added fn to run entire pipeline

also cleaned up some descriptions and stopped adding alt_name to output

base.py

@@ -21,6 +21,8 @@ def filterData(countsFile, metaFile, minloci, minSample, refFilter = None):
     Args:
         countsFile: path to reformatted counts file
         metaFile: path to the metadata file paired with the countsFile
+        minSample: samples must be missing counts data from less than X proportion of markers
+        minloci: markers must be have counts data from more than than Y proportion of samples
         refFilter: (optional) remove references with a divergence score above this value
     '''
     #import counts data
@@ -29,7 +31,7 @@ def filterData(countsFile, metaFile, minloci, minSample, refFilter = None):
 
     #check that all marker names are unique
     marker, markerCount = np.unique(counts.index, return_counts=True)
-    if len(np.where(markerCount > 2)[0]) > 0: print('Multiple markers in same gene, differentiate marker names in the count file')
+    if len(np.where(markerCount > 2)[0]) > 0: print('More than two rows with the same marker name, please differentiate marker names in the count file')
 
     #import sample metadata
     sampleMeta = pd.read_csv(metaFile)
@@ -151,14 +153,7 @@ def labelSamples(snpProportion,sampleMeta,db_communities,embedding, cutHeight, a
     output['short_name'] = snpProportion.columns
     if admixedCutoff:
         output['divergence'] = plot.homozygousDivergence(snpProportion)
-    output['variety'] = pd.NA
-
-    #for each short_name, find the index in sampleMeta and grab the alt_name
-    countryID = []
-    for short_name in snpProportion.columns:
-        countryID.append(sampleMeta[sampleMeta['short_name'] == int(short_name)]['alt_name'].values[0])
-    output['countryID'] = countryID
-
+    output['variety'] = pd.NA    
 
     for cluster in np.unique(db_communities):
         if cluster == -1: #-1 indicates samples that are disconnected from the rest of the clusters  
@@ -220,6 +215,15 @@ def loadParameters(parameterFile):
 
     return minSample, minloci, umapSeed, epsilon, cutHeight, admixedCutoff, filePrefix, inputCountsFile, inputMetaFile
 
+def runPipeline(parameterFile):
+    minSample, minloci, umapSeed, epsilon, cutHeight, admixedCutoff, filePrefix, inputCountsFile, inputMetaFile = loadParameters(parameterFile)
+    snpProportion, snpProportionNoInterpolation, sampleMeta = filterData(inputCountsFile, inputMetaFile, minloci, minSample)
+    embedding = embedData(snpProportion, umapSeed)
+    db_communities = clusteringDBSCAN(snpProportion, sampleMeta, embedding, epsilon, filePrefix, admixedCutoff)
+    output = labelSamples(snpProportion, sampleMeta, db_communities, embedding, cutHeight, admixedCutoff, filePrefix)
+
+    return snpProportion, snpProportionNoInterpolation, sampleMeta, embedding, db_communities, output
+
 if __name__ == '__main__':
     minSample, minloci, umapSeed, epsilon, cutHeight, admixedCutoff, filePrefix, inputCountsFile, inputMetaFile = loadParameters(parameterFile)
     snpProportion, snpProportionNoInterpolation, sampleMeta = filterData(inputCountsFile, inputMetaFile, minloci, minSample)

graphs.py

@@ -690,22 +690,17 @@ def heatmapDendrogramAll(snpProportion, sampleMeta, communities, filePrefix, cut
         plt.savefig(filePrefix+' dendrogram cluster '+str(clusterNum)+' (cut height'+str(cutHeight)+').png', dpi = 300)
 
 def barchartLandrace(snpProportion, output, sampleMeta, filePrefix = None, cutoff = 2):
-
     """
-    Barchart with the prevalence of each observed reference variety
+    Barchart with the prevalence of each observed genetic entity
 
     Args:
         snpProportion: processed SNP proportion data
         output: output dataframe frome base.py
         sampleMeta: metadata paired with genotyping data
         filePrefix: optional prefix for output filenames to save
     """
-    w = sampleMeta[sampleMeta['short_name'].isin(snpProportion.columns.astype('int'))]
     var, counts = np.unique(output['variety'], return_counts=True)
-
-    refshort = w['short_name'][(sampleMeta['reference'].notna())].values.astype('str') #references
-    admixedshort = output['short_name'][output['variety'] == 'Admixed'].values.astype('str') #admixed
-
+
     landraceName = var[np.flatnonzero(np.core.defchararray.find(var.astype('str'),'Genetic entity')!=-1)]
     landraceShort = output['short_name'][np.isin(output['variety'],landraceName)].values.astype('str')
     landraceVarieties, landraceVarietiesCount = np.unique(output[output['short_name'].isin(landraceShort)]['variety'], return_counts=True)
@@ -719,14 +714,15 @@ def barchartLandrace(snpProportion, output, sampleMeta, filePrefix = None, cutof
     ax.set_yticks(np.arange(len(landraceVarietiesFilter)),landraceVarietiesFilter[np.argsort(landraceVarietiesCountFilter)])
     plt.tight_layout()
     if filePrefix:
-        plt.savefig(filePrefix + ' barchart landrace horizontal.png', dpi = 300)
+        plt.savefig(filePrefix + ' genetic entity barchart.png', dpi = 300)
+
     #histogram of occurences of genetic entities
     plt.figure()
     plt.hist(landraceVarietiesCount, bins = np.arange(max(landraceVarietiesCount)+2))
     plt.xticks(np.arange(1,max(landraceVarietiesCount)+2))
     plt.tight_layout()
     if filePrefix:
-        plt.savefig(filePrefix + ' barchart landrace vertical.png', dpi = 300)
+        plt.savefig(filePrefix + ' genetic entity histogram.png', dpi = 300)
 
 def heatmapDendrogram(snpProportion, sampleMeta, communities, COI, cutHeight):
     """