knitr::opts_chunk$set( collapse=TRUE, comment="#>", warning=FALSE, message=FALSE )
receptLoss
is an R package designed to identify
novel nuclear hormone receptors (NHRs) whose expression levels
in cancers could serve as biomarkers for patient survival.
By utilizing both expression data from both tumor and normal tissue,
receptLoss
provides biological context to the process of
tumor subclassification that is lacking in existing methods
that rely solely on expression patterns in tumor tissue.
receptLoss
is complementary to
oncomix
.
Whereas oncomix
detects genes that gain expression in
subsets of tumors relative to normal tissue,
receptLoss
detects genes that lose expression in
subsets of tumors relative to normal tissue.
## Install the development version from GitHub devtools::install_github("dpique/receptLoss", build_opts=c("--no-resave-data", "--no-manual"), build_vignettes=TRUE)
receptLoss
consists of 2 main functions:
receptLoss()
takes in 2 matrices of gene expression data, one from
tumor and one from adjacent normal tissue. The output is a matrix with
rows representing genes and columns representing summary statistics.
plotReceptLoss()
generates a histogram visualization of the
distribution of the gene desired by the user.
We begin by simulating two gene expression data matrices, one from tumor and the other from normal tissue.
library(receptLoss) library(dplyr) library(ggplot2) set.seed(100) ## Simulate matrix of expression values from ## 10 genes measured in both normal tissue and ## tumor tissue in 100 patients exprMatrNml <- matrix(abs(rnorm(100, mean=2.5)), nrow=10) exprMatrTum <- matrix(abs(rnorm(1000)), nrow=10) geneNames <- paste0(letters[seq_len(nrow(exprMatrNml))], seq_len(nrow(exprMatrNml))) rownames(exprMatrNml) <- rownames(exprMatrTum) <- geneNames
exprMatrNml
and exprMatrTum
are $m \times n$ matrices containing
gene expression data from normal and tumor tissue, respectively,
with $m$ genes as rows and $n$ patients as columns. The row names of
these matrices are the gene names.
These two matrices should have the same number of rows (ie genes), with genes listed in the same order between the two matrices. However, they don't have to have the same number of columns (ie patients).
To run receptLoss()
, we also define 2 parameters:
nSdBelow
is an integer value that places a lower boundary
(i.e. lowerBound
, shown as the pink 'B' in image below) $n$
standard deviations below
the mean of each gene's expression levels in normal tissue
(dotted pink curve below).
The larger nSdBelow
is, the smaller (i.e. further to the left)
the lowerBound
becomes.
nSdBelow
=2, as
~r round(pnorm(q=2, mean=0, sd=1), 3) * 100
% of the
normal tissue expression data should be greater than the lowerBound
(assuming the expression data from normal tissue is distributed
as a Gaussian).minPropPerGroup
- a numeric value between $(0,0.5)$ indicating the
minimum proportion of tumor samples desired within each of the
two tumor subgroups defined by the lowerBound
. Determines the value of
meetsMinPropPerGrp
(either TRUE or FALSE) in the output.
minPropPerGroup
=0.20. Values close to 0 may result in
the inclusion of genes that subdivide tumors into very
unequally-sized subgroups.
Values closer to 0.5 will identify genes that subdivide tumors
into nearly equal-sized groups and may be unnecessarily restrictive.nSdBelow <- 2 minPropPerGroup <- .2 rl <- receptLoss(exprMatrNml, exprMatrTum, nSdBelow, minPropPerGroup) head(rl)
The output of receptLoss()
is an $m\times7$ matrix,
with $m$ equaling the number of genes. The 7 columns are as follows:
geneNm
- the gene name
lowerBound
($B$) - the value nSdBelow
the mean of
the normal tissue expression data. Can be expressed as
$$B=\mu_N - \sigma_N * n_{sdBelow},$$
where $\mu_N$ is the mean of the normal tissue expression data, $\sigma_N$ is
the standard deviation of the normal tissue expression data, and $n_{sdBelow}$
is the value nSdBelow
set by the user.
propTumLessThBound
($\pi_L$) - the proportion of tumor samples with
expression levels less than lowerBound
. Can be expressed as:
$$\pi_L =\frac{1}{N_T}\sum_{j=1}^{N_T} \Bigg{
\begin{array}{ll} 1,~ if~x_{j} <
lowerBound \
0, ~ otherwise
\end{array},
$$ where $x_{j}$ is the $j^{th}$ tumor sample and
$N_T$ is the total number of tumor samples.
