Habits
Introduction
The xseq model specifies how the expression \(Y\) of a group of genes in a patient is influenced by the somatic mutation status of a gene \(g\) in the patient. The main question we address is whether gene \(g\) co-associates with disrupted expression to itself or its connected genes as defined by an influence graph. This concept is motivated by biological hypotheses predicting that some functional mutations will exhibit a ``transcriptional shadow’’, resulting from a mechanistic impact on the gene expression profile of a tumour. For example, loss-of-function mutations (nonsense mutations, frame-shifting indels, splice-site mutations or homozygous copy number deletions) occurring in tumour suppressor genes like can cause loss of expression due to nonsense-mediated mRNA decay or gene dosage effects. In this context, we define a as a genetic or epigenetic aberration that results in up-regulation or down-regulation of the gene itself. In contrast, some mutations can disrupt the expression of other genes in the same biochemical pathway (). This class of mutations tends to cast a long transcriptional shadow over many genes across the genome. \(\beta\)-catenin () mutations, which drive constitutive activation of Wnt signalling in several cancer types, are a potent example of mutational impact on gene expression.
Inputs
The model is predicated on the idea that mutations with functional effects on transcription will exhibit measurable signals in mRNA transcripts biochemically related to the mutated gene thus imposing a transcriptional shadow across part (or all) of a pathway. To infer this property, three key inputs are required for the model: a patient-gene matrix encoding the presence/absence of a mutation (any form of somatic genomic aberrations that can be ascribed to a gene, e.g., SNVs, indels, or copy number alterations); a patient-gene expression matrix encoding continuous value expression data (e.g., from RNASeq or microarrays); and a graph structure encoding whether two genes are known to be functionally related (e.g., obtained through literature, databases, or co-expression data). uses a precomputed `influence graph’ as a means to incorporate prior gene-gene relationship knowledge into its modelling framework. For analysis of mutation impact in-, the graph reduces to the simple case where the mutated gene is only connected to itself.
library(xseq)
data(mut, expr, cna.call, cna.logr, net)
mut[1:5,1:5]## sample hgnc_symbol entrezgene variant_type chrom
## 1 TCGA-AB-2802-03 TBX15 0 MISSENSE 1
## 2 TCGA-AB-2802-03 TCHHL1 0 MISSENSE 1
## 3 TCGA-AB-2802-03 ANKRD30A 0 MISSENSE 10
## 4 TCGA-AB-2802-03 PTPN11 0 MISSENSE 12
## 5 TCGA-AB-2802-03 EP400 0 SYNONYMOUS 12expr[1:5,1:5]## NPM1 RUNX1 KDM6A FLT3 TP53
## TCGA-AB-3007-03 13.19168 13.99527 10.178914 12.50839 11.13443
## TCGA-AB-2990-03 13.64727 14.02808 10.099283 13.46506 10.98983
## TCGA-AB-2915-03 12.60635 13.45520 11.060389 12.23997 10.98188
## TCGA-AB-2927-03 12.61067 14.48913 10.743187 13.54870 10.89469
## TCGA-AB-3000-03 13.26361 11.88463 9.955398 13.40446 11.56106cna.call[1:5,1:5]## NPM1 RUNX1 KDM6A FLT3 TP53
## TCGA-AB-2803-03 0 0 0 0 0
## TCGA-AB-2804-03 0 0 0 0 0
## TCGA-AB-2805-03 0 0 0 0 0
## TCGA-AB-2806-03 0 0 0 0 0
## TCGA-AB-2807-03 0 1 0 0 0cna.logr[1:5,1:5]## NPM1 RUNX1 KDM6A FLT3 TP53
## TCGA-AB-2884-03 -0.0065 0.0056 0.0110 -0.0023 -0.0015
## TCGA-AB-2943-03 -0.0878 -0.0800 0.0026 -0.0527 -0.1136
## TCGA-AB-2938-03 0.0384 0.0004 -0.0044 0.0183 0.9600
## TCGA-AB-2806-03 0.0017 -0.0062 -0.0005 0.0011 0.0194
## TCGA-AB-2826-03 0.0230 0.0123 0.0069 0.0095 0.0091net[1:2]## $NPM1
## MDM2 AKT1 HEXIM1 GRK5 SSB NOP2 HMGA2
## 1 1 1 1 1 1 1
## PPID HIST2H2BE BAALC AFF1 TET2 THOC7 HOXA7
## 1 1 1 1 1 1 1
## KPNB1 PC SMC4 IDH1 KIT CD34 STAT5B
## 1 1 1 1 1 1 1
## MN1
## 1
##
## $RUNX1
## SUV39H1 ETS1 SMAD4 RUNX3 IGFBP3 PIM1 CEBPA TLE1
## 1 1 1 1 1 1 1 1
## CCND1 SMAD3 SMARCA4 AR TCF12 SMAD1 LMO2 CD34
## 1 1 1 1 1 1 1 1
## NT5DC3 HRAS ZNF687 CDC73 NOTCH2NL YTHDF2 PBX1 JAK2
## 1 1 1 1 1 1 1 1
## FHOD1 ZFPM1 ABL1 ZFP64 GFI1 BAALC
## 1 1 1 1 1 1Cis-analysis
We first analyze the cis-effects of loss-of-function mutations (frameshift, nonsense and splice-site mutations) on gene expression.
