def parse(self):
gene1_perturbation = SlConstants.PHARMACEUTICAL
gene2_perturbation = 'natural (is a TSG)'
assay = "pharmaceutical + siRNA"
# The following keeps track of the current largest effect size SLI for any given gene A/gene B pair
sli_dict = defaultdict(list)
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
if len(row) < 4:
raise ValueError(
"Only got %d fields but was expecting at least 4 tab-separated fields"
% len(row))
# seperate col containing multiple genes
geneA_sym = row['geneAlist'].split(",")
geneB_sym = row['geneB']
geneB_sym = self.get_current_symbol(geneB_sym)
if geneB_sym in self.get_current_symbol(geneB_sym):
geneB_id = "NCBIGene:{}".format(
self.entrez_dict.get(geneB_sym))
else:
raise ValueError(
"Could not find id for geneB %s in Srivasa 2016" %
geneB_sym)
effect = float(row['effect'].replace(",", "."))
for i in geneA_sym:
i = self.get_current_symbol(i)
if i in self.entrez_dict:
geneA_id = "NCBIGene:{}".format(
self.entrez_dict.get(i))
else:
raise ValueError(
"Could not find id for geneA %s in Srivasa 2016" %
i)
if geneA_id == geneB_id:
continue # There are a few self loops in the data, but these are not SLIs, so we skip them
sli = SyntheticLethalInteraction(
gene_A_symbol=i,
gene_A_id=geneA_id,
gene_B_symbol=geneB_sym,
gene_B_id=geneB_id,
gene_A_pert=gene1_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=SlConstants.ZSCORE,
effect_size=effect,
cell_line=SlConstants.HELA_CELL,
cellosaurus_id=SlConstants.HELA_CELLOSAURUS,
cancer_type=SlConstants.N_A,
ncit_id=SlConstants.N_A,
assay=assay,
pmid=self.pmid,
SL=True)
gene_pair = GenePair(i, geneB_sym)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse(self):
myc = 'MYC'
myc_id = self.get_ncbigene_curie(myc)
myc_perturbation = SlConstants.OVEREXPRESSION
geneB_perturbation = SlConstants.SI_RNA
assay_string = SlConstants.RNA_INTERFERENCE_ASSAY
effect_type = SlConstants.LOG2_DECREASE_IN_ABUNDANCE
cell_line = 'human mammary epithelial cells'
cellosaurus = SlConstants.N_A
cancer = SlConstants.N_A
ncit = SlConstants.N_A
sli_dict = defaultdict(list)
# Pseudogenes, divergent nc transcripts
# DIP maps to two newer symbols (also GIF
unclear_gene_symbols = {
'ATP5EP1', 'C10orf111', 'C19ORF30', 'C3ORF51', 'CG030', 'CLEC4GP1',
'CSN1S2A', 'DIP', 'DKFZP434I0714', 'DVL1L1', 'FLJ20674',
'FLJ22447', 'GIF', 'HCG27', 'HMG14P', 'IGLV@', 'LDHBP', 'OR5D2P',
'RBMXP1', 'RPL19P1'
}
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
if len(row) != 3:
raise ValueError("Bad row with %d fields: %s" %
(len(row), row))
geneBsym = row['symbol']
geneBsym = self.get_current_symbol(geneBsym)
if geneBsym in self.entrez_dict:
geneB_id = self.get_ncbigene_curie(geneBsym)
elif geneBsym in unclear_gene_symbols:
continue
else:
raise ValueError(
"Could not find id for %s in Kessler 2012 " % geneBsym)
medianDiffs = float(row['median.pair.diffs'])
sli = SyntheticLethalInteraction(
gene_A_symbol=myc,
gene_A_id=myc_id,
gene_B_symbol=geneBsym,
gene_B_id=geneB_id,
gene_A_pert=myc_perturbation,
gene_B_pert=geneB_perturbation,
effect_type=effect_type,
effect_size=medianDiffs,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=cancer,
ncit_id=ncit,
assay=assay_string,
pmid=self.pmid,
SL=True)
gene_pair = GenePair(myc, geneBsym)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse(self):
geneA_symbol = 'CHEK1'
geneA_id = 'NCBIGene:1111'
geneA_perturbation = SlConstants.PHARMACEUTICAL
gene2_perturbation = SlConstants.SI_RNA
assay = SlConstants.RNA_INTERFERENCE_ASSAY
effect_type = SlConstants.ZSCORE
cell_line = "HeLa-Cells"
cellosaurus = "CVCL_0030"
cancer = ""
ncit = "" #
# The following keeps track of the current largest effect size SLI for any given gene A/gene B pair
sli_dict = defaultdict(list)
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
# Z-Score Symbol Entrez ID Gene Name
for row in csvreader:
if len(row) < 3:
raise ValueError("Only got %d fields but was expecting at least 3" % len(row))
geneB_sym = row['Symbol']
geneB_sym = self.get_current_symbol(geneB_sym)
if geneB_sym == 'CHEK1':
continue # Do not allow self-loops!
