Package: biolearns
biolearnsBioLearns: Computational Biology and Bioinformatics Toolbox in Pythonhttp://biolearns.medicine.iu.eduInstallationFrom PyPIpipinstallbiolearns-UDocumentation and TutorialsWe select three examples listed below. For full list of tutorial, check our github wiki page:WikiDisclaimerPlease note that this is a pre-release version of the BioLearns which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an "as is" and "as available" basis. BioLearns does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. BioLearns will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.1. Read TCGA DataExample: Read TCGA Breast invasive carcinoma (BRCA) dataData is downloaded directly fromhttps://gdac.broadinstitute.org/.
The results here are in whole or part based upon data generated by
the TCGA Research Network:https://www.cancer.gov/tcga.frombiolearns.datasetimportTCGAbrca=TCGA('BRCA')mRNAseq=brca.mRNAseqclinical=brca.clinicalTCGA cancer table shortcut:BarcodeCancer full nameVersion1ACCAdrenocortical carcinoma2016_01_282BLCABladder urothelial carcinoma2016_01_283BRCABreast invasive carcinoma2016_01_284CESCCervical and endocervical cancers2016_01_285CHOLCholangiocarcinoma2016_01_286COADColon adenocarcinoma2016_01_287COADREADColorectal adenocarcinoma2016_01_288DLBCLymphoid Neoplasm Diffuse Large B-cell Lymphoma2016_01_289ESCAEsophageal carcinoma2016_01_28............2. Gene Co-expression AnalysisWe firstly download and access the mRNAseq data.frombiolearns.datasetimportTCGAbrca=TCGA('BRCA')mRNAseq=brca.mRNAseqmRNAseq data is noisy. We filter out 50% of genes with lowest mean values, and then filter out 50% remained genes with lowest variance values.frombiolearns.preprocessingimportexpression_filtermRNAseq=expression_filter(mRNAseq,meanq=0.5,varq=0.5)We then use lmQCM class to create an lmQCM objectlobj.The gene co-expression analysis is performed by simply call thefit()function.frombiolearns.coexpressionimportlmQCMlobj=lmQCM(mRNAseq)clusters,genes,eigengene_mat=lobj.fit()3. Univariate survival analysisWe firstly download and access the mRNAseq data. Use breast cancer as an example.frombiolearns.datasetimportTCGAbrca=TCGA('BRCA')mRNAseq=brca.mRNAseqWe import logranktest from survival subpackage. Choose gene "ABLIM3" as the univariate input.frombiolearns.survivalimportlogranktestr=mRNAseq.loc['ABLIM3',].valuesWe find the intersection of univariate, time, and event databcd_m=[b[:12]forbinmRNAseq.columns]bcd_p=[b[:12]forbinclinical.index]bcd=np.intersect1d(bcd_m,bcd_p)r=r[np.nonzero(np.in1d(bcd,bcd_m))[0]]t=brca.overall_survival_time[np.nonzero(np.in1d(bcd,bcd_p))[0]]e=brca.overall_survival_event[np.nonzero(np.in1d(bcd,bcd_p))[0]]We perform log-rank test:logrank_results,fig=logranktest(r[~np.isnan(t)],t[~np.isnan(t)],e[~np.isnan(t)])test_statistic,p_value=logrank_results.test_statistic,logrank_results.p_valueThe output figure looks like: