- Introduction
- T-ALL Example dataset
- 1) Obtain clinical, lesion and gene expression data
- 2) Retrieve Genomic Annotations for Genes and Regulatory Features
- 3) Retrieve Chromosome Size Data
- 4) Run Genomic Random Interval (GRIN) Analysis
- 5) Now, let’s Take a Look on the GRIN Output Results:
- 6) Write GRIN Results
- 7) Genome-wide Lesion Plot
- 8) Stacked Barplot for a List of Genes of Interest
- 9) Prepare GRIN Lesion Matrix for an OncoPrint Type of Display
- 10) Lesion Plots for a Certain Gene, Locus or the Whole Chromosome
- 11) Gene-Lesion Matrix for later computations
- Associate Lesions with EXpression (ALEX)
- 12) Prepare Expression and Lesion Data for ALEX-KW Test and ALEX-plots
- 13) Run Kruskal-Wallis Test for Association between Lesion and Expression Data
- 14) Now, let’s Take a Look on the ALEX Kruskal-wallis Results Table:
- 15) Waterfall Plots for Side-by-side Representation of Lesion and Expression Data
- 16) Visualize Lesion and Expression Data by Pathway (JAK/STAT Pathway)
- 17) Lesion Binary Matrix for Association Analysis with Clinical Outcomes
- 18) Run Association Analysis for Lesions with Clinical Outcomes
- 19) Now, let’s Take a Look on the Results Table of the Association between Lesions and Treatment Outcomes:
Introduction to the GRIN2 Package
Abdelrahman Elsayed, PhD and Stanley Pounds, PhD
2025-06-16
Introduction
The GRIN2 package is an improved version of GRIN software that streamlines its use in practice to analyze genomic lesion data, accelerate its computing, and expand its analysis capabilities to answer additional scientific questions including a rigorous evaluation of the association of genomic lesions with RNA expression.
T-ALL Example dataset
- Genomic Landscape of T-ALL
- RNA-seq and WES data for 265 patients identified 6,861 genomic lesions
- Clinical outcome data
1) Obtain clinical, lesion and gene expression data
data(clin_data)
data(lesion_data)
data(expr_data)
# Please make sure that coordinates in the lesion data file (loc.start and loc.end) are based on GRCh38 (hg38) genome assembly. Multiple tools that include the UCSC LiftOver tool (<https://genome.ucsc.edu/cgi-bin/hgLiftOver>) can be used for such conversions.
head(lesion_data)
#> ID chrom loc.start loc.end lsn.type
#> 1 PARFIH 16 67616879 67616879 mutation
#> 2 PARFIH 3 49722238 49722238 mutation
#> 3 PARFIH 1 6253852 6253852 mutation
#> 4 PARFIH 1 235766122 235766122 mutation
#> 5 PARFIH 4 141132499 141132499 mutation
#> 6 PARFIH 9 27206739 27206739 mutation
# Specify a folder on your local machine to store the analysis results:
# resultsPath=tempdir()
# knitr::opts_knit$set(root.dir = normalizePath(path = resultsPath))
2) Retrieve Genomic Annotations for Genes and Regulatory Features
# A subset (417 genes) retrieved from ensembl BioMart will be used as an example gene annotation data file:
data(hg38_gene_annotation)
# To retrieve a complete annotation data for genes and regulatory features from ensembl BioMart, users can use get.ensembl.annotation function:
# hg38.ann <- get.ensembl.annotation("Human_GRCh38")
head(hg38_gene_annotation)
#> gene chrom loc.start loc.end
#> 20 ENSG00000131697 1 5862811 5992473
#> 123 ENSG00000162607 1 62436297 62451804
#> 280 ENSG00000143653 1 246724409 246768137
#> 376 ENSG00000136643 1 213051233 213274774
#> 387 ENSG00000035687 1 244408494 244451909
#> 431 ENSG00000142676 1 23691742 23696835
#> description
#> 20 nephrocystin 4 [Source:HGNC Symbol;Acc:HGNC:19104]
#> 123 ubiquitin specific peptidase 1 [Source:HGNC Symbol;Acc:HGNC:12607]
#> 280 saccharopine dehydrogenase (putative) [Source:HGNC Symbol;Acc:HGNC:24275]
#> 376 ribosomal protein S6 kinase C1 [Source:HGNC Symbol;Acc:HGNC:10439]
#> 387 adenylosuccinate synthase 2 [Source:HGNC Symbol;Acc:HGNC:292]
#> 431 ribosomal protein L11 [Source:HGNC Symbol;Acc:HGNC:10301]
#> gene.name biotype chrom.strand chrom.band
#> 20 NPHP4 protein_coding -1 p36.31
#> 123 USP1 protein_coding 1 p31.3
#> 280 SCCPDH protein_coding 1 q44
#> 376 RPS6KC1 protein_coding 1 q32.3
#> 387 ADSS2 protein_coding -1 q44
#> 431 RPL11 protein_coding 1 p36.113) Retrieve Chromosome Size Data
# chromosome size data:
head(hg38_chrom_size)
#> chrom size
#> 1 1 248956422
#> 2 2 242193529
#> 3 3 198295559
#> 4 4 190214555
#> 5 5 181538259
#> 6 6 170805979
# chromosome size data can be also retrieved for GRCh38 (hg38) genome build from chr.info txt file available on UCSC genome browser:
4) Run Genomic Random Interval (GRIN) Analysis
# grin.stats is the function that can be used to excute the GRIN statistical framework:
grin.results=grin.stats(lesion_data,
hg38_gene_annotation,
hg38_chrom_size)
# In addition to annotated genes, users can run GRIN analysis on regulatory sequences from ensembl regulatory build and FANTOM5 project.
