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# Some Historical Context
In the context of GN code, a "dataset" specifically refers to a grouping of traits. It's easy to confuse that with individual GN traits (which can best be defined as a single set of sample data). So Time Series (TS) data would consist of multiple GN traits, one trait for each time point.
A phenotype is an observed 'feature' such as body weight. In genetics, traits are characteristics about humans and other living organisms that can be described or measured. Sex is a trait. In GN we mixed them up.
When we do an experiment we take measurements across a range of individuals at a time point. Each time point is a vector of data. When repeated with the same individual/strain we can take the mean (meaningfully). That is another vector.
As alluded to earlier, 'datasets' are combinations of measurements referring to one (or more?) experiments. [Suggestion] Link dataset to measurements/phenotypes in an experiment at a certain time point. Thus we have a matrix of data (columns are measurements and means). For probesets and RNA-seq we treat them the same as simple vectors of measurements.
When we have time series we get a 3rd dimension which can be represented in metadata. No need to account for that at the storage level. We'll need to handle it in the UI and with any computations.
We invented the term “attribute” for trait OR metadata type that is even broader. For example an attribute can include an indicator for inclusion of a case in a study. An attribute can be alphanumeric and can be used as a co-factor. Does get messy.
# Extracting TS Data from a provided data set
Suppose you have the following CSV file (snippet):
```
mouse_ID BW day strain sex inf_dose animal.no.
241 CC001_m_1 100 perc_d00 CC001 m 10 FFU 1
242 CC001_m_1 98.56 perc_d03 CC001 m 10 FFU 1
243 CC001_m_1 NA perc_d13 CC001 m 10 FFU 1
244 CC001_m_1 NA perc_d12 CC001 m 10 FFU 1
245 CC001_m_1 NA perc_d10 CC001 m 10 FFU 1
246 CC001_m_1 100.92 perc_d04 CC001 m 10 FFU 1
247 CC001_m_1 98.08 perc_d01 CC001 m 10 FFU 1
248 CC001_m_1 76.21 perc_d08 CC001 m 10 FFU 1
249 CC001_m_1 93.22 perc_d05 CC001 m 10 FFU 1
250 CC001_m_1 90.42 perc_d06 CC001 m 10 FFU 1
[...]
```
Each day (d1, d2, d3) represents a different data set. From the above, a "dataset" is grouped by "day".
In the above, Strain is CC001. It is male and animal no 1. For mapping, only the strain name and group will matter. Combine sex and days and infection status to a data set for mapping. A target file is provided that describes all of this.
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