Update docs (#36)

* Add how to install dependencies * Specify BioMASS.jl version * Fix construction
pasmopy · Jun 20, 2022 · 86bc7db · 86bc7db
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diff --git a/docs/getting-started/construction.md b/docs/getting-started/construction.md
@@ -11,132 +11,136 @@ nav_order: 2
 
 1. Use [**pasmopy.Text2Model**](https://pasmopy.readthedocs.io/en/latest/model_development.html) to build a mechanistic model
 
-   ```python
-   import os
+    ```python
+    import os
 
-   from pasmopy import Text2Model
+    from pasmopy import Text2Model
 
 
-   Text2Model(os.path.join("models", "erbb_network.txt")).convert()
-   ```
+    Text2Model(os.path.join("models", "erbb_network.txt")).convert()
+    ```
 
 1. Rename `erbb_network/` to CCLE_name or TCGA_ID, e.g., `MCF7_BREAST` or `TCGA_3C_AALK_01A`
 
-   ```python
-   import shutil
+    ```python
+    import shutil
 
-   shutil.move(
-       os.path.join("models", "erbb_network"),
-       os.path.join("models", "breast", "TCGA_3C_AALK_01A")
-   )
-   ```
+    shutil.move(
+        os.path.join("models", "erbb_network"),
+        os.path.join("models", "breast", "TCGA_3C_AALK_01A")
+    )
+    ```
 
