Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.
## Deliverables We have implemented a biologically
informed multi-stage machine learning framework termed
BioM2 specifically for phenotype prediction using
omics-scale data based on prior biological information including gene
ontological (GO) and/or KEGG pathways.
Features of BioM2 in a nutshell:
Shunjie Zhang —- (E-mail: 202220148490@mail.scut.edu.cn)
BioM2 has been uploaded to CRAN .
install.packages('BioM2')The latest release can be installed using the code provided below.
install.packages("devtools")
devtools::install_github("jkkomm/BioM2")BioM2 is built on the mlr3 package. To use additional learners, please install the mlr3extralearners package.
remotes::install_github("mlr-org/mlr3extralearners@*release")BioM2 requires: - Genome-wide DNA methylation data / Bulk RNA-seq data - Feature annotation data - Pathway annotation data (Gene ontological and KEGG pathways are used in this tutorial)
<<< Data requirements >>>
-----------------------------------------------------------------------------
## [Genome-wide DNA methylation data] (data.frame)
# First column name must be 'label', and the rest are the features (e.g., CpGs).
label cg21870274 cg09499020 cg16535257 cg00168193
0 0.0057 0.0002 -0.0313 0.0002
0 -0.0317 -0.0444 -0.0578 -0.0160
1 -0.0341 -0.0541 -0.0056 -0.0230
1 0.0811 -0.0029 0.0049 0.0274
1 -0.0187 0.0475 0.1168 0.0169
0 -0.0158 0.0032 -0.0173 0.0133
--------------------------------------------------------------------------------
## [Feature annotation data] (data.frame)
# The data frame must contain the two column names 'ID' and 'entrezID' .
ID entrezID symbol
cg00000029 5934 RBL2
cg00000109 64778 FNDC3B
cg00000155 221927 BRAT1
cg00000221 162282 ANKFN1
cg00000236 7419 VDAC3
cg00000289 87 ACTN1
-----------------------------------------------------------------------------
## [Pathway annotation data] (list)
# The name of each subset of the list is the ID of the pathway, and each subset contains a vector of gene entrezIDs.
List of 15719
$ GO:0000002: chr [1:31] "142" "291" "1763" "1890" ...
$ GO:0000003: chr [1:1513] "2" "18" "49" "51" ...
$ GO:0000012: chr [1:12] "142" "1161" "2074" "3981" ...
$ GO:0000017: chr [1:2] "6523" "6524"
$ GO:0000018: chr [1:131] "60" "86" "142" "604" ...
$ GO:0000019: chr [1:7] "2068" "4292" "4361" "7014" ...
$ GO:0000022: chr [1:9] "4926" "6795" "9055" "9212" ...
$ GO:0000023: chr [1:3] "2548" "2595" "8972"
-----------------------------------------------------------------------------
To predict the phenotype, follow these steps.
library(mlr3verse)
library(caret)
library(parallel)
library(BioM2)
result=BioM2 ( TrainData = data , TestData = NULL , ## If you only have one dataset
pathlistDB = pathlistDB , ## ==>> [Pathway annotation data]
FeatureAnno = FeatureAnno , ## ==>> [Feature annotation data]
classifier = 'liblinear' , nfolds = 5 , ## Choose your learner( use "lrns()" ) , currently only cross-validation is supported
Inner_CV = FALSE , inner_folds=10 , ## Whether to use nested resampling
cores = 5 ## Parallel support
)
...(More Detail)
[1] "-----------------------------------------------------------"
[1] "------------========<<<< Completed! >>>>======-----------"
[1] "-----------------------------------------------------------"
[1] "{|>>>=====Learner: liblinear---Performance Metric---==>> AUC:0.953 ACC:0.876 PCCs:0.785 ======<<<|}"
resampling_id learner_name AUC ACC PCCs
1 1 liblinear 0.9642857 0.9285714 0.8309358
2 2 liblinear 0.9387755 0.7857143 0.7114370
3 3 liblinear 0.9132653 0.8214286 0.7304734
4 4 liblinear 0.9940828 0.9615385 0.9101721
5 5 liblinear 0.9526627 0.8846154 0.7415218
Time difference of 10.99379 mins
[1] "######—————— Well Done！！！——————######"
> str(result)
$ Prediction :List of 5
..$ Resample No. 1:'data.frame': 28 obs. of 2 variables:
.. ..$ sample : chr [1:28] "201172200006_R04C01" "201172200008_R08C01" "201172200011_R04C01" "201172200012_R07C01" ...
