"<a href=\"https://www.ncbi.nlm.nih.gov/pubmed/" + lid + "\">" + lid + "</a>" if not lid.startswith('PMC') else "<a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/" + lid + "\">" + lid + "</a>")), 'MIRECORD': ", ".join( makeLinks( mirecordEvs, lambda lid: "<a href=\"https://www.ncbi.nlm.nih.gov/pubmed/" + lid + "\">" + lid + "</a>")), 'MIRTARBASE': ", ".join(mirtarbaseEvs), 'MIRWALK': ", ".join(mirwalkEvs) } row = DataRow.fromDict(addRow) linkedDF.addRow(row) print("Total Additional miRNAs", totalAdditional) missingDF.export( "/mnt/c/ownCloud/data/miRExplore/overview_no_mirwalk_sase.xlsx", ExportTYPE.XLSX) linkedDF.export( "/mnt/c/ownCloud/data/miRExplore/overview_no_mirwalk_sase.html", ExportTYPE.HTML) #CytoscapeGrapher.showGraph(graph, location=dirTemp, name='chemokines', title='Chemokines', nodeLabel=lambda x: x, edgeLabel=lambda x: '')
'population_size': populationSize, 'success_population': numSuccInPopulation, 'sample_size': sampleSize, 'success_samples': drawnSuccesses, 'pval': pval, 'sample_success_fraction': fractionOfHitSamples, 'genes': ";".join(successIntersection), 'direction': direction } setToResult[setElem] = resultObj sortedElems = [x for x in setToResult] elemPvals = [setToResult[x]["pval"] for x in sortedElems] rej, elemAdjPvals, _, _ = multipletests(elemPvals, alpha=0.05, method='fdr_bh', is_sorted=False, returnsorted=False) for eidx, elem in enumerate(sortedElems): assert (setToResult[elem]['pval'] == elemPvals[eidx]) setToResult[elem]['adj_pval'] = elemAdjPvals[eidx] for elem in sortedElems: dr = DataRow.fromDict(setToResult[elem]) outdf.addRow(dr) outdf.export(args.output.name)
if args.fpkm: #print(curGeneID, geneLength) fpkmValue = row[sample] / (sample2total[sample] * geneLength) * pow(10, 9) rowDict[sample + ".FPKM"] = fpkmValue if args.tpm: tpmValue = row[sample] / (geneLength * sample2ratio[sample]) * pow(10, 6) rowDict[sample + ".TPM"] = tpmValue allRowUpdates.append(rowDict) allCols = set() for x in allRowUpdates: for y in x: if not y in featureCountsColumns: allCols.add(y) outdf.addColumns(featureCountsColumns) outdf.addColumns(sorted(allCols), default=0, ignoreDuplicates=True) outdf.updateRowIndexed("Geneid", allRowUpdates, ignoreMissingCols=True, addIfNotFound=True) outdf.export(args.output.name, exType=ExportTYPE.TSV)
data[x] = row[x] for x in df1Col2New: data[df1Col2New[x]] = row[x] id2dataDf[data["id"]] = data for row in indf2: data = id2dataDf.get(row["id"], {}) for x in df12CommonCols: data[x] = row[x] for x in df2UniqueCols + df2SampleCols: if args.prefix_counts: data[args.prefix2 + "_" + x] = row[x] else: data[x] = row[x] for x in df2Col2New: data[df2Col2New[x]] = row[x] allRowUpdates = [id2dataDf[x] for x in sorted([y for y in id2dataDf])] outdf.updateRowIndexed("id", allRowUpdates, ignoreMissingCols=True, addIfNotFound=True) outdf.export(args.output.name, ExportTYPE.TSV)
dataDict = { 'SET': 'NTinflamm', 'PMID_ID': "<a href='https://www.ncbi.nlm.nih.gov/pubmed/" + str(x) + "' target='_blank'>" + str(x) + "</a>", 'PMID_TITLE': pmidt[x], 'Common': x in ntd } res.addRow(DataRow.fromDict(dataDict)) print(x, pmidt[x]) res.export("/home/mjoppich/win/Desktop/soehnlein_overview.html", ExportTYPE.HTML) with open('/home/mjoppich/win/Desktop/soehnlein.svg', 'w') as f: f.write( render_four_set_venn(allelems['NEUTROPHIL'], allelems['TISSUES'], allelems['DOID'], allelems['INFLAMM'], titles={ 'A': 'NEUTROPHIL', 'B': 'TISSUES', 'C': 'Infection', 'D': 'Inflammation' }))
