Metadata-Version: 2.1
Name: riptide
Version: 3.0.3
Summary: Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe)
Home-page: https://github.com/mjenior/riptide
Author: Matthew Jenior
Author-email: mattjenior@gmail.com
License: MIT
Description: # RIPTiDe
        
        **R**eaction **I**nclusion by **P**arsimony and **T**ranscript **D**istribution
        
        ----
        
        Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches have been shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. This method, known as RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.
        
        Please cite when using:
        ```
        Jenior ML, Moutinho Jr TJ, Dougherty BV, & Papin JA. (2020). Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. PLOS Comp Biol. 
        ```
        
        ## Dependencies
        ```
        >=python-3.6.4
        >=cobra-0.15.3
        >=pandas-0.24.1
        >=symengine-0.4.0
        >=scipy-1.3.0
        ```
        
        ## Installation
        
        Installation is:
        ```
        $ pip install riptide
        ```
        
        ### Arguments for core RIPTiDe functions:
        
        **riptide.read_transcription_file() - Generates dictionary of transcriptomic abundances from a file**
        ```
        REQUIRED
        file : string
            User-provided file name which contains gene IDs and associated transcription values
        
        OPTIONAL
        header : boolean
            Defines if read abundance file has a header that needs to be ignored
            Default is no header
        replicates : boolean
            Defines if read abundances contains replicates and medians require calculation
            Default is no replicates (False)
        sep: string
            Defines what character separates entries on each line
            Defaults to tab (.tsv)
        rarefy : bool
            Rarefies rounded transcript abundances to 90% of the smallest replicate
            Default is False
        level : int
            Level by which to rarefy samples
            Default is 100000
        binning : boolean
            Perform discrete binning of transcript abundances into quantiles
            OPTIONAL, not advised
            Default is False
        quant_max : float
            Largest quantile to consider
            Default is 0.9
        quant_min : float
            Smallest quantile to consider
            Default is 0.5
        step : float
            Step size for parsing quantiles
            Default is 0.125
        norm : bool
            Normalize transcript abundances using RPM calculation
            Performed by default
        factor : numeric
            Denominator for read normalization calculation
            Default is 1e6 (RPM)
        ```
        
        **riptide.contextualize() - Create context-specific model based on transcript distribution**
        ```
        REQUIRED
        model : cobra.Model
            The model to be contextualized
        
        OPTIONAL
        transcriptome : dictionary
            Dictionary of transcript abundances, output of read_transcription_file()
            With default, an artifical transcriptome is generated where all abundances equal 1.0
        samples : int 
            Number of flux samples to collect
            Default is 500
        silent  : bool
            Silences std out 
            Default is False
        exch_weight : bool
            Weight exchange reactions the same as adjacent transporters
            Default is True
        processes : int
            The number of parallel processes to run for FVA. Optional and if not passed,
            will be set to the number of CPUs found. Necessary to change if
            your trying to run paralell instance of RIPTiDe on the same machine
            Default is none
        fraction : float
            Minimum percent of optimal objective value during FBA steps
            Default is 0.8
        minimum : float
            Minimum linear coefficient allowed during weight calculation for pFBA
            Default is None
        conservative : bool
            Conservatively remove inactive reactions based on GPR rules (all member reactions must be inactive to prune)
            Default is False
        objective : bool
            Sets previous objective function as a constraint with minimum flux equal to user input fraction
            Default is True
        additive : bool
            Pool transcription abundances for reactions with multiple contributing gene products
            Default is False
        essential : list
            List of gene or reaction ID strings for which the highest weights are assigned regardless of transcription
            Default is False
        set_bounds : bool
            Uses flux variability analysis results from constrained model to set new bounds for all reactions
            Default is True
        tasks : list
            List of gene or reaction ID strings for forced inclusion in final model (metabolic tasks or essential genes)
        exclude : list
            List of reaction ID strings for forced exclusion from final model
        gpr : bool
            Determines if GPR rules will be considered during coefficient assignment
            Default is False
        threshold : float
            Minimum flux a reaction must acheive in order to avoid pruning during flux sum minimization step
            Default is 1e-6
        defined : False or list
            User defined range of linear coeffients, needs to be defined in a list like [1, 0.5, 0.1, 0.01, 0.001]
            Works best paired with binned abundance catagories from riptide.read_transcription_file()
            Default is False
        open_exchanges : bool
            Sets all exchange reactions bounds to (-1000., 1000)
            Default is False
        ```
        
        **riptide.save_output() - Writes RIPTiDe results to files in a new directory**
        ```
        REQUIRED
        riptide_obj : RIPTiDe object
            Class object creared by riptide.contextualize()
        
        OPTIONAL
        path : str
            New directory to write output files
        file_type : str
            Type of output file for RIPTiDe model
            Accepts either sbml or json
            Default is SBML
        ```
        
        **riptide.maxfit_contextualize() - Iterative RIPTiDe for a range of minimum objective fluxes, returns model with best fit to transcriptome**
        ```
        REQUIRED
        model : cobra.Model
            The model to be contextualized
        transcriptome : dictionary
            Dictionary of transcript abundances, output of read_transcription_file()
        samples : int 
            Number of flux samples to collect
            Default is 500
        frac_min : float
            Lower bound for range of minimal fractions to test
            Default is 0.65
        frac_max : float
            Upper bound for range of minimal fractions to test
            Default is 0.85
        frac_step : float
            Increment to parse input minimal fraction range
            Default is 0.02
        first_max : bool
            Exits early if next subsequent iteration has a worse correlation
            Default is True
        
