impetuous-gfa 0.51.1

Impetuous Quantification, a Statistical Learning library for Humans : Alignments, Clustering, Enrichments and Group Analysis

A Statistical Learning library for Humans

This toolkit currently offers enrichment analysis, hierarchical enrichment analysis, novel PLS regression, shape alignment, connectivity clustering, clustering and hierarchical clustering as well as factor analysis methods.

The fine grained data can be studied via a statistical tests that relates it to observables in a coarse grained journal file. The final p values can then FDR corrected by employing a novel q-value method.

Visit the active code via : https://github.com/richardtjornhammar/impetuous

Visit the published code : https://doi.org/10.5281/zenodo.2594690

Cite using : DOI: 10.5281/zenodo.2594690

Pip installation with :

pip install impetuous-gfa

Version controlled installation of the Impetuous library

The Impetuous library

In order to run these code snippets we recommend that you download the nix package manager. Nix package manager links from Oktober 2020:

https://nixos.org/download.html

$ curl -L https://nixos.org/nix/install | sh

If you cannot install it using your Wintendo then please consider installing Windows Subsystem for Linux first:

https://docs.microsoft.com/en-us/windows/wsl/install-win10

In order to run the code in this notebook you must enter a sensible working environment. Don't worry! We have created one for you. It's version controlled against python3.7 and you can get the file here:

https://github.com/richardtjornhammar/rixcfgs/blob/master/code/environments/impetuous-shell.nix

Since you have installed Nix as well as WSL, or use a Linux (NixOS) or bsd like system, you should be able to execute the following command in a termnial:

$ nix-shell impetuous-shell.nix

Now you should be able to start your jupyter notebook locally:

$ jupyter-notebook impetuous_finance.ipynb

and that's it.

Usage example 1 : elaborate informatics

code: https://gitlab.com/stochasticdynamics/eplsmta-experiments docs: https://arxiv.org/pdf/2001.06544.pdf

Usage example 2 : simple regression code

Now while in a good environment: In your Jupyter notebook or just in a dedicated file.py you can write the following:

import pandas as pd
import numpy as np

import impetuous.quantification as impq

analyte_df = pd.read_csv( 'analytes.csv' , '\t' , index_col=0 )
journal_df = pd.read_csv( 'journal.csv'  , '\t' , index_col=0 )

formula = 'S ~ C(industry) : C(block) + C(industry) + C(block)'

res_dfs 	= impq.run_rpls_regression ( analyte_df , journal_df , formula , owner_by = 'angle' )
results_lookup	= impq.assign_quality_measures( journal_df , res_dfs , formula )

print ( results_lookup )
print ( res_dfs )

Usage example 3 : Novel NLP sequence alignment

Finding a word in a text is a simple and trivial problem in computer science. However matching a sequence of characters to a larger text segment is not. In this example you will be shown how to employ the impetuous text fitting procedure. The strength of the fit is conveyed via the returned score, higher being a stronger match between the two texts. This becomes costly for large texts and we thus break the text into segments and words. If there is a strong word to word match then the entire segment score is calculated. The off and main diagonal power terms refer to how to evaluate a string shift. Fortinbras and Faortinbraaks are probably the same word eventhough the latter has two character shifts in it. In this example both "requests" and "BeautifulSoup" are employed to parse internet text.

import numpy as np
import pandas as pd

import impetuous.fit as impf    # THE IMPETUOUS FIT MODULE
                                # CONTAINS SCORE ALIGNMENT ROUTINE

import requests                 # FOR MAKING URL REQUESTS
from bs4 import BeautifulSoup   # FOR PARSING URL REQUEST CONTENT

if __name__ == '__main__' :

    print ( 'DOING TEXT SCORING VIA MY SEQUENCE ALIGNMENT ALGORITHM' )
    url_       = 'http://shakespeare.mit.edu/hamlet/full.html'

    response   = requests.get( url_ )
    bs_content = BeautifulSoup ( response.content , features="html.parser")

    name = 'fortinbras'
    score_co = 500
    S , S2 , N = 0 , 0 , 0
    for btext in bs_content.find_all('blockquote'):

        theTextSection = btext.get_text()
        theText        = theTextSection.split('\n')

        for segment in theText:
            pieces = segment.split(' ')
            if len(pieces)>1 :
                for piece in pieces :
                    if len(piece)>1 :
                        score = impf.score_alignment( [ name , piece ],
                                    main_diagonal_power = 3.5, shift_allowance=2,
                                    off_diagonal_power = [1.5,0.5] )
                        S    += score
                        S2   += score*score
                        N    += 1
                        if score > score_co :
                            print ( "" )
                            print ( score,name,piece )
                            print ( theTextSection )
                            print ( impf.score_alignment( [ name , theTextSection ],
                                        main_diagonal_power = 3.5, shift_allowance=2,
                                        off_diagonal_power = [1.5,0.5] ) )
                            print ( "" )

    print ( S/N )
    print ( S2/N-S*S/N/N )