weight = EstimateExpression(expr)
# Impute missing values
expr = ImputeKnn(expr)
cna.logr = ImputeKnn(cna.logr)
# Quantile-Normalization
expr.quantile = QuantileNorm(expr)#=========================================================================================
## Get the conditional distritions P(Y|G)
#
# We first show TP53 mutations, expression, and copy number alterations
tmp = GetExpressionDistribution(expr=expr.quantile, mut=mut, cna.call=cna.call,
gene="TP53", show.plot=TRUE)
expr.dis.quantile = GetExpressionDistribution(expr=expr.quantile, mut=mut)#=========================================================================================
## Filtering not expressed genes, and only analyzing loss-of-function
## Mutations
##
id = weight[mut[, "hgnc_symbol"]] > 0.9 &
(mut[, "variant_type"] %in% c("FRAMESHIFT", "NONSENSE", "SPLICE"))
id = id & !is.na(id)
mut.filt = mut[id, ]
#=========================================================================================
init = SetXseqPrior(expr.dis = expr.dis.quantile,
net = net,
mut = mut.filt,
mut.type = "loss",
cis = TRUE)
# parameter constraints in EM-iterations
constraint = list(equal.fg=FALSE)
model.cis = InitXseqModel(mut = mut.filt,
expr = expr.quantile,
net = net,
expr.dis = expr.dis.quantile,
cpd = init$cpd,
cis = TRUE,
prior = init$prior)
model.cis.em = LearnXseqParameter(model = model.cis,
constraint = constraint,
iter.max = 50,
threshold = 1e-6)
xseq.pred = ConvertXseqOutput(model.cis.em$posterior)
xseq.pred[1:20,]## sample hgnc_symbol P(F) P(D)
## 67 TCGA-AB-2820-03 TP53 0.9639755 0.9905104
## 68 TCGA-AB-2857-03 TP53 0.9613231 0.9905104
## 69 TCGA-AB-2860-03 TP53 0.9818841 0.9905104
## 70 TCGA-AB-2868-03 TP53 0.9648281 0.9905104
## 71 TCGA-AB-2908-03 TP53 0.9796830 0.9905104
## 72 TCGA-AB-2938-03 TP53 0.7893094 0.9905104
## 113 TCGA-AB-2871-03 STAG2 0.9749255 0.9846371
## 114 TCGA-AB-2913-03 STAG2 0.7286295 0.9846371
## 115 TCGA-AB-2964-03 STAG2 0.9779552 0.9846371
## 116 TCGA-AB-2972-03 STAG2 0.9769733 0.9846371
## 117 TCGA-AB-2978-03 STAG2 0.9789665 0.9846371
## 62 TCGA-AB-2818-03 RAD21 0.8466841 0.9823520
## 63 TCGA-AB-2886-03 RAD21 0.9700700 0.9823520
## 64 TCGA-AB-2967-03 RAD21 0.9708465 0.9823520
## 65 TCGA-AB-2975-03 RAD21 0.9654264 0.9823520
## 66 TCGA-AB-2986-03 RAD21 0.9684474 0.9823520
## 118 TCGA-AB-2900-03 SMC1A 0.8430609 0.6738602
## 9 TCGA-AB-2807-03 POLR2E 0.7352240 0.5944941
## 108 TCGA-AB-2851-03 SMC3 0.7756835 0.5110214
## 109 TCGA-AB-2950-03 SMC3 0.4095276 0.5110214Trans-analysis
#=========================================================================================
