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# DGE Log-Fold Change vs Mean

A common way to visualize the results of a DGE analysis.

***Plotting Guide Setup***

A shared setup for all plotting guides.

```{code-cell} ipython3
# OS-independent path management.
from os import environ
from pathlib import Path
import numpy as np
import GSForge as gsf
import holoviews as hv
hv.extension('bokeh')

OSF_PATH = Path(environ.get("GSFORGE_DEMO_DATA", default="~/GSForge_demo_data/")).expanduser().joinpath("osfstorage", "oryza_sativa")
GEM_PATH = OSF_PATH.joinpath("AnnotatedGEMs", "oryza_sativa_hisat2_raw.nc")
TOUR_DGE = OSF_PATH.joinpath("GeneSetCollections", "tour_DGE")
```

```{code-cell} ipython3
agem = gsf.AnnotatedGEM(GEM_PATH)
agem
```

***Load Differential Gene Expression Analysis Results into a `GeneSetCollection`***

```{code-cell} ipython3
deg_gsc = gsf.GeneSetCollection.from_folder(gem=agem, target_dir=TOUR_DGE, name="DEG Results")
deg_gsc
```

***Select a particular result set of interest***

```{code-cell} ipython3
deg_gs = deg_gsc.gene_sets["'0 + treatment:genotype'__treatment[HEAT]"]
deg_gs
```

***View the data stored within this GeneSet result***

```{code-cell} ipython3
deg_gs.data
```

## Plot gene means vs log-fold change

In some cases we can infer the names of the dimensions, otherwise you will need to pass values to: `log_fold_change_var`, `mean_value_var`, `p_value_var`.

```{code-cell} ipython3
gsf.plots.results.MeanVsLFC(deg_gs)
```