.. ..$ prediction: num [1:28] 0.354 0.781 0.723 0.544 0.401 ...
..$ Resample No. 2:'data.frame': 27 obs. of 2 variables:
.. ..$ sample : chr [1:27] "201172200003_R06C01" "201172200004_R06C01" "201172200005_R06C01" "201172200011_R05C01" ...
.. ..$ prediction: num [1:27] 0.97 0.9942 0.2156 0.6554 0.0587 ...
..$ Resample No. 3:'data.frame': 27 obs. of 2 variables:
.. ..$ sample : chr [1:27] "201172200001_R07C01" "201172200006_R06C01" "201172200006_R08C01" "201172200019_R07C01" ...
.. ..$ prediction: num [1:27] 0.024 0.9319 0.9827 0.0119 0.9646 ...
..$ Resample No. 4:'data.frame': 27 obs. of 2 variables:
.. ..$ sample : chr [1:27] "201172200006_R05C01" "201172200012_R08C01" "201172200013_R04C01" "201172200050_R05C01" ...
.. ..$ prediction: num [1:27] 0.925 0.197 0.914 0.944 0.421 ...
..$ Resample No. 5:'data.frame': 27 obs. of 2 variables:
.. ..$ sample : chr [1:27] "201172200001_R06C01" "201172200004_R04C01" "201172200005_R07C01" "201172200016_R04C01" ...
.. ..$ prediction: num [1:27] 0.0504 0.4289 0.1212 0.9876 0.0164 ...
$ Metric :'data.frame': 5 obs. of 5 variables:
..$ resampling_id: int [1:5] 1 2 3 4 5
..$ learner_name : chr [1:5] "liblinear" "liblinear" "liblinear" "liblinear" ...
..$ AUC : num [1:5] 0.964 0.938 0.913 0.994 0.952
..$ ACC : num [1:5] 0.928 0.785 0.821 0.961 0.884
..$ PCCs : num [1:5] 0.830 0.711 0.730 0.910 0.741
$ TotalMetric: Named num [1:3] 0.953 0.876 0.785
..- attr(*, "names")= chr [1:3] "AUC" "ACC" "PCCs"
To explore the potential impact of biological pathways on the disease/phenotype, set the parameter (target=‘pathways’). Show the association between each biological pathway used for prediction and the phenotype.
library(mlr3verse)
library(caret)
library(parallel)
library(BioM2)
result=BioM2 ( TrainData = data , TestData = NULL ,
pathlistDB = pathlistDB ,
FeatureAnno = FeatureAnno ,
classifier = 'liblinear' , nfolds = 5 ,
target='pathways', ##==>> [ target = 'pathways']
cores = 5
)
[1] "-----------------------------------------------------------"
[1] "------------========<<<< Completed! >>>>======-----------"
[1] "-----------------------------------------------------------"
id cor p.value adjust_p.value
254 GO:0001708 0.6421436 4.839108e-17 1.440668e-13
165 GO:0072073 0.6349709 1.102139e-16 3.287145e-13
1141 GO:0072009 0.6263595 3.534259e-16 1.051309e-13
692 GO:0035265 0.6217819 4.435684e-16 1.323840e-12
557 GO:0003341 0.6499000 7.614604e-16 2.264562e-12
term
254 cell fate specification
165 kidney epithelium development
1141 nephron epithelium development
692 organ growth
557 cilium movement
Time difference of 8.649476 mins
[1] "######—————— Well Done！！！——————######"
> str(result)
List of 2
$ PathwaysMatrix: num [1:136, 1:2974] 0 1 1 0 0 0 1 0 1 1 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:2974] "label" "GO:0000002" "GO:0000018" "GO:0000038" ...