muAb
($\mu_A$) - "mu above", the arithmetic mean across
expression values from tumors greater than
(ie above) the lowerBound
.
muBl
($\mu_B$) - "mu below", the arithmetic mean across
expression values from tumors less than
(ie below) the lowerBound
.
deltaMu
($\Delta\mu$) - equal to $\mu_A - \mu_B$. The rows in the
output matrix are
sorted in descending order by the deltaMu
statistic, which indicates the
degree of separation between the two tumor subgroups. Higher deltaMu
values
indicate tumor subgroups that are more cleanly separated and more likely to
constitute a bimodal distribution within the tumor samples.
meetsMinPropPerGrp
- a logical indicating whether the proportion
of samples in each group is greater than that set by minPropPerGroup
.
If $min(\pi_L, 1-\pi_L) >$ minPropPerGroup
,
then meetsMinPropPerGrp
is TRUE
; otherwise, it is FALSE
.
Genes for which meetsMinPropPerGrp
equals FALSE
can be filtered out -
they do not have a sufficient
proportion of tumors in each group to permit useful tumor subgrouping.
Let's take the top-ranked gene and plot its distribution.
clrs <- c("#E78AC3", "#8DA0CB") tryCatch({plotReceptLoss(exprMatrNml, exprMatrTum, rl, geneName=as.character(rl[1,1]), clrs=clrs)}, warning=function(cond){ knitr::include_graphics("rl_fig.png") }, error=function(cond){ knitr::include_graphics("rl_fig.png") } )
Here's what this graph is showing us:
The x-axis represents RNA expression values, with lower values toward
the left and larger values (i.e. higher expression) toward the right.
The y-axis represents density. The name of the gene
("r as.character(rl[1,1])
") is shown
in the upper left of the plot.
The dotted curve represents a Gaussian distribution fit to the expression data from normal tissue, and the blue histogram represents expression data from tumor tissue.
The pink vertical line corresponds to the lowerBound
for the
expression data from normal tissue.
Since most normal tissue expresses the RNA above the lowerBound
,
any tumors that express the RNA below this value have lost RNA
expression relative to normal tissue. Thus, the lowerBound
forms
a boundary between 2 tumor subgroups that either have or have not
lost RNA expression relative to normal tissue.
The question that inspired this package was whether the loss of expression of any of the ~50 NHRs (beyond the well-known estrogen, progesterone, and androgen NHRs) in uterine tumors was associated with differences in patient survival. NHRs might not only serve as survival biomarkers but also as drug targets, as their activity can be modulated by small molecules that resemble their hormonal ligands.
To facilitate the application of this question to additional cancer
types, a list of all NHRs is included in this package as the object nhrs
.
This object facilitates filtering of NHRs from a matrix of gene expression data, as it contains several commonly-used gene identifiers (e.g. HGNC symbol, HGNC ID, Entrez ID, and Ensembl ID) for the NHRs that might be found in different RNA expression datasets.
The source code for generating nhrs
is available in "data-raw/nhrs.R".
receptLoss::nhrs
receptLoss
identifies genes that subclassify tumors
based on their RNA expression levels relative to normal tissue.
The genes are ranked by their $\Delta\mu$ statistic which reflects
a measure of the cleaness of separation (ie bimodality)
between the two tumor subgroups.
receptLoss
can be expanded for use with a variety of tumors,
genes (e.g. to identify novel candidate tumor suppressors),
biological data (e.g. miRNA, protein expression), and even
non-biological data types where you have numeric data from two groups
(one normal group and one abnormal group) and where subgroup
identification is desired within the abnormal group
receptLoss
is particularly useful when there are a large number of
tumor samples (hundreds) relative to normal samples (dozens), as is the
case in several cancer databases, including the uterine cancer database from
the Cancer Genome Atlas/Genomic Data Commons.
By assuming that the normal expression data are distributed as a
single Gaussian, receptLoss
can subclassify large numbers of
tumors even in the presence of small numbers of normal tissue samples.
Please contact me at daniel.pique@med.einstein.yu.edu with any suggestions, questions, or comments. Thank you!
vignette("receptLoss")
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