5) Now, let’s Take a Look on the GRIN Output Results:
# Extract GRIN results table:
grin.table=grin.results$gene.hits
sorted.results <- grin.table[order(as.numeric(as.character(grin.table$p2.nsubj))),]First section of GRIN results table will include gene annotation in addition to the number of subjects affected by each type of lesions:
head(sorted.results[,c(7,11:14)])
#> gene.name nsubj.fusion nsubj.gain nsubj.loss nsubj.mutation
#> 2760 PTEN 0 2 23 37
#> 16361 MYB 4 26 2 13
#> 4114 ETV6 2 0 15 7
#> 4755 CDKN1B 0 0 20 4
#> 2818 WT1 0 0 9 24
#> 19924 DDX3X 2 1 2 4Results will also include the probability (p) and FDR adjusted q-value for each gene to be affected by each type of lesion:
head(sorted.results[,c(7,19:22)])
#> gene.name q.nsubj.fusion q.nsubj.gain q.nsubj.loss q.nsubj.mutation
#> 2760 PTEN 1.000000e+00 1.00000e+00 6.962771e-35 1.544248e-76
#> 16361 MYB 3.147439e-09 3.00149e-50 9.120621e-01 7.453079e-27
#> 4114 ETV6 5.973467e-03 1.00000e+00 1.044321e-12 1.485813e-06
#> 4755 CDKN1B 1.000000e+00 1.00000e+00 1.089932e-21 4.375947e-06
#> 2818 WT1 1.000000e+00 1.00000e+00 1.644414e-06 6.329540e-50
#> 19924 DDX3X 5.527172e-05 1.00000e+00 9.120621e-01 1.554019e-06Another important part of the output is the constellation results testing if the gene is affected by one type of lesions (p1.nusubj) or a constellation of two types of lesions (p2.nsubj), three types of lesions (p3.nsubj), etc.. with FDR adjusted q-values added to the table as well:
head(sorted.results[,c(7,27:30)])
#> gene.name q1.nsubj q2.nsubj q3.nsubj q4.nsubj
#> 2760 PTEN 1.366953e-76 2.096383e-69 1.000000e+00 1
#> 16361 MYB 4.981666e-51 5.754654e-53 4.590287e-29 1
#> 4114 ETV6 1.530741e-12 9.529379e-12 1.255076e-09 1
#> 4755 CDKN1B 8.925351e-22 7.501134e-11 1.000000e+00 1
#> 2818 WT1 6.788063e-50 8.984019e-11 1.000000e+00 1
#> 19924 DDX3X 7.495956e-07 2.637376e-10 1.000000e+00 1The second part of the results table report the same set of results but for the number of hits affecting each gene for each lesion type instead of the number of unique affected subjects. For example, if NOTCH1 gene is affected by 4 mutations in the same subject, this event will be counted as 4 hits in the n.hits stats but 1 subject in the n.subj stats:
head(sorted.results[,c(7,31:34)])
#> gene.name nhit.fusion nhit.gain nhit.loss nhit.mutation
#> 2760 PTEN 0 2 39 45
#> 16361 MYB 4 27 2 14
#> 4114 ETV6 2 0 16 7
#> 4755 CDKN1B 0 0 20 4
#> 2818 WT1 0 0 9 27
#> 19924 DDX3X 2 1 2 56) Write GRIN Results
# write.grin.xlsx function return an excel file with multiple sheets that include GRIN results table, interpretation of each column in the results, and methods paragraph
# write.grin.xlsx(grin.results, "T-ALL_GRIN_result_annotated_genes.xlsx")
# To return the results table without other information (will be helpful in case of large lesion data files where the gene.lsn.data sheet will be > 1 million rows that halt the write.grin.xlsx function).