## Remove the cis-effects of copy number alterations on gene expression
#
# We first show an example: PTEN copy number alterations and expression in AML
tmp = NormExpr(cna.logr=cna.logr, expr=expr, gene="TP53", show.plot=TRUE)
expr.norm = NormExpr(cna.logr=cna.logr, expr=expr)
expr.norm.quantile = QuantileNorm(expr.norm)
#=========================================================================================
## Get the conditional distritions P(Y|G),
#
expr.dis.norm.quantile = GetExpressionDistribution(expr=expr.norm.quantile,
mut=mut)
#=========================================================================================
##
## Filtering not expressed genes
##
id = weight[mut[, "hgnc_symbol"]] > 0.9
id = id & !is.na(id)
mut.filt = mut[id, ]
#=========================================================================================
# Filter the network by only keeping the top-50 genes,
# with connection strongth no less than 0.4
net = sapply(net, function(z) {
z = z[z >= 0.4]
if (length(z) > 50) {
z = z[1:50]
}
return (z)
})
init = SetXseqPrior(expr.dis = expr.dis.norm.quantile,
net = net,
mut = mut.filt,
mut.type = "both",
cis = FALSE)
# parameter constraints in EM-iterations
constraint = list(equal.fg=TRUE, baseline=init$baseline)
model.trans = InitXseqModel(mut = mut.filt,
expr = expr.norm.quantile,
net = net,
expr.dis = expr.dis.norm.quantile,
cpd = init$cpd,
cis = FALSE,
prior = init$prior)
## EM algorithm for parameter estimations
model.trans.em = LearnXseqParameter(model = model.trans,
constraint = constraint,
iter.max = 50,
threshold = 1e-6)
#=========================================================================================
# Reformat output
xseq.pred = ConvertXseqOutput(model.trans.em$posterior)
xseq.pred[1:20, ]## sample hgnc_symbol P(F) P(D)
## 49 TCGA-AB-2810-03 NPM1 0.6150921 1
## 50 TCGA-AB-2811-03 NPM1 0.7559845 1
## 51 TCGA-AB-2812-03 NPM1 0.9998402 1
## 52 TCGA-AB-2816-03 NPM1 0.7741887 1
## 53 TCGA-AB-2818-03 NPM1 0.9989033 1
## 54 TCGA-AB-2824-03 NPM1 0.9493704 1
## 55 TCGA-AB-2825-03 NPM1 0.9901913 1
## 56 TCGA-AB-2826-03 NPM1 0.8165706 1
## 57 TCGA-AB-2835-03 NPM1 0.8168570 1
## 58 TCGA-AB-2836-03 NPM1 0.7120723 1
## 59 TCGA-AB-2837-03 NPM1 0.8694290 1
## 60 TCGA-AB-2839-03 NPM1 0.9338839 1
## 61 TCGA-AB-2848-03 NPM1 0.9450092 1
## 62 TCGA-AB-2853-03 NPM1 0.9999715 1
## 63 TCGA-AB-2859-03 NPM1 0.7302909 1
## 64 TCGA-AB-2861-03 NPM1 0.9952025 1
## 65 TCGA-AB-2869-03 NPM1 0.8525880 1
## 66 TCGA-AB-2871-03 NPM1 0.5290987 1
## 67 TCGA-AB-2877-03 NPM1 0.9738998 1
## 68 TCGA-AB-2879-03 NPM1 0.9609612 1# We finally show the dysregulation probabilites of genes connected to TP53
tmp = PlotRegulationHeatmap(gene="TP53", posterior=model.trans.em$posterior, main="in_AML",
mut=mut, subtype=list(NULL), key=FALSE, dendrogram="row")