Yaamini’s Notebook: Gonad Histology Update 3

GLMs are neat

I forgot how useful all of the regression analysis techniques I learned last spring were until I had to use them for my own dataset! I met with Tim last week, who suggested I use a binomial GLM with a logit link to see if treatment affected maturation stage.

I created this R script to analyze my revised dataset.

Maturation stage

Any oyster with a maturation stage of 3 (ripe) or 4 (spawning/spent), I considered mature. I then created a new column that assigned 0 for immature and 1 for mature based on the maturation stages. I also renamed

I then used stepwise addition to discern which covariates to put into my model. I started with three separate models to explain maturation: Mature ~ Treatment (ambient vs. low pH), Mature ~ modifiedSex (female vs. male vs. unripe), and Mature ~ Pre.or.Post.OA (pre-treatment vs. post-treatment sampling). The model that used sex to explain maturity was the most significant model, so that became my base. Using add1, I found that no other variable was significant and needed to be included in the model. Looking at the model summary, I saw that males were typically more mature than females.

Sex ratios

For my post-treatment data, I restructured the data to make a contingency table with pH treatment as rows and sex classification (female vs. male vs. unripe) as columns. I then used a poisson GLM with a log link to analyze the contingency table with a chi-squared test for homogeneity. I failed to reject my null hypothesis.

TL;DR

Treatment did not affect maturation. This makes sense with my other results that found egg production did not differ between low and ambient pH treatments. The only phenotype we have so far is lower hatch rates when a cross included a low pH female. I’m really interested to analyze my larval mortality data to see if there is a cohesive narrative.

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Yaamini’s Notebook: Gonad Histology Update 2

I think I understand classification better now

I met with Molly in February to discuss histology classificiation methods. She suggested I read Mladineo et al. 2007 and Ropes 1968 for their descriptions of maturation stages, as well as better pictures to help me discern between sexes and stages. I read the papers and they were very helpful! Mladineo et al. 2007 also had some interesting visuals and analysis methods that I could use if I find any significant differences in maturation between treatments.

I downloaded the original Google Sheets document and saved it as a .csv file in my repository. I then revised my classifications. I felt more confident assigning sexes than I did before, which made some of the stage classification a bit easier. I still have my doubts on a few classifications, so I’m going to send it over to Brent to see if he can help me now that Molly’s busy with work at Taylor.

Remeber those red spots I said I saw in my previous histology update? I emailed those pictures to Lisa Crosson. She sent me this response:

I think they might be ferrous cells/granules which typically have a brown/red “blob” morphology. I’ve seen them before in shellfish digestive, kidney and gonadal tissues likely due to the fact that there is a lot of cellular turnover going on leaving behind iron deposits. Could also be a random staining artifact which happens from time to time.

In my classification spreadsheet, I added a column for ferrous inclusions. I don’t know if this could be realted to maturation stage or sex, but it could be interesting to see if there’s a difference in presence/absence between treatments.

Now that I feel more confident with my classifications, I can work on analyzing them. If I make any change to the classifications, all I need to do is read my .csv file in again!

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