grin.res.table=grin.results$gene.hits
7) Genome-wide Lesion Plot
genomewide.plot=genomewide.lsn.plot(grin.results,
max.log10q=50) 
Figure 1. Genome-wide lesion plot
# This function use the list of grin.results
8) Stacked Barplot for a List of Genes of Interest
# This barplot shows the number of patients affected by different types of lesions in a list of genes of interest:
count.genes=as.vector(c("CDKN2A", "NOTCH1", "CDKN2B", "TAL1", "FBXW7", "PTEN", "IRF8",
"NRAS", "BCL11B", "MYB", "LEF1","RB1", "MLLT3", "EZH2", "ETV6",
"CTCF", "JAK1", "KRAS", "RUNX1", "IKZF1", "KMT2A", "RPL11", "TCF7",
"WT1", "JAK2", "JAK3", "FLT3"))
# return the stacked barplot
grin.barplt(grin.results,
count.genes)
Figure 2. stacked barplot with number of patients affected by different types of lesions in a list of genes of interest
9) Prepare GRIN Lesion Matrix for an OncoPrint Type of Display
# First identify the list of genes to be included in the oncoprint:
oncoprint.genes=as.vector(c("ENSG00000101307", "ENSG00000171862", "ENSG00000138795",
"ENSG00000139083", "ENSG00000162434", "ENSG00000134371",
"ENSG00000118058", "ENSG00000171843", "ENSG00000139687",
"ENSG00000184674", "ENSG00000118513", "ENSG00000197888",
"ENSG00000111276", "ENSG00000258223", "ENSG00000187266",
"ENSG00000174473", "ENSG00000133433", "ENSG00000159216",
"ENSG00000107104", "ENSG00000099984", "ENSG00000078403",
"ENSG00000183150", "ENSG00000081059", "ENSG00000175354",
"ENSG00000164438"))
# Prepare a lesion matrix for the selected list of genes with each row as a gene and each column is a patient (this matrix is compatible with oncoPrint function in ComplexHeatmap package):
oncoprint.mtx=grin.oncoprint.mtx(grin.results,
oncoprint.genes)
head(oncoprint.mtx[,1:6])
#> PASGFH PASKSY PASTDU PASYWF PATDRC PATFWF
#> TCF7 loss; loss; mutation; loss; loss; loss;
#> KANK1 gain;
#> CDKN1B loss; loss; loss; loss;
#> MYB mutation;
#> LEF1
#> ETV6 loss; loss; loss; loss;
# Use onco.print.props function to specify a height proportion for each lesion category:
onco.props<-onco.print.props(lesion.data,
hgt = c("gain"=5, "loss"=4, "mutation"=2, "fusion"=1))
#> Error: object 'lesion.data' not found
column_title = "" # optional
# use oncoprint function from ComplexHeatmap library to plot the oncoprint:
10) Lesion Plots for a Certain Gene, Locus or the Whole Chromosome
# First call the data for "hg38_cytoband":
data(hg38_cytoband)
# Plots Showing a Selected Type of Lesion (Ex: deletions) Affecting a region of Interest:
cdkn2a.locus=lsn.transcripts.plot(grin.results, transTrack = FALSE,
hg38.cytoband=hg38_cytoband, chrom=9, plot.start=19900000, plot.end=25600000, lesion.grp = "loss", spec.lsn.clr = "blue")
Figure 3. lesion plot showing all deletions affecting CDKN2A locus on chromosome 9
# This type of lesion plots can be also prepared for a certain gene or locus of interest with transcripts track added using the same lsn.transcripts.plot function.