$ PathwaysResult:'data.frame': 2973 obs. of 5 variables:
..$ id : chr [1:2973] "GO:0001708" "GO:0072073" "GO:0072009" "GO:0035265" ...
..$ cor : num [1:2973] 0.64 0.634 0.626 0.625 0.621 ...
..$ p.value : num [1:2973] 4.83e-17 1.10e-16 3.53e-16 4.43e-16 7.61e-16 ...
..$ adjust_p.value: num [1:2973] 1.44e-13 3.28e-13 1.05e-12 1.32e-12 2.26e-12 ...
..$ term : chr [1:2973] "cell fate specification" "kidney epithelium development" "nephron epithelium development" "organ growth" ...
A pathway matrix can be obtained by using BioM2(, target = ‘pathways’). The WGCNA method aggregates pathways with similar expression patterns into a module, and uses biological semantic information to assist in screening modules with high biological interpretability, and compares these biological pathway modules association with phenotype.
library(mlr3verse)
library(caret)
library(parallel)
library(BioM2)
result=BioM2 ( TrainData = data , TestData = NULL ,
pathlistDB = pathlistDB ,
FeatureAnno = FeatureAnno ,
classifier = 'liblinear' , nfolds = 5 ,
target='pathways', ##==>> [ target = 'pathways']
cores = 5
)
Matrix=result$PathwaysMatrix
library(WGCNA)
Para=FindParaModule(pathways_matrix = Matrix, minModuleSize = c(10,15,20,25), mergeCutHeight=c(0.1,0.15,0.2,0.25,0.3,0.35,0.4))
> str(Para)
List of 2
$ TotalResult :'data.frame': 33 obs. of 6 variables:
..$ mergeCutHeight : num [1:33] 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.1 0.15 0.2 ...
..$ Number_clusters : int [1:33] 160 156 152 144 122 100 65 115 114 113 ...
..$ Mean_number_pathways: num [1:33] 18.4 18.8 19.3 20.4 21.3 ...
..$ Mean_Fraction : num [1:33] 65 64.7 64.4 64.3 64.3 ...
..$ Sd_Fraction : num [1:33] 18.4 18.5 18.2 18.5 18.2 ...
..$ minModuleSize : num [1:33] 10 10 10 10 10 10 10 ...
$ BestParameter: Named num [1:3] 8 10 0.4
..- attr(*, "names")= chr [1:3] "power" "ModuleSize" "mergeCutHeight"
The optimal parameters can be provided by FindParaModule() or chosen by the user. The modules relevant to the illness can then be obtained, with high biological interpretability.
library(WGCNA)
Modules=PathwaysModule(pathways_matrix = Matrix , control_label = 0, minModuleSize = 10, mergeCutHeight = 0.4, cutoff = 70)
> str(Modules)
List of 4
$ ModuleResult :'data.frame': 2939 obs. of 2 variables:
..$ ID : chr [1:2939] "GO:0000002" "GO:0000018" "GO:0000038" "GO:0000041" ...
..$ cluster: num [1:2939] 0 1 62 30 35 27 50 1 50 50 ...
$ RAW_PathwaysModule:'data.frame': 66 obs. of 5 variables:
..$ module : chr [1:66] "ME0" "ME1" "ME10" "ME11" ...
..$ Num_pathways : int [1:66] 200 1351 41 38 38 37 37 35 34 28 ...
..$ Fraction : num [1:66] 36.5 44.3 73.2 71.1 63.2 ...
