9.6 Try it Question 3 - What microbes (ASVs) differ between males and females, and does age have an impact

Approach: Perform DESeq analysis between females and males and determine how many differentially abundant microbes are there between the sexes. Then examine if age has a further impact on the abundance of the microbes between the sexes.

9.6.1 Step 3A. Perform differential abundance analysis between genders.

  • Design - is based on gender (no double quotes)

  • Groups to compare - females, F and males, M (baseline)

  • Plot Phylum on the X-axis and color by Class

  • Make sure your graph has a title that includes the comparison you are making (ex. WO vs HD)

  • Refer to the “Differential abundance” section of the “Analyze 16S rRNA Data with phyloseq” tutorial for help using the plot_ordination() function.

  • Use the code below as a template:

# STEP 2: Select the groups to compare, where the latter group is your baseline
my_comparison <-c("gender", "fill in the blank", "fill in the blank (baseline)")

# STEP 3: Run the differential abundance analysis 
Significant_DEseq2_ASVs<-Differential_Abundance(miso_counts, DESeq2, my_comparison, 0.05)

# STEP 4: Retrieve the list of ASVs with a significant difference in abundance between the chosen groups
Significant_DEseq2_ASVs

# STEP 5: Plot the results with your chosen x axis and legend
ggplot(significant_ASVs, aes(x = fill in the blank, y=log2FoldChange, color= fill in the blank)) + geom_point(size=4, position="jitter") +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5))+
  ggtitle("fill in the blank")
3A-1. Insert the resulting plot below:

3A-2. How many differentially abundant ASVs were identified? This is the number of rows in the result table.

3A-3. Give an example of a differentially abundant ASVs that was significantly higher in females than males. Include ASV ID and taxa. HINT: If male was your baseline, then an example of an ASV that is higher in females will have a positive log2FC, and vice versa if female was your baseline group. Use the arrows in the header row of the table to scroll through the taxonomy of an ASV.

3A-4. Look up this microbe. What function does it serve in the human gut, and how might its abundance be connected to gender?

3A-5. Give an example of a differentially abundant ASVs that was significantly higher in males than females. Include ASV ID and taxa. HINT: If male was your baseline, then an example of an ASV that is higher in males will have a negative log2FC, and vice versa if female was your baseline group. Use the arrows in the header row of the table to scroll through the taxonomy of an ASV.

3A-6. Look up this microbe. What function does it serve in the human gut, and how might its abundance be connected to gender?

9.6.2 Step 3B. Determine the impact of younger age on differential abundance in males and females, by subsetting age < 50 data from the phyloseq object, and performing DESeq2 analysis on this subset.

  • Subset phyloseq object to only include younger individuals (age < 50)
  • For DESeq2 analysis, keep the design the same as in part A - based on gender
  • Groups to compare: females (F) and males (M, baseline)
  • Plot Phylum on the X-axis and color by Class
  • Use template code below:
# STEP 1: subset by age < 50
ageA = subset_samples(miso_counts, age <50) 

# STEP 2: Convert the phyloseq object to a DESeq2 object and specify experimental design
DESeq2 <- phyloseq_to_deseq2(ageA, design = ~ fill in the blank)

# STEP 3: Select the groups to compare, where log2FoldChange reported will correspond to  y/x.
my_comparison <-c("gender", "fill in the blank", "fill in the blank (baseline)")

# STEP 4: Run the differential abundance analysis 
Significant_DEseq2_ASVs<-Differential_Abundance(miso_counts, DESeq2, my_comparison, 0.05)

# STEP 5: Retrieve the list of ASVs with a significant difference in abundance between the chosen groups
Significant_DEseq2_ASVs

# STEP 6: Plot the results with your chosen x axis and legend
ggplot(Significant_DEseq2_ASVs, aes(x = fill in the blank, y=log2FoldChange, color= fill in the blank)) + geom_point(size=4, position = "jitter") +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5))+
  ggtitle("fill in the blank")
3B-1. Insert the resulting plot below:

3B-2. How many differentially abundant ASVs were identified?

3B-3. Give an example of a differentially abundant ASVs that was significantly higher in younger females than younger males. Include ASV ID and taxa. HINT: If male was your baseline, then an example of an ASV that is higher in females will have a positive log2FC, and vice versa if female was your baseline group. Use the arrows in the header row of the table to scroll through the taxonomy of an ASV.

3B-4. Give an example of a differentially abundant ASVs that was significantly higher in younger males than younger females. Include ASV ID and taxa. HINT: If male was your baseline, then an example of an ASV that is higher in males will have a negative log2FC, and vice versa if female was your baseline group. Use the arrows in the header row of the table to scroll through the taxonomy of an ASV.

3B-5. Compared to the overall dataset, do younger individuals have more or less differentially abundant microbes between genders?

9.6.3 Step 3C. Determine the impact of older age on differential abundance in males and females, by subsetting age >= 50 data from the phyloseq object, and performing DESeq2 analysis on this subset.

  • You can use template code below:
# STEP 1: subset by age >=  50
ageB = subset_samples(miso_counts, age >= 50) 

# STEP 2: Convert the phyloseq object to a DESeq2 object and specify experimental design
DESeq2 <- phyloseq_to_deseq2(ageB, design = ~ fill in the blank)

# STEP 3: Select the groups to compare, where log2FoldChange reported will correspond to  y/x.
my_comparison <-c("gender", "fill in the blank", "fill in the blank")

# STEP 4: Run the differential abundance analysis 
Significant_DEseq2_ASVs<-Differential_Abundance(miso_counts, DESeq2, my_comparison, 0.05)

# STEP 5: Retrieve the list of ASVs with a significant difference in abundance between the chosen groups
Significant_DEseq2_ASVs

# STEP 6: Plot the results with your chosen x axis and legend
ggplot(Significant_DEseq2_ASVs, aes(x = fill in the blank, y=log2FoldChange, color= fill in the blank)) + geom_point(size=4, position = "jitter") +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5))+
  ggtitle("fill in the blank")
3C-1. Insert the resulting plot below:

3C-2. How many differentially abundant ASVs were identified? This is the number of rows in the result table.

3C-3. Compared to the overall dataset, do older individuals have more or less differentially abundant microbes between genders?

3C-4. Compare all the plots you have made. Give a specific example of one difference you see.

9.6.4 Footnotes

9.6.4.2 Contributions and affiliations

  • Valeriya Gaysinskaya, Johns Hopkins University
  • Gauri Paul, Clovis Community College
  • Frederick Tan, Johns Hopkins University
  • Sayumi York, Notre Dame of Maryland University
All materials CC-BY unless noted otherwise.