 ## Other tasks for incorporating gene expression levels
 
 1. Add weighting factors for each gene (prefix: `"w_"`) to `name2idx/parameters.py`
 
-   ```python
-   from pasmopy import Model
-   from pasmopy.preprocessing import WeightingFactors
-
-   from models import erbb_network
-
-
-   model = Model(erbb_network.__package__).create()
-
-   gene_expression = {
-       "ErbB1": ["EGFR"],
-       "ErbB2": ["ERBB2"],
-       "ErbB3": ["ERBB3"],
-       "ErbB4": ["ERBB4"],
-       "Grb2": ["GRB2"],
-       "Shc": ["SHC1", "SHC2", "SHC3", "SHC4"],
-       "RasGAP": ["RASA1", "RASA2", "RASA3"],
-       "PI3K": ["PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG"],
-       "PTEN": ["PTEN"],
-       "SOS": ["SOS1", "SOS2"],
-       "Gab1": ["GAB1"],
-       "RasGDP": ["HRAS", "KRAS", "NRAS"],
-       "Raf": ["ARAF", "BRAF", "RAF1"],
-       "MEK": ["MAP2K1", "MAP2K2"],
-       "ERK": ["MAPK1", "MAPK3"],
-       "Akt": ["AKT1", "AKT2"],
-       "PTP1B": ["PTPN1"],
-       "GSK3b": ["GSK3B"],
-       "DUSP": ["DUSP5", "DUSP6", "DUSP7"],
-       "cMyc": ["MYC"],
-   }
-
-   weighting_factors = WeightingFactors(model, gene_expression)
-   weighting_factors.add_to_params()
-   weighting_factors.set_search_bounds()
-   ```
-
-1. Edit `set_search_param.py`
-
-   ```python
-   import os
-
-   import numpy as np
-
-   from pasmopy import Individualization
-
-   from . import __path__
-   from .name2idx import C, V
-   from .set_model import initial_values, param_values
-
-
-   incorporating_gene_expression_levels = Individualization(
-       parameters=C.NAMES,
-       species=V.NAMES,
-       transcriptomic_data=os.path.join("transcriptomic_data", "TPM_RLE_postComBat_BRCA_BREAST.csv"),
-       gene_expression={
-           "ErbB1": ["EGFR"],
-           "ErbB2": ["ERBB2"],
-           "ErbB3": ["ERBB3"],
-           "ErbB4": ["ERBB4"],
-           "Grb2": ["GRB2"],
-           "Shc": ["SHC1", "SHC2", "SHC3", "SHC4"],
-           "RasGAP": ["RASA1", "RASA2", "RASA3"],
-           "PI3K": ["PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG"],
-           "PTEN": ["PTEN"],
-           "SOS": ["SOS1", "SOS2"],
-           "Gab1": ["GAB1"],
-           "RasGDP": ["HRAS", "KRAS", "NRAS"],
-           "Raf": ["ARAF", "BRAF", "RAF1"],
-           "MEK": ["MAP2K1", "MAP2K2"],
-           "ERK": ["MAPK1", "MAPK3"],
-           "Akt": ["AKT1", "AKT2"],
-           "PTP1B": ["PTPN1"],
-           "GSK3b": ["GSK3B"],
-           "DUSP": ["DUSP5", "DUSP6", "DUSP7"],
-           "cMyc": ["MYC"],
-       },
-       read_csv_kws={"index_col": "Description"}
-   )
-
-   ...
-
-   def update(self, indiv):
-       x = param_values()
-       y0 = initial_values()
-
-       for i, j in enumerate(self.idx_params):
-           x[j] = indiv[i]
-       for i, j in enumerate(self.idx_initials):
-           y0[j] = indiv[i + len(self.idx_params)]
-
-       # As maximal transcription rate
-       x[C.V291] = incorporating_gene_expression_levels.as_reaction_rate(
-           __path__[0].split(os.sep)[-1], x, "V291", "DUSP"
-       )
-       x[C.V310] = incorporating_gene_expression_levels.as_reaction_rate(
-           __path__[0].split(os.sep)[-1], x, "V310", "cMyc"
-       )
-       # As initial conditions
-       y0 = incorporating_gene_expression_levels.as_initial_conditions(
-           __path__[0].split(os.sep)[-1], x, y0
-       )
-
-       ...
-   ```
+    ```python
+    from pasmopy import Model
+    from pasmopy.preprocessing import WeightingFactors
+
+    from models import erbb_network
+
+
+    model = Model(erbb_network.__package__).create()
+
+    gene_expression = {
+        "ErbB1": ["EGFR"],
+        "ErbB2": ["ERBB2"],
+        "ErbB3": ["ERBB3"],
+        "ErbB4": ["ERBB4"],
+        "Grb2": ["GRB2"],
+        "Shc": ["SHC1", "SHC2", "SHC3", "SHC4"],
+        "RasGAP": ["RASA1", "RASA2", "RASA3"],
+        "PI3K": ["PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG"],
+        "PTEN": ["PTEN"],
+        "SOS": ["SOS1", "SOS2"],
+        "Gab1": ["GAB1"],
+        "RasGDP": ["HRAS", "KRAS", "NRAS"],
+        "Raf": ["ARAF", "BRAF", "RAF1"],
+        "MEK": ["MAP2K1", "MAP2K2"],
+        "ERK": ["MAPK1", "MAPK3"],
+        "Akt": ["AKT1", "AKT2"],
+        "PTP1B": ["PTPN1"],
+        "GSK3b": ["GSK3B"],
+        "DUSP": ["DUSP5", "DUSP6", "DUSP7"],
+        "cMyc": ["MYC"],
+    }
+
+    weighting_factors = WeightingFactors(model, gene_expression)
+    weighting_factors.add_to_params()
+    weighting_factors.set_search_bounds()
+    ```
+
+1. Edit `SearchParam` class
+
+    ```python
+    import os
+
+    import numpy as np
+
+    from pasmopy import Individualization
+
+    from . import __path__
+    from .name2idx import C, V
+    from .set_model import initial_values, param_values
+
+
+    incorporating_gene_expression_levels = Individualization(
+        parameters=C.NAMES,
+        species=V.NAMES,
+        transcriptomic_data=os.path.join("transcriptomic_data", "TPM_RLE_postComBat_BRCA_BREAST.csv"),
+        gene_expression={
+            "ErbB1": ["EGFR"],
+            "ErbB2": ["ERBB2"],
+            "ErbB3": ["ERBB3"],
+            "ErbB4": ["ERBB4"],
+            "Grb2": ["GRB2"],
+            "Shc": ["SHC1", "SHC2", "SHC3", "SHC4"],
+            "RasGAP": ["RASA1", "RASA2", "RASA3"],
+            "PI3K": ["PIK3CA", "PIK3CB", "PIK3CD", "PIK3CG"],
+            "PTEN": ["PTEN"],
+            "SOS": ["SOS1", "SOS2"],
+            "Gab1": ["GAB1"],
+            "RasGDP": ["HRAS", "KRAS", "NRAS"],
+            "Raf": ["ARAF", "BRAF", "RAF1"],
+            "MEK": ["MAP2K1", "MAP2K2"],
+            "ERK": ["MAPK1", "MAPK3"],
+            "Akt": ["AKT1", "AKT2"],
+            "PTP1B": ["PTPN1"],
+            "GSK3b": ["GSK3B"],
+            "DUSP": ["DUSP5", "DUSP6", "DUSP7"],
+            "cMyc": ["MYC"],
+        },
+        read_csv_kws={"index_col": "Description"}
+    )
+
+    ...
+
+    class SearchParam(object):
+
+        ...
+
+        def update(self, indiv):
+            x = param_values()
+            y0 = initial_values()
+
+            for i, j in enumerate(self.idx_params):
+                x[j] = indiv[i]
+            for i, j in enumerate(self.idx_initials):
+                y0[j] = indiv[i + len(self.idx_params)]
+
+            # As maximal transcription rate
+            x[C.V291] = incorporating_gene_expression_levels.as_reaction_rate(
+                __path__[0].split(os.sep)[-1], x, "V291", "DUSP"
+            )
+            x[C.V310] = incorporating_gene_expression_levels.as_reaction_rate(
+                __path__[0].split(os.sep)[-1], x, "V310", "cMyc"
+            )
+            # As initial conditions
+            y0 = incorporating_gene_expression_levels.as_initial_conditions(
+                __path__[0].split(os.sep)[-1], x, y0
+            )
+
+            ...
+    ```
diff --git a/docs/requirements.md b/docs/requirements.md
@@ -18,29 +18,41 @@ The following packages are required for transcriptomic data integration, paramet
 
