from analysis
-> Support modularity
-> removed outdated options & outputs from profiling script
1. Preprocessing: minimal output from profiling script:
- preprocessed data matrices (oligo/L1 /L2/species/absolute
scale) with NMF/RPA/SUM
- preprocessing log (parameter values etc.)
- quality control plots (heatmap)
2. Analysis & visualization routines
- based on profiling script output & done afterwards -> modular
- used when needed, not run by default
-> keeping it simple & storing disk space

11. Summary: code development & sharing through GitHub
In-house sharing infrastructure for code
-> distributed package maintenance
-> avoid bugs; facilitate transparency & reproducibility
-> additional visibility & citations?
Avoid extra work and focus on the essential
-> check for ready-made examples from the wiki!
-> ask for help -> let's add examples to the wiki!
Manage and share your own code?
-> GitHub and microbiome R package
-> Version control
microbiome.github.com

12. To discuss
Do you have R code which could be useful for others?
-> let's polish, document & add it in the package!
Which tools to include?
- diversity/richness/evenness calculations
- PCA, hierarchical clusterings, RDA etc.
- Wilcoxon tests
- Association (Spearman) tables phylotypes vs. phenotypes
- Relative contributions from bg variables
-> ideally, only standard things should be standardized;
for rare analyses just use basic R & other packages

14. HITChip preprocessing steps
- Spatial correction
- Between array normalization
- Background correction
- Oligo summarization

15. 1. Spatial correction

16. 2. Between­array normalization: minmax vs. quantiles?

17. 3. Background correction: skip!

18. 4. Oligo summarization
NMF
RPA
SUM
AVE

19. Preprocessing: recommendations
* Normalization:
- minmax: use by default
- quantile: use if samples have 'similar' microbiota
* Background correction
-> ignore
* Oligo summarization
-> NMF: for L0/L1 /L2 levels
-> RPA: if species level is also included
-> (SUM: for comparison)
-> AVE: deprecated
=> The defaults readily implemented in the pipeline

20. Diversity analysis
Richness, evenness, diversity
Shannon vs. Inverse Simpson?
Detection threshold?

21. Richness with various indices and thresholds

22. Recommendation:
- oligo level
- shannon diversity
- richness as species
count with 80%
quantile detection
threshold
- evenness with
Pielou's index

23. Further analysis tools

24. microbiome.github.com