", ".join( makeLinks( sorted([ x[1] for x in findEdgeInfo(x, interact, ['PUBMED']) ]), lambda lid: "<a href=\"https://www.ncbi.nlm.nih.gov/pubmed/" + lid + "\">" + lid + "</a>")), 'MIRTARBASE': ", ".join( makeLinks( sorted([ x[1] for x in findEdgeInfo(x, interact, ['MIRTARBASE']) ]), lambda lid: "<a href=\"http://mirtarbase.mbc.nctu.edu.tw/php/search.php?opt=search_box&kw=" + x + "&sort=id\">" + lid + "</a>")) } row = DataRow.fromDict(addRow) linkedDF.addRow(row) print("Total Missing miRNAs", totalMissing) print("Total Additional miRNAs", totalAdditional) missingDF.export("/mnt/c/ownCloud/data/miRExplore/overview_weber.xlsx", ExportTYPE.XLSX) linkedDF.export("/mnt/c/ownCloud/data/miRExplore/overview_weber.html", ExportTYPE.HTML) #CytoscapeGrapher.showGraph(graph, location=dirTemp, name='chemokines', title='Chemokines', nodeLabel=lambda x: x, edgeLabel=lambda x: '')
print( "Gene:", gene, "Status: ", ", ".join([ ": ".join([x, str(typeByGene[gene][x])]) for x in typeByGene[gene] ]), "Missing miRNAs: " + ",".join(elemsByGene[gene]['missing'])) print() print() print() print() networkGraphs[network] = networkGraph htmlDF.export( "/mnt/d/yanc_network/" + network.replace(" ", "_") + ".html", ExportTYPE.HTML) htmlDF.export( "/mnt/d/yanc_network/" + network.replace(" ", "_") + ".tsv", ExportTYPE.TSV) figidx = 0 for stages in makeStory: mergedGraph = networkGraphs[stages[0]] for i in range(1, len(stages)): mergedGraph = nx.compose(mergedGraph, networkGraphs[stages[i]]) hasLargeStage = any(['large' in stage for stage in stages])
for cells in cells2mirnas: ncells = [] for cell in cells: ncells.append( ""+cell+"}" ) rowdict = { 'miRNA': "\\makecell[l]{" + "\\\\".join(sorted(cells2mirnas[cells])) + "}", 'cells': "\\makecell[l]{" + "\\\\".join(cells) + "}" } cellCommunicatorDF.addRow(DataRow.fromDict(rowdict)) cellCommunicatorDF.export("/mnt/d/yanc_network/stage_cells_cliques"+stage+".latex", ExportTYPE.LATEX) cellCommunicatorDF.export("/mnt/d/yanc_network/stage_cells_cliques"+stage+".tsv", ExportTYPE.TSV) CytoscapeGrapher.showGraph(cellgraph, '/mnt/d/yanc_network/', name="stage_cells_" + stage) figidx = CytoscapeGrapher.plotNXGraph(cellgraph, stage, ["/mnt/d/yanc_network/stage_cells_" + stage.replace(" ", "_") + ".png", "/mnt/d/yanc_network/stage_cells_" + stage.replace(" ", "_") + ".pdf"], figidx) print() print() print() print() print() plotKeys = [] plotValues = [] for (cell, count) in cellCounter.most_common(20):
rowdict = {'sample': org} for homid in targetHOMIDS: homID = homid val = homDB.get_cluster(homID) if org in val: rowdict[homID] = 1 else: rowdict[homID] = 0 dfrow = DataRow.fromDict(rowdict) df.addRow(dfrow) df.export(outFile=None) print(allorgs) print(len(homClusterIDs)) if len(homClusterIDs) < 10: print(homClusterIDs) orgMatrix = {} for org in mc + nmc: orgRes = [] for homID in homClusterIDs: val = org in homDB.get_homology_cluster(homID)