def load_gexpressions(idList, labelList, percentage):
os.getcwd() #Para conocer el directorio actual
# Connect to database to obtain data according to idList ids
res = pa.runNorm(idList, 'webapp/[email protected]:1521')
# convert data to proper dataframe
cont = 0
df = pd.DataFrame(index=res[cont].tolist())
for i in labelList:
cont += 1
df[i] = res[cont].tolist()
## obtain unstable genes
ngenes = int(df.index.size * percentage)
unstable_genes = fs.findUnstable(df.values, ngenes)
## select unstable genes from dataframe to plot
M = pd.DataFrame()
for g in unstable_genes:
M = pd.concat([M, df.iloc[[g]]])
# transform to zscope in axis 1 (rows)
dfzs1 = sst.zscore(M.values, axis=1, ddof=1)
dfz = pd.DataFrame(index=M.index, data=dfzs1, columns=labelList)
return dfz
def generate_heatmap(idList, labelList, percentage):
path = os.getcwd()
# Connect to database to obtain data according to idList ids
res = pa.runNorm(idList, 'webapp/[email protected]:1521')
# convert data to proper dataframe
cont = 0
df = pd.DataFrame(index=res[cont].tolist())
for i in labelList:
cont += 1
df[i] = res[cont].tolist()
## obtain unstable genes
ngenes = int(df.index.size * percentage)
unstable_genes = fs.findUnstable(df.values, ngenes)
## select unstable genes from dataframe to plot
M = pd.DataFrame()
for g in unstable_genes:
M = pd.concat([M, df.iloc[[g]]])
# transform to zscope in axis 1 (rows)
dfzs1 = sst.zscore(M.values, axis=1, ddof=1)
dfz = pd.DataFrame(index=M.index, data=dfzs1, columns=labelList)
# generate hetmap plot
# add colored label
#sns_plot = sns.clustermap(dfz, figsize=(len(labelList)/2, ngenes/2), cmap='viridis', metric="correlation", row_colors=row_colors)
sns_plot = sns.clustermap(dfz,
figsize=(ngenes / 3, ngenes / 2),
cmap='viridis')
#obtain time stamp
ts = time.time()
st = datetime.fromtimestamp(ts).strftime('%Y-%m-%d_%H%M%S')
#save figure with hetmap
figpath = path + "\\output_" + st + ".png"
sns_plot.savefig(figpath)
return figpath
df['E2'] = res[2].tolist()
df['E3'] = res[3].tolist()
df['E20'] = res[4].tolist()
df['E21'] = res[5].tolist()
df['E22'] = res[6].tolist()
df['E25'] = res[7].tolist()
df['E31'] = res[8].tolist()
df['E32'] = res[9].tolist()
sns.clustermap(df, figsize=(5, 100), cmap='viridis')
df.describe()
df.to_csv('dataset.csv')
unstable_genes = fs.findUnstable(df.values, 100)
M = pd.DataFrame()
for g in unstable_genes:
M = pd.concat([M, df.iloc[[g]]])
sns.clustermap(M, figsize=(5, 50), cmap='viridis')
# =============================================================================
# df=pd.DataFrame(index=res[0].tolist(),data=sst.zscore(res[1],axis=0, ddof=1).tolist(),columns=['E1']) # 1st row as the column names
# df['E2']=sst.zscore(res[2],axis=1, ddof=10).tolist()
# df['E3']=sst.zscore(res[3],axis=1, ddof=10).tolist()
# df['E20']=sst.zscore(res[4],axis=1, ddof=10).tolist()
# df['E21']=sst.zscore(res[5],axis=1, ddof=10).tolist()
# df['E22']=sst.zscore(res[6],axis=1, ddof=10).tolist()
# df['E25']=sst.zscore(res[7],axis=1, ddof=10).tolist()