if geneB_sym in self.entrez_dict:
geneB_id = "NCBIGene:{}".format(self.entrez_dict.get(geneB_sym))
else:
raise ValueError("Could not find id for gene symbol %s in Shen 2015" % geneB_sym)
effect = float(row['Z-Score'].replace(",", "."))
sl_genes = ["FZR1", "RAD17", "RFC1", "BLM", "CDC73", "CDC6", "WEE1"]
if geneB_sym in sl_genes:
SL = True
else:
SL = False
sli = SyntheticLethalInteraction(gene_A_symbol=geneA_symbol,
gene_A_id=geneA_id,
gene_B_symbol=geneB_sym,
gene_B_id=geneB_id,
gene_A_pert=geneA_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=effect_type,
effect_size=effect,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=cancer,
ncit_id=ncit,
assay=assay,
pmid=self.pmid,
SL=SL)
gene_pair = GenePair(geneA_symbol, geneB_sym)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse(self):
perturbation = SlConstants.KNOCKOUT
cellosuarus = SlConstants.HAP1_CELLOSAURUS
assay = 'proportions.of.sense.and.antisense.insertions'
sli_dict = defaultdict(list)
# GENE SUMMARY PUBMED ID INTERACTING QUERY GENE
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
geneA = self.get_current_symbol(row['GENE'])
if geneA in self.entrez_dict:
geneA_id = "NCBIGene:{}".format(
self.entrez_dict.get(geneA))
else:
raise ValueError(
"[ERROR] We could not find a gene id for " + geneA)
geneBlist = row['INTERACTING QUERY GENE']
for geneB in geneBlist.split(';'):
geneB = geneB.strip()
geneB = self.get_current_symbol(geneB)
if geneB in self.entrez_dict:
geneB_id = "NCBIGene:{}".format(
self.entrez_dict.get(geneB))
else:
raise ValueError(
"Could not get NCBI id for gene \"%s\" in Blomen 2015"
% geneB)
sli = SyntheticLethalInteraction(
gene_A_symbol=geneA,
gene_A_id=geneA_id,
gene_B_symbol=geneB,
gene_B_id=geneB_id,
gene_A_pert=perturbation,
gene_B_pert=perturbation,
effect_type=SlConstants.N_A,
effect_size=0,
cell_line=SlConstants.HAP1_CELL,
cellosaurus_id=cellosuarus,
cancer_type=SlConstants.N_A,
ncit_id=SlConstants.N_A,
assay=assay,
pmid=self.pmid,
SL=True)
gene_pair = GenePair(geneA, geneB)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse_suppl10_11(self, fname):
rb1 = 'RB1'
rb1_id = SlConstants.RB1_GENE_ID
rb1_perturbation = SlConstants.LOF_MUTATION
gene2_perturbation = SlConstants.SI_RNA
assay_string = "siMEM+penetrance"
effect_type = "penetrance"
cell_line = SlConstants.N_A
cellosaurus = SlConstants.N_A
cancer = SlConstants.N_A
ncit = SlConstants.N_A
with open(fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
# print(row)
geneBsym = self.get_current_symbol(row['target'])
if ',' in geneBsym:
continue # We cannot assign an effect unambiguously to one of the genes
# some of the entries are like PMS2,PMS2CL
if geneBsym in self.entrez_dict:
geneB_id = self.get_ncbigene_curie(geneBsym)
elif geneBsym in self.unclear_gene_symbols:
continue
else:
raise ValueError("Could not find id for %s in Brough 2018 2008 " % geneBsym)
penetrance = int(row['Penetrance.(%)'])
if penetrance >= 80:
sli = SyntheticLethalInteraction(gene_A_symbol=rb1,
gene_A_id=rb1_id,
gene_B_symbol=geneBsym,
gene_B_id=geneB_id,
gene_A_pert=rb1_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=effect_type,
effect_size=penetrance,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=cancer,
ncit_id=ncit,
assay=assay_string,
pmid=self.pmid,
SL=True)
gene_pair = GenePair(rb1, geneBsym)
self.sli_dict[gene_pair].append(sli)
def parse_suppl9(self):
fname = 'data/brough_2012_suppl9.tsv'
rb1 = 'RB1'
rb1_id = SlConstants.RB1_GENE_ID