Usage example 4 : Diabetes analysis

Here we show how to use a novel multifactor method on a diabetes data set to deduce important transcripts with respect to being diabetic. The data was obtained from the Broad Insitute and contains gene expressions from a microarray hgu133a platform. We choose to employ the Diabetes_collapsed_symbols.gct file since it has already been collapsed down to useful transcripts. We have entered an impetuous-gfa ( version >= 0.50.0 ) environment and set up the a diabetes.py file with the follwing code content:

import pandas as pd
import numpy as np

if __name__ == '__main__' :
    analyte_df = pd.read_csv('../data/Diabetes_collapsed_symbols.gct','\t', index_col=0, header=2).iloc[:,1:]

In order to illustrate the use of low value supression we use the reducer module. A tanh based soft max function is employed by the confred function to supress values lower than the median of the entire sample series for each sample.

    from impetuous.reducer import get_procentile,confred
    for i_ in range(len(analyte_df.columns.values)):
        vals   = analyte_df.iloc[:,i_].values
        eta    = get_procentile( vals,50 )
        varpi  = get_procentile( vals,66 ) - get_procentile( vals,33 )
        analyte_df.iloc[:,i_] = confred(vals,eta,varpi)

    print ( analyte_df )

The data now contain samples along the columns and gene transcript symbols along the rows where the original values have been quenched with low value supression. The table have the following appearance

NAME	NGT_mm12_10591	...	DM2_mm81_10199
215538_at	16.826041	...	31.764484
...
LDLR	19.261185	...	30.004612

We proceed to write a journal data frame by adding the following lines to our code

    journal_df = pd.DataFrame([ v.split('_')[0] for v in analyte_df.columns] , columns=['Status'] , index = analyte_df.columns.values ).T
    print ( journal_df )

which will produce the following journal table :

	NGT_mm12_10591	...	DM2_mm81_10199
Status	NGT	...	DM2

Now we check if there are aggregation tendencies among these two groups prior to the multifactor analysis. We could use the hierarchical clustering algorithm, but refrain from it and instead use the associations method together with the connectivity clustering algorithm. The associations can be thought of as a type of ranked correlations similar to spearman correlations. If two samples are strongly associated with each other they will be close to 1 (or -1 if they are anti associated). Since they are all humans, with many transcript features, the values will be close to 1. After recasting the associations into distances we can determine if two samples are connected at a given distance by using the connectivity routine. All connected points are then grouped into technical clusters, or batches, and added to the journal.

    from impetuous.quantification import associations
    ranked_similarity_df = associations ( analyte_df .T )
    sample_distances = ( 1 - ranked_similarity_df ) * 2.

    from impetuous.clustering import connectivity
    cluster_ids = [ 'B'+str(c[0]) for c in connectivity( sample_distances.values , 5.0E-2 )[1] ]
    print ( cluster_ids )

    journal_df .loc['Batches'] = cluster_ids

which will produce a cluster list containing 13 batches with members whom are Normal Glucose Tolerant or have Diabetes Mellitus 2. We write down the formula for deducing which genes are best at recreating the diabetic state and batch identities by writing:

    formula = 'f~C(Status)+C(Batches)'

The multifactor method calculates how to produce an encoded version of the journal data frame given an analyte data set. It does this by forming the psuedo inverse matrix that best describes the inverse of the analyte frame and then calculates the dot product of the inverse with the encoded journal data frame. This yields the coefficient frame needed to solve for the numerical encoding frame. The method has many nice statistical properties that we will not discuss further here. The first thing that the multifactor method does is to create the encoded data frame. The encoded data frame for this problem can be obtained with the following code snippet

    encoded_df = create_encoding_data_frame ( journal_df , formula ).T
    print ( encoded_df )

and it will look something like this

	NGT_mm12_10591	...	DM2_mm81_10199
B10	0.0	...	0.0
B5	0.0	...	0.0
B12	0.0	...	1.0
B2	0.0	...	0.0
B11	1.0	...	0.0
B8	0.0	...	0.0
B1	0.0	...	0.0
B7	0.0	...	0.0
B4	0.0	...	0.0
B0	0.0	...	0.0
B6	0.0	...	0.0
B9	0.0	...	0.0
B3	0.0	...	0.0
NGT	1.0	...	0.0
DM2	0.0	...	1.0

This encoded dataframe can be used to calculate statistical parameters or solve other linear equations. Take the fast calculation of the mean gene expressions across all groups as an example

    print ( pd .DataFrame ( np.dot( encoded_df,analyte_df.T ) ,
                          columns = analyte_df .index ,
                          index   = encoded_df .index ) .apply ( lambda x:x/np.sum(encoded_df,1) ) )

which will immediately calculate the mean values of all transcripts across all different groups.