        OPTIONAL
            All other optional parameters for riptide.contextualize()
        '''
        ```
        
        
        ## Usage
        
        **Comments before starting:** 
        - Make sure that genes in the transcriptome file matches those that are in your model.
        - Check the example files for proper data formatting
        - Binning genes into discrete thresholds for coefficient assignment is available in riptide.read_transcription_file() (not recommended)
        - Opening the majority of exchange reactions (bounds = +/- 1000) may yeild better prediction when extracellular conditions are unknown
        - The resulting RIPTiDe object has multiple properties including the context-specific model and flux analyses, accessing each is described below
        
        ```python
        from riptide import *
        
        my_model = cobra.io.read_sbml_model('examples/genre.sbml')
        
        transcript_abundances_1 = riptide.read_transcription_file('examples/transcriptome1.tsv')
        transcript_abundances_2 = riptide.read_transcription_file('examples/transcriptome2.tsv', replicates=True)
        
        riptide_object_1_a = riptide.contextualize(model=my_model, transcriptome=transcript_abundances_1)
        riptide_object_1_b = riptide.contextualize(model=my_model, transcriptome=transcript_abundances_1, tasks=['rxn1'], exclude=['rxn2','rxn3'])
        
        riptide_object_2_maxfit = riptide.maxfit_contextualize(model=my_model, transcriptome=transcript_abundances_2)
        
        riptide.save_output(riptide_obj=riptide_object_1_a, path='~/Desktop/riptide_output')
        ``` 
        
        ### Example riptide.contextualize() stdout report:
        ```
        
        Initializing model and integrating transcriptomic data...
        Pruning zero flux subnetworks...
        Analyzing context-specific flux distributions...
        
        Reactions pruned to 285 from 1129 (74.76% change)
        Metabolites pruned to 285 from 1132 (74.82% change)
        Flux through the objective DECREASED to ~54.71 from ~65.43 (16.38% change)
        Context-specific metabolism correlates with transcriptome (r=0.149, p=0.011 *)
        
        RIPTiDe completed in 17 seconds
        
        ```
        
        In the final step, RIPTiDe assesses the fit of transcriptomic data for the calculated network activity through correlation of transcript abundance and median flux value for each corresponding reaction. The Spearman correlation coefficient and associated p-value are the reported following the fraction of network topology that is pruned during the flux minimization step.
        
        ### Example riptide.maxfit_contextualize() stdout report:
        ```
        
        Running max fit RIPTiDe for objective fraction range: 0.65 to 0.85 with intervals of 0.02 
        
        Iter 1 of 10 | frac = 0.65 | rho = 0.15 ; p = 0.008
        Iter 2 of 10 | frac = 0.67 | rho = 0.159 ; p = 0.005
        Iter 3 of 10 | frac = 0.69 | rho = 0.165 ; p = 0.004
        Iter 4 of 10 | frac = 0.71 | rho = 0.186 ; p = 0.001
        Top correlation found, exiting search...
        
        Context-specific metabolism best fit with 0.71 of optimal objective flux
        
        Max fit RIPTiDe completed in 1 minute and 32 seconds
        
        ```
        
        Max fit RIPTiDe tests all minimum objective flux fractions over the provided range and returns only the model with the best Spearman correlation between context-specific flux for reactions and the associated transcriptomic values. The search is terminated if a subsequent iteration has a lower correlation coefficient than the last, however this could be a local maxima and must be considered if an exhaustive analysis is preferred.
        
        ### Resulting RIPTiDe object (class) properties:
        The resulting object is a container for the following data structures.
        
        - **model** - Contextualized genome-scale metabolic network reconstruction
        - **transcriptome** - Transcriptomic abundances provided by user
        - **percent_of_mapping** - Percent of genes in mapping file found in input GENRE
        - **minimization_coefficients** - Linear coefficients used during flux sum minimization
        - **maximization_coefficients** - Linear coefficients for each reaction based used during flux sampling
        - **pruned** - Dictionary containing the IDs of genes, reactions, and metabolites pruned by RIPTiDe
        - **flux_samples** - Flux samples from constrained model
        - **flux_variability** - Flux variability analysis from constrained model
        - **fraction_of_optimum** - Minimum specified percentage of optimal objective flux during contextualization
        - **metabolic_tasks** - User defined reactions whose activity is saved from pruning
        - **concordance** - Spearman correlation results between linear coefficients and median fluxes from sampling
        - **gpr_integration** - Whether GPR rules were considered during assignment of linear coefficients
        - **defined_coefficients** - Range of linear coefficients RIPTiDe is allowed to utilize provided as a list
        - **included_important** - Reactions or Genes included in the final model which the user defined as important
        - **additional_parameters** - Dictionary of additional parameters RIPTiDe uses
        
        ### Additional maxfit-only RIPTiDe object (class) properties:
        
        - **fraction_bounds** - Minimum and maximum values for the range of objective flux minimum fractions tested
        - **fraction_step** - Increment for series of objective flux minima created within fraction bound range
        
        **Examples of accessing components of RIPTiDe output:**
        ```python
        context_specific_GENRE = riptide_object.model
        context_specific_FVA = riptide_object.flux_variability
        context_specific_flux_samples = riptide_object.flux_samples
        ```
        
        ## Additional Information
        
        Thank you for your interest in RIPTiDe, for additional questions please email mljenior@virginia.edu.
        
        If you encounter any problems, please [file an issue](https://github.com/mjenior/riptide/issues) along with a detailed description.
        
        Distributed under the terms of the [MIT](http://opensource.org/licenses/MIT) license, "riptide" is free and open source software
        
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