# Plots Showing Different Types of Lesions Affecting the whole chromosome:
chrom.plot=lsn.transcripts.plot(grin.results, transTrack = FALSE,
hg38.cytoband=hg38_cytoband, chrom=9, plot.start=1, plot.end=141000000)
Figure 4. Plots showing all different types of lesions affecting the whole chromosome
11) Gene-Lesion Matrix for later computations
# Prepare gene and lesion data for later computations
# This lesion matrix has all lesion types that affect a single gene in one row. It can be used to run association analysis with expression data (part of alex.prep.lsn.expr function)
# First step is to prepare gene and lesion data for later computations
gene.lsn=prep.gene.lsn.data(lesion_data,
hg38_gene_annotation)
# Then determine lesions that overlap each gene (locus)
gene.lsn.overlap= find.gene.lsn.overlaps(gene.lsn)
# Finally, build the lesion matrix using prep.lsn.type.matrix function:
gene.lsn.type.mtx=prep.lsn.type.matrix(gene.lsn.overlap,
min.ngrp=5)
# prep.lsn.type.matrix function return each gene in a row, if the gene is affected by multiple types of lesions (for example gain AND mutations), entry will be denoted as "multiple" for this specific patient.
# min.ngrp can be used to specify the minimum number of patients with a lesion to be included in the final lesion matrix.
head(gene.lsn.type.mtx[,1:5])
#> PARASZ PARAYM PARCVM PAREGZ PARFDL
#> ENSG00000005700 "loss" "none" "loss" "none" "none"
#> ENSG00000010810 "loss" "none" "loss" "none" "none"
#> ENSG00000014123 "loss" "none" "loss" "none" "none"
#> ENSG00000056972 "loss" "none" "loss" "none" "none"
#> ENSG00000057663 "loss" "none" "loss" "none" "none"
#> ENSG00000065615 "loss" "none" "loss" "none" "none"Associate Lesions with EXpression (ALEX)
12) Prepare Expression and Lesion Data for ALEX-KW Test and ALEX-plots
# alex.prep.lsn.expr function prepare expression, lesion data and return the set of genes with both types of data available ordered by gene IDs in rows and patient IDs in columns:
alex.data=alex.prep.lsn.expr(expr_data,
lesion_data,
hg38_gene_annotation,
min.expr=1,
min.pts.lsn=5)
# ALEX ordered lesion data:
alex.lsn=alex.data$alex.lsn
head(alex.lsn[,1:5])
#> PARASZ PARAYM PARCVM PAREGZ PARFDL
#> ENSG00000005339 none none none none none
#> ENSG00000005700 loss none loss none none
#> ENSG00000006283 none none none none none
#> ENSG00000010438 none loss none none none
#> ENSG00000010810 loss none loss none none
#> ENSG00000014123 loss none loss none none
# ALEX ordered expression data:
alex.expr=alex.data$alex.expr
head(alex.expr[,1:5])
#> PARASZ PARAYM PARCVM PAREGZ PARFDL
#> ENSG00000005339 4.012 3.718 3.253 2.294 3.568
#> ENSG00000005700 2.503 3.738 3.011 3.524 3.437
#> ENSG00000006283 0.035 0.000 0.016 0.005 0.043
#> ENSG00000010438 0.155 0.000 0.454 0.000 0.153
#> ENSG00000010810 3.992 4.402 3.985 3.934 4.443
#> ENSG00000014123 3.009 4.245 3.699 3.032 3.93213) Run Kruskal-Wallis Test for Association between Lesion and Expression Data
# KW.hit.express function runs Kruskal-Wallis test for association between lesion groups and expression level of the same corresponding gene:
alex.kw.results=KW.hit.express(alex.data,
hg38_gene_annotation,
min.grp.size=5)
14) Now, let’s Take a Look on the ALEX Kruskal-wallis Results Table:
# order the genes by the ones with most significant KW q-value:
sorted.kw <- alex.kw.results[order(as.numeric(as.character(alex.kw.results$q.KW))),]First section of the results table will include gene annotation in addition to the kruskal-wallis test p and q values evaluating if there’s a statistically significant differences in the gene expression level between different lesion groups:
head(sorted.kw[,c(6,7,11,12)])
#> gene.name biotype p.KW q.KW
#> ENSG00000198642 KLHL9 protein_coding 7.141766e-21 1.963986e-18
#> ENSG00000120159 CAAP1 protein_coding 2.417600e-20 3.324200e-18
#> ENSG00000162367 TAL1 protein_coding 1.152926e-18 1.056849e-16
#> ENSG00000137073 UBAP2 protein_coding 4.612214e-18 3.170897e-16
#> ENSG00000107185 RGP1 protein_coding 1.465553e-16 8.060539e-15
#> ENSG00000147889 CDKN2A protein_coding 5.650917e-16 2.590004e-14For each gene, results table will include the number of patients affected by each type of lesion in addition to number of patients affected by multiple types of lesions in the same gene and patients without any lesion:
head(sorted.kw[,c(13:18)])
#> fusion_n.subjects gain_n.subjects loss_n.subjects
#> ENSG00000198642 0 0 99
#> ENSG00000120159 0 1 44
#> ENSG00000162367 54 0 0
#> ENSG00000137073 0 0 36
#> ENSG00000107185 0 0 36
#> ENSG00000147889 1 0 199
#> multiple_n.subjects mutation_n.subjects none_n.subjects
#> ENSG00000198642 0 0 164
#> ENSG00000120159 0 0 218
#> ENSG00000162367 0 0 209
#> ENSG00000137073 0 0 227
#> ENSG00000107185 0 0 227
#> ENSG00000147889 1 1 61Results table will also include the mean expression of the gene by different lesion groups in addition to the median expression and standard deviation.