..$ adjust_pvalue: num [1:66] 1.57e-06 4.85e-09 9.47e-05 2.03e-03 4.32e-06 ...
..$ cor : num [1:66] 0.504 0.574 0.392 0.311 0.458 ...
$ DE_PathwaysModule :'data.frame': 9 obs. of 5 variables:
..$ module : chr [1:9] "ME28" "ME52" "ME40" "ME65" ...
..$ Num_pathways : int [1:9] 16 8 11 6 41 17 14 15 7
..$ Fraction : num [1:9] 81.2 75 90.9 100 73.2 ...
..$ adjust_pvalue: num [1:9] 6.81e-07 1.57e-06 1.92e-05 1.97e-05 9.47e-05 ...
..$ cor : num [1:9] 0.418 0.454 0.4 0.435 0.392 ...
$ Matrix : num [1:136, 1:2974] 0 1 1 0 0 0 1 0 1 1 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:2974] "label" "GO:0000002" "GO:0000018" "GO:0000038" ...
ModulesInner = ShowModule(Modules,c(25,27,34))
> str(ModulesInner)
List of 3
$ ME25:'data.frame': 16 obs. of 4 variables:
..$ GO : chr [1:16] "GO:0006023" "GO:0006024" "GO:0006029" "GO:0015012" ...
..$ Name : chr [1:16] "aminoglycan biosynthetic process" "glycosaminoglycan biosynthetic process" "proteoglycan metabolic process" "heparan sulfate proteoglycan biosynthetic process" ...
..$ Ancestor : chr [1:16] "organic substance metabolic process" "organic substance metabolic process" "organic substance metabolic process" "organic substance metabolic process" ...
..$ AncestorGO: chr [1:16] "GO:0071704" "GO:0071704" "GO:0071704" "GO:0071704" ...
$ ME27:'data.frame': 11 obs. of 4 variables:
..$ GO : chr [1:11] "GO:0007173" "GO:0007176" "GO:0038127" "GO:0042058" ...
..$ Name : chr [1:11] "epidermal growth factor receptor signaling pathway" "regulation of epidermal growth factor-activated receptor activity" "ERBB signaling pathway" "regulation of epidermal growth factor receptor signaling pathway" ...
..$ Ancestor : chr [1:11] "regulation of cellular process" "regulation of cellular process" "regulation of cellular process" "regulation of cellular process" ...
..$ AncestorGO: chr [1:11] "GO:0050794" "GO:0050794" "GO:0050794" "GO:0050794" ...
$ ME34:'data.frame': 8 obs. of 4 variables:
..$ GO : chr [1:8] "GO:0003351" "GO:0007288" "GO:0030317" "GO:0060294" ...
..$ Name : chr [1:8] "epithelial cilium movement involved in extracellular fluid movement" "sperm axoneme assembly" "flagellated sperm motility" "cilium movement involved in cell motility" ...
..$ Ancestor : chr [1:8] "movement of cell or subcellular component" "movement of cell or subcellular component" "movement of cell or subcellular component" "movement of cell or subcellular component" ...
..$ AncestorGO: chr [1:8] "GO:0006928" "GO:0006928" "GO:0006928" "GO:0006928" ...
