def processed_X(Xprocessed, conditions, GDM, OGs, Map, MapSpecies):
L = len(Xprocessed)
res = np.array([None] * L, dtype=object)
resData = np.array([None] * L, dtype=object)
resHeader = np.array([None] * L, dtype=object)
resGeneNames = np.array([None] * L, dtype=object)
for l in range(L):
# Header (Samples)
#samploc = np.array(Samples[l])
#uniqueSamploc = np.unique(SamplesIDs[l])
#uniqueSamploc = uniqueSamploc[uniqueSamploc >= 0]
#resHeader[l] = [samploc[np.array(SamplesIDs[l]) == s][0] for s in uniqueSamploc]
resHeader[l] = conditions[l]
if Map is None:
resHeader[l] = np.array([['Genes'] + resHeader[l]])
else:
resHeader[l] = np.array([['OGs'] + MapSpecies.tolist() + resHeader[l]])
# Gene names
if Map is None:
resGeneNames[l] = OGs[GDM[:, l]].reshape(-1,1)
else:
genenames = [[ds.concatenateStrings(gs) for gs in Map[GDM[:, l]][:, sp]] for sp in range(Map.shape[1])]
resGeneNames[l] = np.concatenate((OGs[GDM[:, l]].reshape(-1,1), np.transpose(genenames)), axis=1)
# Data
resData[l] = np.array(Xprocessed[l])
# concatenate them
res[l] = np.concatenate((resGeneNames[l], resData[l]), axis=1)
res[l] = np.concatenate((resHeader[l], res[l]), axis=0)
res[l] = np.array(res[l], dtype=str)
return res
def calculateGDMandUpdateDatasets(X,
Genes,
Map=None,
mapheader=True,
OGsFirstColMap=True,
delimGenesInMap='\\W+',
OGsIncludedIfAtLeastInDatasets=1):
Xloc = ds.listofarrays2arrayofarrays(X)
Genesloc = deepcopy(Genes)
if Map is None:
OGsDatasets = deepcopy(Genes)
OGs = np.unique(ds.flattenAList(
OGsDatasets)) # Unique list of genes (or mapped genes)
MapNew = None
MapSpecies = None
else:
(OGs, OGsDatasets, MapNew,
MapSpecies) = mapGenesToCommonIDs(Genes, Map, mapheader,
OGsFirstColMap, delimGenesInMap)
L = len(Genesloc) # Number of datasets
# Ng = len(OGs) # Number of unique genes
GDMall = np.transpose([np.in1d(OGs, gs)
for gs in OGsDatasets]) # GDM: (Ng)x(L) boolean
# Exclude OGs that do not exist in at least (OGsIncludedIfAtLeastInDatasets) datasets
IncludedOGs = np.sum(GDMall, axis=1) >= OGsIncludedIfAtLeastInDatasets
GDM = GDMall[IncludedOGs]
OGs = OGs[IncludedOGs]
if MapNew is not None:
MapNew = MapNew[IncludedOGs]
Ngs = np.sum(GDM, axis=0) # Numbers of unique mapped genes in each dataset
Xnew = np.array([None] * L, dtype=object)
GenesDatasets = np.array([None] * L, dtype=object)
for l in range(L):
arelogs = np.nansum(
abs(Xloc[l][~isnan(Xloc[l])]) < 30
) > 0.98 * ds.numel(
Xloc[l][~isnan(Xloc[l])]) # More than 98% of values are below 30.0
d = Xloc[l].shape[1] # Number of dimensions (samples) in this dataset
Xnew[l] = np.zeros([Ngs[l], d], dtype=float)
GenesDatasets[l] = np.empty(Ngs[l], dtype=object)
OGsInThisDS = OGs[GDM[:, l]] # Unique OGs in this dataset
# TODO: Optimise the code below by exploiting ds.findArrayInSubArraysOfAnotherArray1D (like in line 203 above)
for ogi in range(len(OGsInThisDS)):
og = OGsInThisDS[ogi]
if arelogs:
Xnew[l][ogi] = np.log2(
np.sum(np.power(2.0, Xloc[l][np.in1d(OGsDatasets[l], og)]),
axis=0))
else:
Xnew[l][ogi] = np.sum(Xloc[l][np.in1d(OGsDatasets[l], og)],
axis=0)
GenesDatasets[l][ogi] = ds.concatenateStrings(Genesloc[l][np.in1d(
OGsDatasets[l], og)])
return Xnew, GDM, GDMall, OGs, MapNew, MapSpecies
def clusters_genes_OGs(B, OGs, Map, MapSpecies, delim='; '):
if Map is None:
Nsp = 0
else:
Nsp = len(MapSpecies) # Number of species
K = B.shape[1] # Number of clusters
if K == 0:
return np.array(np.empty([1, 1]), dtype=object)
Csizes = np.sum(B, axis=0) # Clusters' sizes
maxCsize = np.max(Csizes) # Largest cluster size
res = np.array(np.empty([maxCsize, (Nsp + 1) * K], dtype=str), dtype=object)
header = np.array([None] * ((Nsp + 1) * K * 2), dtype=object).reshape([2, ((Nsp + 1) * K)])
for k in range(K):
col = k * (Nsp + 1)
res[0:Csizes[k], col] = OGs[B[:, k]]
res[Csizes[k]:, col] = ''
header[0, col] = 'C{0} ({1} {2})'.format(k, Csizes[k], 'genes' if Map is None else 'OGs')
header[1, col] = 'Genes' if Map is None else 'OGs'
for sp in range(Nsp): # Will not get into this if Map is None, as Nsp will be zero in that case
col = k * (Nsp + 1) + sp + 1
res[0:Csizes[k], col] = [ds.concatenateStrings(gs, delim) for gs in Map[B[:, k], sp]]
res[Csizes[k]:, col] = ''
header[0, col] = ''
header[1, col] = MapSpecies[sp]
return np.array(np.concatenate((header, res), axis=0), dtype=str)