head(sorted.kw[,c(19:24)])
#> fusion_mean gain_mean loss_mean multiple_mean mutation_mean
#> ENSG00000198642 NA NA 1.8904242 NA NA
#> ENSG00000120159 NA 3.779 2.6011136 NA NA
#> ENSG00000162367 3.494315 NA NA NA NA
#> ENSG00000137073 NA NA 2.6841944 NA NA
#> ENSG00000107185 NA NA 1.7349722 NA NA
#> ENSG00000147889 3.215000 NA 0.3813266 2.425 4.571
#> none_mean
#> ENSG00000198642 3.063372
#> ENSG00000120159 3.309950
#> ENSG00000162367 1.584507
#> ENSG00000137073 3.441229
#> ENSG00000107185 2.534004
#> ENSG00000147889 1.31173815) Waterfall Plots for Side-by-side Representation of Lesion and Expression Data
# waterfall plots allow a side-by-side representation of expression and lesion data of the gene of interest.
# First prepare expression and lesion data for waterfall plots:
WT1.waterfall.prep=alex.waterfall.prep(alex.data,
alex.kw.results,
"WT1",
lesion_data)
# alex.waterfall.plot can be used to return the plot
WT1.waterfall.plot=alex.waterfall.plot(WT1.waterfall.prep,
lesion_data)
Figure 5. JAK2 Water-fall plot which offers a side-by-side graphical representation of lesion and expression data for each patient
16) Visualize Lesion and Expression Data by Pathway (JAK/STAT Pathway)
data("pathways")
# alex.pathway will return two panels figure of lesion and expression data of ordered subjects based on the computed lesions distance in all genes assigned to the pathway of interest:
alex.path=alex.pathway(alex.data,
lsn.data = lesion_data,
pathways = pathways,
selected.pathway = "Jak_Pathway")
Figure 6. Ordered Lesion and Expression Data based on the Clustering Analysis on the pathway level (JAK/STAT pathway)
# To return ordered lesion and expression data of the genes assigned to the pathway of interest (same patients order in the plot):
alex.path[1:10,1:5]
#> PASGFH PATMRE PASWXZ PATZYC PATBRV
#> JAK2 _lsn none none loss loss none
#> JAK3 _lsn mutation mutation mutation mutation none
#> JAK1 _lsn none none none none none
#> IL7R _lsn gain none none gain none
#> STAT5B _lsn mutation mutation mutation mutation mutation
#> PTPN2 _lsn none none none none none
#> JAK2 _expr 1.833 3.147 1.542 1.25 2.144
#> JAK3 _expr 3.065 3.264 4.533 3.798 4.45
#> JAK1 _expr 4.314 5.29 5.126 4.192 5.78
#> IL7R _expr 4.025 5.053 6.579 5.496 4.68417) Lesion Binary Matrix for Association Analysis with Clinical Outcomes
# This type of lesion matrices with each gene affected by a certain type of lesion in a separate row is very helpful to run multiple levels of association analysis that include association between lesions and treatment outcomes.
# Users should first Prepare gene and lesion data and determine lesions that overlap each gene (locus):
gene.lsn=prep.gene.lsn.data(lesion_data,
hg38_gene_annotation)
gene.lsn.overlap= find.gene.lsn.overlaps(gene.lsn)
# use prep.binary.lsn.mtx function to prepare the lesion binary matrix:
lsn.binary.mtx.atleast5=prep.binary.lsn.mtx(gene.lsn.overlap,
min.ngrp=5)
# Each row is a lesion type that affect a certain gene for example NOTCH1_mutation (entry will be labelled as 1 if the patient is affected by by this type of lesion and 0 otherwise).
