I'm trying to improve cell concentrations by changing the type of medium. I have 4 different cases:
Control medium
Medium with substance X
Medium with substance Y
Medium with substance Z
I've collected cell concentration data in triplicate (n=3 for each case) every 24 hours over a total time period of 10 days. The cell type and culture methods are all the same, it's just the medium that's changing. I'm struggling to figure out which statistical test would be appropriate to analyze the conditions against one another. It's the added aspect of the 10 day experiment that's confusing me. Thanks!
Just to clarify, when you say that you collected cell concentration data "in triplicate", do you mean that (1) you simply have 3 independent samples that are each sampled once per day? or (2) that you're collecting data 3 times per day?
I am working under the assumption that you mean the first option. Yes, a repeated measures ANOVA would be appropriate. To expand on the previous answer though, you are essentially asking "Do different Mediums affect cell concentration data differently?". With the 10-day variable added though, you can further answer "How does cell concentration data change over time when grown on different mediums?"
To do so, you will have to treat 'Sample" as a random effect because you are repeatedly sampling from the same one each time. The website below has a similar research design and should help you think about your own project. It's in a specific data analysis software package that costs money (JMP), but you can look up similar examples in your favorite software of choice.
http://www.jmp.com/support/notes/30/584.html
As it relates to your project, you would want to have the following as independent variables: "Medium" (if significant, this will tell you that medium has an effect), "Time" (if significant, this will tell you that time has a significant effect), "Medium*Time" (if significant, this will tell you that there there is a relationship between cell concrentation and time, but this relationship varies depending on the Medium), and "Sample[Medium]" (where a random effect is incorporated).
Hope that helps a bit more. Good luck!
Just to clarify, when you say that you collected cell concentration data "in triplicate", do you mean that (1) you simply have 3 independent samples that are each sampled once per day? or (2) that you're collecting data 3 times per day?
I am working under the assumption that you mean the first option. Yes, a repeated measures ANOVA would be appropriate. To expand on the previous answer though, you are essentially asking "Do different Mediums affect cell concentration data differently?". With the 10-day variable added though, you can further answer "How does cell concentration data change over time when grown on different mediums?"
To do so, you will have to treat 'Sample" as a random effect because you are repeatedly sampling from the same one each time. The website below has a similar research design and should help you think about your own project. It's in a specific data analysis software package that costs money (JMP), but you can look up similar examples in your favorite software of choice.
http://www.jmp.com/support/notes/30/584.html
As it relates to your project, you would want to have the following as independent variables: "Medium" (if significant, this will tell you that medium has an effect), "Time" (if significant, this will tell you that time has a significant effect), "Medium*Time" (if significant, this will tell you that there there is a relationship between cell concrentation and time, but this relationship varies depending on the Medium), and "Sample[Medium]" (where a random effect is incorporated).
Hope that helps a bit more. Good luck!
I would consider time as a continuous variable, and plot the cell concentration data agaisnt time, and use some kind of regression, could be linear, maybe logarithmic. You will have 4 different functions, one for every medium you tested. The highest function would be of the best medium for rapid cell cultivation, if that is your desire.
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