 ### Python:
 
-- [pasmopy==0.1.0](https://github.com/pasmopy/pasmopy)
-- [biomass>=0.5.2,<0.6](https://github.com/biomass-dev/biomass)
-- matplotlib==3.3.4
-- numpy==1.19.2
-- pandas==1.2.4
-- seaborn==0.11.2
-- scipy==1.6.0
+```
+$ pip install -r requirements.txt
+```
+
+> - [pasmopy==0.1.0](https://github.com/pasmopy/pasmopy)
+> - [biomass>=0.5.2,<0.6](https://github.com/biomass-dev/biomass)
+> - [matplotlib==3.3.4](https://matplotlib.org)
+> - [numpy==1.19.2](https://numpy.org)
+> - [pandas==1.2.4](https://pandas.pydata.org)
+> - [seaborn==0.11.2](https://seaborn.pydata.org)
+> - [scipy==1.6.0](https://scipy.org)
 
 ### Julia:
 
-- [BioMASS.jl==0.5.0](https://github.com/biomass-dev/BioMASS.jl)
+```shell
+$ julia
+julia> ] add BioMASS @0.5.0
+```
+
+> - [BioMASS.jl==0.5.0](https://github.com/biomass-dev/BioMASS.jl)
 
 ### R:
 
-- TCGAbiolinks
-- sva
-- biomaRt
-- ComplexHeatmap
-- viridisLite
-- dplyr
-- edgeR
-- sva
-- tibble
-- data.table
-- stringr
-- biomaRt
+```shell
+$ R
+> source("install_requirements.R")
+```
+
+> - [TCGAbiolinks](https://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html)
+> - [sva](https://bioconductor.org/packages/release/bioc/html/sva.html)
+> - [biomaRt](https://bioconductor.org/packages/release/bioc/html/biomaRt.html)
+> - [ComplexHeatmap](https://www.bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html)
+> - [viridisLite](https://github.com/sjmgarnier/viridisLite)
+> - [dplyr](https://dplyr.tidyverse.org)
+> - [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html)
+> - [tibble](https://tibble.tidyverse.org)
+> - [data.table](https://github.com/Rdatatable/data.table)
+> - [stringr](https://stringr.tidyverse.org)