rb1_perturbation = SlConstants.LOF_MUTATION
gene2_perturbation = SlConstants.SI_RNA
assay_string = "siMEM+penetrance"
effect_type = "penetrance"
cell_line = SlConstants.N_A
cellosaurus = SlConstants.N_A
cancer = SlConstants.N_A
ncit = SlConstants.N_A
with open(fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
# print(row)
geneBsym = self.get_current_symbol(row['symbol'])
if geneBsym in self.entrez_dict:
geneB_id = "NCBIGene:{}".format(self.entrez_dict.get(geneBsym))
elif geneBsym in self.unclear_gene_symbols:
continue
else:
raise ValueError("Could not find iid for %s in Brough 2018 2008 " % geneBsym)
penetrance = int(row['Penetrance.%'])
if penetrance >= 80:
sli = SyntheticLethalInteraction(gene_A_symbol=rb1,
gene_A_id=rb1_id,
gene_B_symbol=geneBsym,
gene_B_id=geneB_id,
gene_A_pert=rb1_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=effect_type,
effect_size=penetrance,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=cancer,
ncit_id=ncit,
assay=assay_string,
pmid=self.pmid,
SL=True)
gene_pair = GenePair(rb1, geneBsym)
self.sli_dict[gene_pair].append(sli)
def parse(self):
parp1_symbol = 'PARP1'
parp1_id = 'NCBIGene:142'
parp1_perturbation = SlConstants.PHARMACEUTICAL
gene2_perturbation = SlConstants.SI_RNA
assays = ['competitive hybridization', 'multicolor competition assay']
assay_string = ";".join(assays)
effect_type = 'stddev'
cell_line = 'CAL-51'
cellosaurus = 'CVCL_1110'
cancer = "Breast Carcinoma"
ncit = "NCIT:C4872"
sli_dict = defaultdict(list)
with open(self.fname) as csvfile:
# SMARTpool Z score percent-siCONTROL
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
if len(row) < 3:
raise ValueError(
"Bad row for Turner et al, only %d fields found (%s)" %
(len(row), row))
geneB_sym = self.get_current_symbol(row['SMARTpool'])
zscore = float(row['Z score'])
if geneB_sym in self.entrez_dict:
geneB_id = "NCBIGene:{}".format(
self.entrez_dict.get(geneB_sym))
elif geneB_sym == 'IMPK':
continue # could not be found in HGNC or NCBI Gene
elif geneB_sym == 'FLJ34389':
geneB_sym = 'MLKL'
geneB_id = "NCBIGene:197259"
else:
if zscore > 3.0:
raise ValueError(
"Could not get NCBI id for gene %s in Turner 2008"
% geneB_sym)
else:
continue # These are negative examples, we will just skiip
if zscore <= -3.0:
SL = True
else:
SL = False
sli = SyntheticLethalInteraction(
gene_A_symbol=parp1_symbol,
gene_A_id=parp1_id,
gene_B_symbol=geneB_sym,
gene_B_id=geneB_id,
gene_A_pert=parp1_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=effect_type,
effect_size=zscore,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=cancer,
ncit_id=ncit,
assay=assay_string,
pmid=self.pmid,
SL=SL)
gene_pair = GenePair(parp1_symbol, geneB_sym)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse(self):
kras_symbol = 'KRAS'
kras_id = 'NCBIGene:3845'
kras_perturbation = SlConstants.ACTIVATING_MUTATION
gene2_perturbation = 'shRNA'
assays = ['competitive hybridization', 'multicolor competition assay']
assay_string = ";".join(assays)
effect_type = 'stddev'
cell_line = "DLD-1"
cellosaurus = "CVCL_0248"
cancer = "Colorectal Carcinoma"
ncit = "NCIT:C2955"
# The following keeps track of the current largest effect size SLI for any given gene A/gene B pair
# Symbol Accession v2SH Sequence Mean.DLD1 SD.DLD1 Mean.HCT116 SD.HCT116
sli_dict = defaultdict(list)
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
if len(row) < 8:
raise ValueError(
"Bad line in Luo2009 with less than 8 fields")