The multifactor_evaluation calculates the coefficients that best recreates the encoded journal by employing the psudo inverse of the analyte frame utlizing Singular Value Decomposition. The beta coefficients are then evaluated using a normal distribution assumption to obtain p values and rank corrected q values are also returned. The full function can be called with the following code

    from impetuous.quantification import multifactor_evaluation
    multifactor_results = multifactor_evaluation ( analyte_df , journal_df , formula )

    print ( multifactor_results.sort_values('DM2,q').iloc[:25,:].index.values  )

which tells us that the genes

['MYH2' 'RPL39' 'HBG1 /// HBG2' 'DDN' 'UBC' 'RPS18' 'ACTC' 'HBA2' 'GAPD'
 'ANKRD2' 'NEB' 'MYL2' 'MT1H' 'KPNA4' 'CA3' 'RPLP2' 'MRLC2 /// MRCL3'
 '211074_at' 'SLC25A16' 'KBTBD10' 'HSPA2' 'LDHB' 'COX7B' 'COX7A1' 'APOD']

have something to do with the altered metabolism in Type 2 Diabetics. We could now proceed to use the hierarchical enrichment routine to understand what that something is, but first we save the data

    multifactor_results.to_csv('multifactor_dm2.csv','\t')

Example 5 : Understanding what it means

If you have a well curated .gmt file that contains analyte ids as unique sets that belong to different groups then you can check whether or not a specific group seems significant with respect to all of the significant and insignificant analytes that you just calculated. One can derive such a hierarchy or rely on already curated information. Since we are dealing with genes and biologist generally have strong opinions about these things we go to a directed acyclic knowledge graph called Reactome and translate that information into a set of files that we can use to build our own knowledge hierarchy. After downloading that .zip file (and unzipping) you will be able to execute the following code

import pandas as pd
import numpy as np

if __name__=='__main__':
    import impetuous.pathways as impw
    impw.description()

which will blurt out code you can use as inspiration to generate the Reactome knowledge hierarchy. So now we do that

    paths = impw.Reactome( './Ensembl2Reactome_All_Levels_v71.txt' )

but we also need to translate the gene ids into the correct format so we employ BioMart. To obtain the conversion text file we select Human genes GRCh38.p13 and choose attributes Gene stable ID, Gene name and Gene Synonym and save the file as biomart.txt.

    biomart_dictionary = {}
    with open('biomart.txt','r') as input:
        for line in input :
            lsp = line.split('\n')[0].split('\t')
            biomart_dictionary[lsp[0]] = [ n for n in lsp[1:] if len(n)>0 ]
    paths.add_pathway_synonyms( synonym_dict=biomart_dictionary )

    paths .make_gmt_pathway_file( './reactome_v71.gmt' )

Now we are almost ready to conduct the hierarchical pathway enrichment, to see what cellular processes are significant with respect to our gene discoveries, but we still need to build the Directed Acyclic Graph (DAG) from the parent child file and the pathway definitions.

    import impetuous.hierarchical as imph
    dag_df , tree = imph.create_dag_representation_df ( pathway_file = './reactome_v71.gmt',
                                                        pcfile = './NewestReactomeNodeRelations.txt' )

We will use it in the HierarchicalEnrichment routine later in order not to double count genes that have already contributed at lower levels of the hierarchy. Now where did we store those gene results...

    quantified_analyte_df = pd.read_csv('multifactor_dm2.csv','\t',index_col=0)
    a_very_significant_cutoff = 1E-10

    enrichment_results = imph.HierarchicalEnrichment ( quantified_analyte_df , dag_df , 
                                  ancestors_id_label = 'DAG,ancestors' , dag_level_label = 'DAG,level' ,
                                  threshold = a_very_significant_cutoff ,
                                  p_label = 'DM2,q' )

lets see what came out on top!

    print( enrichment_results.sort_values('Hierarchical,p').loc[:,['description','Hierarchical,p']].iloc[0,:] )

which will report that

description	Striated Muscle Contraction
Hierarchical,p	6.55459e-05
Name:	R-HSA-390522

is affected or perhaps needs to be compensated for... now perhaps you thought this exercise was a tad tedious? Well you are correct. It is and you could just as well have copied the gene transcripts into String-db and gotten similar results out. But, then you wouldn't have gotten to use the hierarchical enrichment method I invented!

These examples were meant as illustrations of some of the codes implemented in the impetuous-gfa package.

Manually updated code backups for this library :

GitLab: https://gitlab.com/richardtjornhammar/impetuous

CSDN: https://codechina.csdn.net/m0_52121311/impetuous