Title: Gut Microbiota Analyses of Inflammatory Bowel Diseases from a representative Saudi
Population
Version: 2 Date: 06 Sep 2022
Reviewer:  João Carlos Gomes-neto
Reviewer's report:
Dear authors,
I will try to be practical here, and my comments are intentionally made to help highlighting the
quality of the work and its impact. However, I understand that I may be intruding since I will be
proposing major changes in the structure of the narrative and figures.  This is not an imposition
but I suggestion in order to facilitate readability.
Here is how I would structure the new figures with the same data:
1. Alpha-diversity analysis panel
- use two metrics of alpha-diversity such as simpson and shannon indexes and plot across the
main treatment (IBD status or case/control status). You could use R face_wrap and break it apart
based on the other features such as age, sex, etc. I understand that some of these variables would
have to categorized or left as continuous for such a plotting. I would then run a glm model (alpha
metric ~ each variable) or a permanova model and retrieve the p-value and R-squared for each
variable and add to the individual plots partitioned by that corresponding variable. This panel
would exhaustively look at differences in alpha-diversity and the contribution of each variable
on the data.
2. Beta-diversity analysis panel
- this is similar to point 1, but now using Bray-curtis like you have with 2D PCAs colored by
each variable, while adding the p-value and R-squared per variable, which can be retrieved from
a permanova model.
3 - Once the diversity analysis has be done systematically, now you could use 2 approaches
to identify the genus and/or species differentiating between the major treatment (case-control
variable - IBD status)
- T.test with FDR or bonferroni correction vs the log fold change
- Random forest
4. Once you have identified the major taxa differentiating between the major treatment, now you
can seek to measure the real contribution of this taxa on predicting the outcome of interest (IBD
status), while accounting for the other factors in the model either as fixed or random effects.
If using IBD status as binary then a logistic regression model would work. That would be my
preference and each model would be stated as (ibd status ~ taxa + fixed  and/or random effect),
and the predicted probabilities could be graphically depicted.
5. Then lastly, I would explore the correlation between those taxa across treatments.
Other notes:
I don't think you would need to filter out minor taxa prior to doing the analysis, first because they
are part of the data and composition, and second, because those will not pop up as significant
anyway, but their heterogeneity may affect diversity metrics. Another thing that needs to be
clarified is if normalized or rarefied taxonomic data was used for the analysis. Maybe I missed
that point. If so, apologies.
Sincerely
Title: Gut Microbiota Analyses of Inflammatory Bowel Diseases from a representative Saudi
Population
Version: 2 Date: 06 Sep 2022
Reviewer:  João Carlos Gomes-neto
Reviewer's report:
Dear authors,
I will try to be practical here, and my comments are intentionally made to help highlighting the
quality of the work and its impact. However, I understand that I may be intruding since I will be
proposing major changes in the structure of the narrative and figures.  This is not an imposition
but I suggestion in order to facilitate readability.
Here is how I would structure the new figures with the same data:
1. Alpha-diversity analysis panel
- use two metrics of alpha-diversity such as simpson and shannon indexes and plot across the
main treatment (IBD status or case/control status). You could use R face_wrap and break it apart
based on the other features such as age, sex, etc. I understand that some of these variables would
have to categorized or left as continuous for such a plotting. I would then run a glm model (alpha
metric ~ each variable) or a permanova model and retrieve the p-value and R-squared for each
variable and add to the individual plots partitioned by that corresponding variable. This panel
would exhaustively look at differences in alpha-diversity and the contribution of each variable
on the data.
2. Beta-diversity analysis panel
- this is similar to point 1, but now using Bray-curtis like you have with 2D PCAs colored by
each variable, while adding the p-value and R-squared per variable, which can be retrieved from
a permanova model.
3 - Once the diversity analysis has be done systematically, now you could use 2 approaches
to identify the genus and/or species differentiating between the major treatment (case-control
variable - IBD status)
- T.test with FDR or bonferroni correction vs the log fold change
- Random forest
4. Once you have identified the major taxa differentiating between the major treatment, now you
can seek to measure the real contribution of this taxa on predicting the outcome of interest (IBD
status), while accounting for the other factors in the model either as fixed or random effects.
If using IBD status as binary then a logistic regression model would work. That would be my
preference and each model would be stated as (ibd status ~ taxa + fixed  and/or random effect),
and the predicted probabilities could be graphically depicted.
5. Then lastly, I would explore the correlation between those taxa across treatments.
Other notes:
I don't think you would need to filter out minor taxa prior to doing the analysis, first because they
are part of the data and composition, and second, because those will not pop up as significant
anyway, but their heterogeneity may affect diversity metrics. Another thing that needs to be
clarified is if normalized or rarefied taxonomic data was used for the analysis. Maybe I missed
that point. If so, apologies.
Sincerely
Please review this paper.
user
Title: Gut Microbiota Analyses of Inflammatory Bowel Diseases from a representative Saudi
Population
Version: 2 Date: 06 Sep 2022
Reviewer:  João Carlos Gomes-neto
Reviewer's report:
Dear authors,
I will try to be practical here, and my comments are intentionally made to help highlighting the
quality of the work and its impact. However, I understand that I may be intruding since I will be
proposing major changes in the structure of the narrative and figures.  This is not an imposition
but I suggestion in order to facilitate readability.
Here is how I would structure the new figures with the same data:
1. Alpha-diversity analysis panel
- use two metrics of alpha-diversity such as simpson and shannon indexes and plot across the
main treatment (IBD status or case/control status). You could use R face_wrap and break it apart
based on the other features such as age, sex, etc. I understand that some of these variables would
have to categorized or left as continuous for such a plotting. I would then run a glm model (alpha
metric ~ each variable) or a permanova model and retrieve the p-value and R-squared for each
variable and add to the individual plots partitioned by that corresponding variable. This panel
would exhaustively look at differences in alpha-diversity and the contribution of each variable
on the data.
2. Beta-diversity analysis panel
- this is similar to point 1, but now using Bray-curtis like you have with 2D PCAs colored by
each variable, while adding the p-value and R-squared per variable, which can be retrieved from
a permanova model.
3 - Once the diversity analysis has be done systematically, now you could use 2 approaches
to identify the genus and/or species differentiating between the major treatment (case-control
variable - IBD status)
- T.test with FDR or bonferroni correction vs the log fold change
- Random forest
4. Once you have identified the major taxa differentiating between the major treatment, now you
can seek to measure the real contribution of this taxa on predicting the outcome of interest (IBD
status), while accounting for the other factors in the model either as fixed or random effects.
If using IBD status as binary then a logistic regression model would work. That would be my
preference and each model would be stated as (ibd status ~ taxa + fixed  and/or random effect),
and the predicted probabilities could be graphically depicted.
5. Then lastly, I would explore the correlation between those taxa across treatments.
Other notes:
I don't think you would need to filter out minor taxa prior to doing the analysis, first because they
are part of the data and composition, and second, because those will not pop up as significant
anyway, but their heterogeneity may affect diversity metrics. Another thing that needs to be
clarified is if normalized or rarefied taxonomic data was used for the analysis. Maybe I missed
that point. If so, apologies.
Sincerely
assistant