Reactome pathway
source
get_reactome_raw
get_reactome_raw (gene_list)
Reactome pathway analysis for a given gene set; returns raw output in dataframe.
pi3ks= ['PIK3CA' ,'PIK3CB' ,'PIK3CD' ,'PIK3CG' ,'PIK3R1' ,'PIK3R2' ,'PIK3R3' ,'PTEN' ,'AKT1' ,'AKT2' ,'AKT3' ,'MTOR' ,'RICTOR' ,'RPTOR' ,'TSC1' ,'TSC2' ,'PDK1' ,'IRS1' ,'IRS2' ,'INSR' ,'IGF1R' ,'GAB1' ,'HRAS' ,'NRAS' ,'KRAS' ,'EGFR' ,'ERBB2' ,'ERBB3' ,'ERBB4' ]
raw_out = get_reactome_raw(pi3ks)
raw_out.head()
0
R-HSA-1963640
1963640
GRB2 events in ERBB2 signaling
True
False
48887
9606
Homo sapiens
TOTAL
21
9
0.001304
1.110223e-16
1.221245e-15
[]
TOTAL
4
4
0.000255
1
R-HSA-9665348
9665348
Signaling by ERBB2 ECD mutants
True
True
48887
9606
Homo sapiens
TOTAL
23
9
0.001428
1.110223e-16
1.221245e-15
[]
TOTAL
15
15
0.000957
2
R-HSA-9664565
9664565
Signaling by ERBB2 KD Mutants
True
True
48887
9606
Homo sapiens
TOTAL
35
13
0.002174
1.110223e-16
1.221245e-15
[]
TOTAL
17
17
0.001085
3
R-HSA-1227990
1227990
Signaling by ERBB2 in Cancer
False
True
48887
9606
Homo sapiens
TOTAL
36
13
0.002236
1.110223e-16
1.221245e-15
[]
TOTAL
62
62
0.003957
4
R-HSA-9665686
9665686
Signaling by ERBB2 TMD/JMD mutants
True
True
48887
9606
Homo sapiens
TOTAL
30
10
0.001863
1.110223e-16
1.221245e-15
[]
TOTAL
13
13
0.000830
source
get_reactome
get_reactome (gene_list, p_type='FDR')
Reactome pathway analysis for a given gene set; returns formated output in dataframe with additional -log10(p)
gene_list
p_type
str
FDR
or p
out = get_reactome(pi3ks,p_type= 'p' )
out.head()
Running pathway anlysis
Done
0
GRB2 events in ERBB2 signaling
R-HSA-1963640
1.110223e-16
15.955
1
Signaling by ERBB2 ECD mutants
R-HSA-9665348
1.110223e-16
15.955
2
Signaling by ERBB2 KD Mutants
R-HSA-9664565
1.110223e-16
15.955
3
Signaling by ERBB2 in Cancer
R-HSA-1227990
1.110223e-16
15.955
4
Signaling by ERBB2 TMD/JMD mutants
R-HSA-9665686
1.110223e-16
15.955
out = get_reactome(pi3ks,p_type= 'FDR' )
out.head()
Running pathway anlysis
Done
0
GRB2 events in ERBB2 signaling
R-HSA-1963640
1.221245e-15
14.913
1
Signaling by ERBB2 ECD mutants
R-HSA-9665348
1.221245e-15
14.913
2
Signaling by ERBB2 KD Mutants
R-HSA-9664565
1.221245e-15
14.913
3
Signaling by ERBB2 in Cancer
R-HSA-1227990
1.221245e-15
14.913
4
Signaling by ERBB2 TMD/JMD mutants
R-HSA-9665686
1.221245e-15
14.913
0
GRB2 events in ERBB2 signaling
R-HSA-1963640
1.221245e-15
14.913
1
Signaling by ERBB2 ECD mutants
R-HSA-9665348
1.221245e-15
14.913
2
Signaling by ERBB2 KD Mutants
R-HSA-9664565
1.221245e-15
14.913
3
Signaling by ERBB2 in Cancer
R-HSA-1227990
1.221245e-15
14.913
4
Signaling by ERBB2 TMD/JMD mutants
R-HSA-9665686
1.221245e-15
14.913
...
...
...
...
...