geneB_sym = self.get_current_symbol(row['Symbol'])
if geneB_sym == 'CXORF40A':
geneB_sym = 'EOLA1'
if geneB_sym in self.entrez_dict:
geneB_id = "NCBIGene:{}".format(
self.entrez_dict.get(geneB_sym))
elif geneB_sym == 'FLJ34747':
# This is a LINC, plus the symbol is old
geneB_sym = 'LINC00547'
geneB_id = 'NCBIGene:400121'
elif geneB_sym == 'LOC283194':
geneB_id = 'NCBIGene:283194' # an ncRNA
elif geneB_sym == 'LOC285556':
geneB_id = 'NCBIGene:285556'
elif geneB_sym == 'LOC149654' or geneB_sym == 'LOC730000':
continue # Could not find these in NCBI Gene or HCNG
else:
raise ValueError(
"Could not get NCBI id for gene %s in Luo2009" %
geneB_sym)
stddev = float(row['SD.DLD1']) # float(fields[5])
SL = True # All data in this set is True # TODO CHECK
sli = SyntheticLethalInteraction(
gene_A_symbol=kras_symbol,
gene_A_id=kras_id,
gene_B_symbol=geneB_sym,
gene_B_id=geneB_id,
gene_A_pert=kras_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=effect_type,
effect_size=stddev,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=cancer,
ncit_id=ncit,
assay=assay_string,
pmid=self.pmid,
SL=SL)
gene_pair = GenePair(kras_symbol, geneB_sym)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse(self):
kras_symbol = 'KRAS'
kras_id = SlConstants.KRAS_GENE_ID
kras_perturbation = SlConstants.ACTIVATING_MUTATION # activating_mutation
gene2_perturbation = SlConstants.SI_RNA # 'siRNA'
assay_string = SlConstants.RNA_INTERFERENCE_ASSAY
effect_type = 'stddev'
cell_line = SlConstants.HCT_116
cellosaurus = SlConstants.HCT_116_CELLOSAURUS
cancer = SlConstants.COLORECTAL_CARCINOMA
ncit = SlConstants.COLORECTAL_CARCINOMA_NCIT
sli_dict = defaultdict(list)
# Immunoglobulin or multiple mapping old symbols
# COAS3, CES4, POM121L1, MYCL2 are aliases for a pseudogene
unclear_gene_symbols = {
'MAD', 'IGHG4', 'DKFZp434C1418', 'COAS3', 'HNT', 'CES4', 'SAS',
'HLA-DRB3', 'LOC90557', 'POM121L1', 'MLL2', '37499', 'MYCL2',
'CAMKIINALPHA', 'TGIF', 'PCDHA2', 'PCDHA9'
}
# GeneID Locus.ID Accession HCT-116.Z-score HKE-3.Z-score D.Z-score
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
if len(row) != 6:
raise ValueError("Line has %d fields (should have 6): %s" %
(len(row), row))
geneB_sym = row['GeneID'] # F[0]
geneB_sym = self.get_current_symbol(geneB_sym)
locusID = row['Locus.ID'] # F[1]
accession = row['Accession'] # F[2]
HCT116_zscore = float(row['HCT-116.Z-score']) # float(F[3])
HKE3_zscore = float(row['HKE-3.Z-score']) # float(F[4])
delta_zscore = float(row['D.Z-score'])
if geneB_sym in self.entrez_dict:
geneB_id = "NCBIGene:{}".format(
self.entrez_dict.get(geneB_sym))
elif geneB_sym == 'C9ORF96':
geneB_sym = 'STKLD1'
elif geneB_sym in unclear_gene_symbols:
continue
elif delta_zscore < 2:
continue # one of the many negative samples, we can skip it if it cannot be mapped
else:
raise ValueError(
"Could not find id for gene %s in Steckel 2012" %
geneB_sym)
if geneB_sym == "KRAS":
continue # This was an internal control!
if delta_zscore >= 3.3 and HKE3_zscore < 2:
SL = True
else:
SL = False
sli = SyntheticLethalInteraction(
gene_A_symbol=kras_symbol,
gene_A_id=kras_id,
gene_B_symbol=geneB_sym,
gene_B_id=geneB_id,
gene_A_pert=kras_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=effect_type,
effect_size=HCT116_zscore,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=cancer,
ncit_id=ncit,
assay=assay_string,
pmid=self.pmid,
SL=SL)
gene_pair = GenePair(kras_symbol, geneB_sym)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse(self):