# min.ngrp can be used to specify the minimum number of patients with a lesion to be included in the final lesion matrix.
head(lsn.binary.mtx.atleast5[,1:5])
#> PATXKW PASHNK PARMUC PATKWU PARXMV
#> ENSG00000005339_mutation 0 1 1 1 1
#> ENSG00000005700_loss 0 0 0 0 0
#> ENSG00000006283_gain 0 0 0 0 0
#> ENSG00000010438_loss 0 0 0 0 0
#> ENSG00000010810_loss 0 0 0 0 0
#> ENSG00000014123_loss 0 0 0 0 018) Run Association Analysis for Lesions with Clinical Outcomes
# Prepare Event-free Survival (EFS) and Overall Survival (OS) as survival objects:
clin_data$EFS <- Surv(clin_data$efs.time, clin_data$efs.censor)
clin_data$OS <- Surv(clin_data$os.time, clin_data$os.censor)
# List all clinical variables of interest to be included in the association analysis:
clinvars=c("MRD.binary", "EFS", "OS")
# Run association analysis between lesions and clinical variables:
assc.outcomes=grin.assoc.lsn.outcome(lsn.binary.mtx.atleast5,
clin_data,
hg38_gene_annotation,
clinvars)
# Optional: Adjust for covariates using the 'covariate' argument19) Now, let’s Take a Look on the Results Table of the Association between Lesions and Treatment Outcomes:
# order the genes by the ones with most significant KW q-value:
sorted.outcomes <- assc.outcomes[order(as.numeric(as.character(assc.outcomes$`MRD.binary.p-value`))),]First section of the results table will include gene annotation in addition to the odds ratio, lower95, upper95 confidence intervals in addition to p and FDR adjusted q-values for the logistic regression models testing for the association between lesions and binary outcome variables such as Minimal Residual Disease (MRD). COX proportional hazard models will be used in case of survival objects such as Event-free survival (EFS) and Overall Survival (OS) with hazard ratios reported instead of odds ratio:
head(sorted.outcomes[1:7,c(6,11,14,15)])
#> gene.name MRD.binary.odds.ratio MRD.binary.p-value
#> ENSG00000147889_loss CDKN2A 0.2132367 5.889274e-07
#> ENSG00000099810_loss MTAP 0.2750191 1.458384e-05
#> ENSG00000184937_mutation WT1 6.8888889 2.698869e-05
#> ENSG00000198642_loss KLHL9 0.3205882 6.114398e-04
#> ENSG00000171843_loss MLLT3 0.2253290 1.057842e-03
#> ENSG00000188352_loss FOCAD 0.3019324 1.326181e-03
#> MRD.binary.q-value
#> ENSG00000147889_loss 9.850801e-05
#> ENSG00000099810_loss 1.219696e-03
#> ENSG00000184937_mutation 1.504771e-03
#> ENSG00000198642_loss 2.556839e-02
#> ENSG00000171843_loss 3.538838e-02
#> ENSG00000188352_loss 3.697101e-02Results table will also include the number of patients with/without lesion who experienced or did not experience the event:
head(sorted.outcomes[1:7,c(6, 16:19)])
#> gene.name MRD.binary.event.with.lsn
#> ENSG00000147889_loss CDKN2A 37
#> ENSG00000099810_loss MTAP 36
#> ENSG00000184937_mutation WT1 16
#> ENSG00000198642_loss KLHL9 14
#> ENSG00000171843_loss MLLT3 6
#> ENSG00000188352_loss FOCAD 10
#> MRD.binary.event.without.lsn
#> ENSG00000147889_loss 33
#> ENSG00000099810_loss 34
#> ENSG00000184937_mutation 54
#> ENSG00000198642_loss 56
#> ENSG00000171843_loss 64
#> ENSG00000188352_loss 60
#> MRD.binary.no.event.with.lsn
#> ENSG00000147889_loss 163
#> ENSG00000099810_loss 154
#> ENSG00000184937_mutation 8
#> ENSG00000198642_loss 85
#> ENSG00000171843_loss 57
#> ENSG00000188352_loss 69
#> MRD.binary.no.event.without.lsn
#> ENSG00000147889_loss 31
#> ENSG00000099810_loss 40
#> ENSG00000184937_mutation 186
#> ENSG00000198642_loss 109
#> ENSG00000171843_loss 137
#> ENSG00000188352_loss 125library(GRIN2)