319
Viral Infection Pathways
R-HSA-9824446
3.888317e-02
1.410
320
RHO GTPase cycle
R-HSA-9012999
3.922694e-02
1.406
321
RUNX3 regulates p14-ARF
R-HSA-8951936
4.374857e-02
1.359
322
Cellular response to chemical stress
R-HSA-9711123
4.654844e-02
1.332
323
Signaling by Rho GTPases
R-HSA-194315
4.672781e-02
1.330
324 rows × 4 columns
Reference
Download from Reactome/Download_data: https://reactome.org/download-data
Download UniProt to All pathways
under Identifier mapping files
for type, there are IEA (Inferred from Electronic Annotation) and TAS (Traceable Author Statement, higher confidence)
ref = Data.get_reactome_pathway()
0
A0A023GPK8
R-DME-1500931
Cell-Cell communication
IEA
Drosophila melanogaster
1
A0A023GPK8
R-DME-373753
Nephrin family interactions
IEA
Drosophila melanogaster
2
A0A023GRW3
R-DME-72163
mRNA Splicing - Major Pathway
IEA
Drosophila melanogaster
3
A0A023GRW3
R-DME-72172
mRNA Splicing
IEA
Drosophila melanogaster
4
A0A023GRW3
R-DME-72203
Processing of Capped Intron-Containing Pre-mRNA
IEA
Drosophila melanogaster
source
query_reactome
query_reactome (uniprot_id)
Query uniprot ID in Reactome all level pathway database.
akt_path = query_reactome('P31749' )
0
R-HSA-109581
P31749
Apoptosis
IEA
Homo sapiens
0
1
R-HSA-109582
P31749
Hemostasis
TAS
Homo sapiens
0
2
R-HSA-109606
P31749
Intrinsic Pathway for Apoptosis
IEA
Homo sapiens
0
3
R-HSA-111447
P31749
Activation of BAD and translocation to mitocho...
IEA
Homo sapiens
1
4
R-HSA-114452
P31749
Activation of BH3-only proteins
IEA
Homo sapiens
0
...
...
...
...
...
...
...
107
R-HSA-9841251
P31749
Mitochondrial unfolded protein response (UPRmt)
TAS
Homo sapiens
1
108
R-HSA-9855142
P31749
Cellular responses to mechanical stimuli
IEA, TAS
Homo sapiens
0
109
R-HSA-9856530
P31749
High laminar flow shear stress activates signa...
IEA, TAS
Homo sapiens
1
110
R-HSA-9856532
P31749
Mechanical load activates signaling by PIEZO1 ...
IEA
Homo sapiens
1
111
R-HSA-9860931
P31749
Response of endothelial cells to shear stress
IEA, TAS
Homo sapiens
0
112 rows × 6 columns
# lowest
akt_path[akt_path.lowest== 1 ].shape
source
add_reactome_ref
add_reactome_ref (df, uniprot)
out = add_reactome_ref(out,'P31749' )
Bar plot of pathways
source
plot_path
plot_path (react_df, p_type='FDR', ref_id_list=None, ref_col=None,
top_n=10, max_label_length=80)
Plot the output of get_reactome. If ref_df is provided, bars corresponding to pathways in ref_df are shown in dark red.
react_df
the output df of get_reactome
p_type
str
FDR
ref_id_list
NoneType
None
list of reactome_id
ref_col
NoneType
None
column in reac_df, 1 or 0 to indicate whether it’s in ref
top_n
int
10
max_label_length
int
80
plot_path(out)
plt.title('PI3K Pathways' );
# All level
plot_path(out,p_type= 'FDR' ,ref_id_list= akt_path.reactome_id,top_n= 15 )
plt.title('PI3K Pathways (with highlight as overlap with all level Reactome database)' );
0
GRB2 events in ERBB2 signaling
R-HSA-1963640
1.221245e-15
14.913
0
0
1
Signaling by ERBB2 ECD mutants
R-HSA-9665348
1.221245e-15
14.913
0
0
2
Signaling by ERBB2 KD Mutants
R-HSA-9664565
1.221245e-15
14.913
0
0
3
Signaling by ERBB2 in Cancer
R-HSA-1227990
1.221245e-15
14.913
0
0
4
Signaling by ERBB2 TMD/JMD mutants
R-HSA-9665686
1.221245e-15
14.913
0
0
# All level, use ref_col
plot_path(out,p_type= 'FDR' ,ref_col= 'P31749_path_all' ,top_n= 15 )
plt.title('PI3K Pathways (with highlight as overlap with all level Reactome database)' );
# All level
plot_path(out,p_type= 'FDR' ,ref_col= 'P31749_path_lowest' ,top_n= 15 )
plt.title('PI3K Pathways (with highlight as overlap with lowest level Reactome database)' );
Overlap
source
get_overlap
get_overlap (react_df, ref_id_list=None, ref_col=None, p_type='FDR',
thr=0.05, plot=True)
react_df
ref_id_list
NoneType
None
ref_col
NoneType
None
column in react_df, 1 or 0 to indicate whether it’s in ref
p_type
str
FDR
thr
float
0.05
original threshold of p value, will be log10 transformed
plot
bool
True
get_overlap(out, ref_id_list= akt_path.reactome_id,plot= True )
get_overlap(out, ref_col= 'P31749_path_all' )
get_overlap(out, ref_col= 'P31749_path_lowest' )
Pipeline
out = get_reactome(pi3ks,p_type= 'FDR' )
out = add_reactome_ref(out,'P31749' ) # kinase uniprot
accuracy = get_overlap(out, ref_col= 'P31749_path_all' ,plot= True ) # if lowest, change all to lo