# because of the experiment, geneA is always VHL.
vhl_symbol = 'VHL'
vhl_id = SlConstants.VHL_GENE_ID
vhl_perturbation = SlConstants.LOF_MUTATION
gene2_perturbation = SlConstants.SH_RNA
assays = [SlConstants.COMPETITIVE_HYBRIDIZATION, SlConstants.MULTICOLOR_COMPETITION_ASSAY]
effect_type = 'differential_viability'
cell_786O = "786-0"
cellosaurus_786O = "CVCL_1051"
cell_RCC4 = "RCC4"
cellosaurus_RCC4 = "CVCL_0498"
# The following keeps track of the current largest effect size SLI for any given gene A/gene B pair
sli_dict = defaultdict(list)
# The following list includes symbols that are not current but either could
# not be matched or match to multiple possible candidates
unclear_gene_symbols = {'PITSLRE', 'TAK1', 'PKD3', 'CAMLCK', 'MAPAPK3', 'CK1E', 'CK2A2', 'PDGRFB', 'ZC1/HGK'}
# gene differential cell table
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
if len(row) < 4:
raise ValueError("Only got %d fields but was expecting 4" % len(row))
genesy = row['gene'].upper()
geneB_sym = self.get_current_symbol(genesy)
if geneB_sym == "IRR" or geneB_sym == "HER4":
continue
if geneB_sym in unclear_gene_symbols:
continue # Symbol could be either CDK11A or CDK11B
if geneB_sym in self.entrez_dict:
geneB_id = "NCBIGene:{}".format(self.entrez_dict.get(geneB_sym))
else:
raise ValueError("Could not find id for %s in Bommi 2008" % geneB_sym)
effect = float(row['differential'])
cell = row['cell']
if cell == 'RCC4':
cell_line = cell_RCC4
cellosaurus = cellosaurus_RCC4
elif cell == '786-0':
cell_line = cell_786O
cellosaurus = cellosaurus_786O
else:
raise ValueError("Did not recognize cell type '%s'" % cell)
table = row['table']
assay_string = "differential viability assay {}({})".format(cell, table)
SL = True # All data in this set is True # TODO CHECK
sli = SyntheticLethalInteraction(gene_A_symbol=vhl_symbol,
gene_A_id=vhl_id,
gene_B_symbol=geneB_sym,
gene_B_id=geneB_id,
gene_A_pert=vhl_perturbation,
gene_B_pert=gene2_perturbation,
effect_type=effect_type,
effect_size=effect,
cell_line=cell_line,
cellosaurus_id=cellosaurus,
cancer_type=SlConstants.CLEAR_CELL_RENAL_CELL_CARCINOMA,
ncit_id=SlConstants.CLEAR_CELL_RENAL_CELL_CARCINOMA_NCIT,
assay=assay_string,
pmid=self.pmid,
SL=SL)
gene_pair = GenePair(vhl_symbol, geneB_sym)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list
def parse(self):
geneA = 'ATR'
geneAid = 'NCBIGene:545'
sli_dict = defaultdict(list)
with open(self.fname) as csvfile:
csvreader = csv.DictReader(csvfile, delimiter='\t')
for row in csvreader:
geneB = row['Gene Symbol']
geneB = self.get_current_symbol(geneB)
if geneB == 'ATR':
continue # Self interaction, not a SLI!
# A few special cases -- capitalization is not correct in the HGNC file
if geneB == 'C10ORF119':
geneB = 'MCMBP'
elif geneB == 'C15ORF20':
geneB = 'PIF1'
elif geneB == 'CXORF53':
geneB = 'BRCC3'
if geneB in self.entrez_dict:
geneBid = self.get_ncbigene_curie(geneB)
else:
raise ValueError("Could not find id for gene %s in Mohni 2014" % geneB)
mock1 = float(row['Mock.1'])
atr1 = float(row['ATRi.1'])
mock2 = float(row['Mock.2'])
atr2 = float(row['ATRi.2'])
mock3 = float(row['Mock.3'])
atr3 = float(row['ATRi.3'])
mock4 = float(row['Mock.4'])
atr4 = float(row['ATRi.4'])
d1 = atr1 - mock1
d2 = atr2 - mock2
d3 = atr3 - mock3
d4 = atr4 - mock4
# We demand that at least three replicates show SL
a = np.array([d1, d2, d3, d4])
mn = a.mean()
if mn < -2:
SL = True
elif mn >= 0:
SL = False
else:
raise ValueError("Expecting mean either below -2 or above 0")
sli = SyntheticLethalInteraction(gene_A_symbol=geneA,
gene_A_id=geneAid,
gene_B_symbol=geneB,
gene_B_id=geneBid,
gene_A_pert=SlConstants.PHARMACEUTICAL,
gene_B_pert=SlConstants.SI_RNA,
effect_type=SlConstants.ZSCORE,
effect_size=mn,
cell_line=SlConstants.U2OS_CELL,
cellosaurus_id=SlConstants.U2OS_CELLOSAURUS,
cancer_type='n/a',
ncit_id='n/a',
assay=SlConstants.RNA_INTERFERENCE_ASSAY,
pmid=self.pmid,
SL=SL)
gene_pair = GenePair(geneA, geneB)
sli_dict[gene_pair].append(sli)
sli_list = self._mark_maximum_entries(sli_dict)
return sli_list