Visualisation of significant pathway-level features
#Load the required R packages
library(ggplot2)
library(viridis)
result=BioM2 ( TrainData = data , TestData = NULL ,
pathlistDB = pathlistDB ,
FeatureAnno = FeatureAnno ,
classifier = 'liblinear' , nfolds = 5 ,
target='pathways', ##==>> [ target = 'pathways']
cores = 5
)
PlotPathFearture(BioM2_pathways_obj=result , pathlistDB = pathlistDB)
Visualisation Original features that make up the pathway
#Load the required R packages
library(CMplot)
#Select the top 10 most significant pathways
PathNames=result$PathwaysResult$id[1:10]
PlotPathInner(data=TrainData,pathlistDB=pathlistDB,FeatureAnno=FeatureAnno,
PathNames=PathNames)
Network diagram of pathways-level features
#Load the required R packages
library(igraph)
library(ggnetwork)
library(ggplot2)
result=BioM2 ( TrainData = data , TestData = NULL ,
pathlistDB = pathlistDB ,
FeatureAnno = FeatureAnno ,
classifier = 'liblinear' , nfolds = 5 ,
target='pathways', ##==>> [ target = 'pathways']
cores = 5
)
#Select the top 10 most significant pathways
PathNames=result$PathwaysResult$id[1:10]
PlotPathNet(data=TrainData,BioM2_pathways_obj=result,
FeatureAnno = FeatureAnno,pathlistDB=pathlistDB,
PathNames=PathNames)
VisMultiModule ( ,BioM2_pathways_obj ) ： Each point represents a pathway, and each pathway belongs to a biological category. The higher the point, the more significant the difference between the pathway and the phenotype
#Load the required R packages
library(ggpubr)
library(ggthemes)
library(CMplot)
library(ggplot2)
library(RColorBrewer)
library(webshot)
library(wordcloud2)
library(jiebaR)
library("htmlwidgets")
result=BioM2 ( TrainData = data , TestData = NULL ,
pathlistDB = pathlistDB ,
FeatureAnno = FeatureAnno ,
classifier = 'liblinear' , nfolds = 5 ,
target='pathways', ##==>> [ target = 'pathways']
cores = 5
)
VisMultiModule(BioM2_pathways_obj = result)
VisMultiModule ( , FindParaModule_obj ) ： Visualize the process of selecting optimal parameters based on biological terms.
Matrix=result$PathwaysMatrix
Para=FindParaModule(pathways_matrix = Matrix, minModuleSize = c(6,7,8), mergeCutHeight=c(0.2,0.25,0.3,0.35,0.4,0.45,0.5))
VisMultiModule(FindParaModule_obj=Para)
VisMultiModule ( , PathwaysModule_obj ) ： Each point represents a path, and points of the same color belong to the same illness-relevant module
Matrix=result$PathwaysMatrix
Modules=PathwaysModule(pathways_matrix = Matrix , control_label = 0, minModuleSize = 6, mergeCutHeight = 0.3, cutoff = 70)
VisMultiModule(PathwaysModule_obj=Modules)
Violin plot showing statistics for the pathway modules
Matrix=result$PathwaysMatrix
Modules=PathwaysModule(pathways_matrix = Matrix , control_label = 0, minModuleSize = 6, mergeCutHeight = 0.3, cutoff = 70)
VisMultiModule(PathwaysModule_obj=Modules,volin=TURE,control_label=0,module=c(14,15,28,4)))
VisMultiModule ( , ShowModule_obj ) ： Summarize the biological information of the pathways in the module with a wordcloud.
Matrix=result$PathwaysMatrix
Modules=PathwaysModule(pathways_matrix = Matrix , control_label = 0, minModuleSize = 6, mergeCutHeight = 0.3, cutoff = 70)
ModulesInner = ShowModule(Modules,c(14,15,28,4))
VisMultiModule(ShowModule_obj=ModulesInner)
Correlalogram for illness-relevant modules
Matrix=result$PathwaysMatrix
Modules=PathwaysModule(pathways_matrix = Matrix , control_label = 0, minModuleSize = 6, mergeCutHeight = 0.3, cutoff = 70)
PlotCorModule=(PathwaysModule_obj=Modules)
# Citation - NIPS ML4H submission: Chen, J. and Schwarz,
E., 2017. BioMM: Biologically-informed Multi-stage Machine learning for
identification of epigenetic fingerprints. arXiv preprint
arXiv:1712.00336. - Chen, Junfang, et al. “Association of a reproducible
epigenetic risk profile for schizophrenia with brain methylation and
function.” JAMA psychiatry 77.6 (2020): 628-636.