---
title: "Analyzing GeoMx-NGS RNA Expression Data with GeomxTools"
shorttitle: "GeomxTools NGS-RNA Workflow"
author:
- name: Jason Reeves
affiliation: NanoString Technologies, Inc
- name: Prajan Divakar
affiliation: NanoString Technologies, Inc
- name: Nicole Ortogero
affiliation: NanoString Technologies, Inc
- name: Maddy Griswold
affiliation: NanoString Technologies, Inc
- name: Zhi Yang
affiliation: NanoString Technologies, Inc
- name: Stephanie Zimmerman
affiliation: NanoString Technologies, Inc
- name: Rona Vitancol
affiliation: NanoString Technologies, Inc
- name: David Henderson
affiliation: NanoString Technologies, Inc
package: GeomxTools
abstract: >
Here we walk through an end-to-end GeoMx-NGS gene expression analysis
workflow. We start with raw gene expression count files. Using a
combination of NanoString-developed (GeoMxTools & NanoStringNCTools) and
open source R packages, we evaluate samples and expression targets and
prepare gene-level count data for downstream analysis. To understand our
spatial data, we perform unsupervised clustering, dimension reduction, and
differential gene expression analyses and visually explore the results.
date: "21 October, 2021"
output:
BiocStyle::html_document:
toc: true
toc_float: true
vignette: >
%\VignetteIndexEntry{Analyzing GeoMx-NGS Data with GeomxTools}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: inline
---
```{r style, echo = FALSE, results = "asis"}
BiocStyle::markdown()
```
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 5,
fig.height = 4.5,
dpi = 200
)
```
<p>
**R version**: `r R.version.string`
<br>
**Bioconductor version**: `r BiocManager::version()`
<br>
**Package**: `r packageVersion("GeomxTools")`
<\p>
# Introduction
In this vignette, we will introduce a data analysis workflow for GeoMx-NGS mRNA
expression data.
The GeoMx Digital Spatial Profiler (DSP) is a platform for capturing spatially
resolved high-plex gene (or protein) expression data from tissue
[Merritt et al., 2020](https://pubmed.ncbi.nlm.nih.gov/32393914/). In
particular, formalin-fixed paraffin-embedded (FFPE) or fresh-frozen (FF) tissue
sections are stained with barcoded in-situ hybridization probes that bind to
endogenous mRNA transcripts. The user then selects regions of the interest
(ROI) to profile; if desired, each ROI segment can be further sub-divided into
areas of illumination (AOI) based on tissue morphology. The GeoMx then
photo-cleaves and collects expression barcodes for each AOI segment separately
for downstream sequencing and data processing.
The final results are spatially resolved unique expression datasets for every
protein-coding gene (>18,000 genes) from every individual segment profiled from
tissue.
## Motivation & Scope
The motivation for this vignette is to enable scientists to work with GeoMx-NGS
gene expression data and understand a standard data analysis workflow.
Our specific objectives:
* Load GeoMx raw count files and metadata (DCC, PKC, and annotation file)
* Perform quality control (QC), filtering, and normalization to prepare the data
* Perform downstream visualizations and statistical analyses including:
+ Dimension reduction with UMAP or t-SNE
+ Heatmaps and other visualizations of gene expression
+ Differential expression analyses with linear mixed effect models
# Getting started
Let's install and load the GeoMx packages we need:
```{r InstallGeomxTools, echo = TRUE, eval = FALSE}
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes most up to date version of Bioc
BiocManager::install()
BiocManager::install("NanoStringNCTools")
BiocManager::install("GeomxTools")
BiocManager::install("GeoMxWorkflows")
```
```{r libs, message = FALSE, warning = FALSE, eval = TRUE}
library(NanoStringNCTools)
library(GeomxTools)
library(GeoMxWorkflows)
```
```{r checking package versions}
if(packageVersion("GeomxTools") < "2.1" &
packageVersion("GeoMxWorkflows") >= "1.0.1"){
stop("GeomxTools and Workflow versions do not match. Please use the same version.
This workflow is meant to be used with most current version of packages.
If you are using an older version of Bioconductor please reinstall GeoMxWorkflows and use vignette(GeoMxWorkflows) instead")
}
if(packageVersion("GeomxTools") > "2.1" &
packageVersion("GeoMxWorkflows") <= "1.0.1"){
stop("GeomxTools and Workflow versions do not match.
Please use the same version, see install instructions above.")
# to remove current package version
# remove.packages("GeomxTools")
# remove.packages("GeoMxWorkflows")
# see install instructions above
}
```
## Loading Data
In this vignette, we will analyze a GeoMx kidney dataset created with the human
whole transcriptome atlas (WTA) assay. The dataset includes 4 diabetic kidney
disease (DKD) and 3 healthy kidney tissue samples. Regions of interest (ROI)
were spatially profiled to focus on two different kidney structures: tubules
or glomeruli. One glomerular ROI contains the entirety of a single glomerulus.
Each tubular ROI contains multiple tubules that were segmented into distal
(PanCK+) and proximal (PanCK-) tubule areas of illumination (AOI).
Download and the unzip the kidney data set found
[on the NanoString Website](http://nanostring-public-share.s3-website-us-west-2.amazonaws.com/GeoScriptHub/Kidney_Dataset_for_GeomxTools.zip)
The key data files are:
* DCCs files - expression count data and sequencing quality metadata
* PKCs file(s) - probe assay metadata describing the gene targets present in
the data, PKC files can be found [here](https://nanostring.com/products/geomx-digital-spatial-profiler/geomx-dsp-configuration-files/)
* Annotation file - useful tissue information, including the type of segment
profiled (ex: glomerulus vs. tubule), segment area/nuclei count, and other
tissue characteristics (ex: diseased vs. healthy). If working with a new
dataset, use the lab worksheet from the GeoMx instrument study readout package,
as the annotation order of NTCs is important to ensure proper processing of
files.
We first locate the downloaded files:
```{r quickstart, message = FALSE, warning = FALSE}
# Reference the main folder 'file.path' containing the sub-folders with each
# data file type:
datadir <- system.file("extdata", "WTA_NGS_Example",
package="GeoMxWorkflows")
# to locate a specific file path replace the above line with
# datadir <- file.path("~/Folder/SubFolder/DataLocation")
# replace the Folder, SubFolder, DataLocation as needed
# the DataLocation folder should contain a dccs, pkcs, and annotation folder
# with each set of files present as needed
```
```{r locateFiles, message = FALSE, warning = FALSE}
# automatically list files in each directory for use
DCCFiles <- dir(file.path(datadir, "dccs"), pattern = ".dcc$",
full.names = TRUE, recursive = TRUE)
PKCFiles <- unzip(zipfile = dir(file.path(datadir, "pkcs"), pattern = ".zip$",
full.names = TRUE, recursive = TRUE))
SampleAnnotationFile <-
dir(file.path(datadir, "annotation"), pattern = ".xlsx$",
full.names = TRUE, recursive = TRUE)
```
We then load the data to create a data object using the
`readNanoStringGeoMxSet` function.
``` {r loadData, message = FALSE, warning = FALSE}
# load data
demoData <-
readNanoStringGeoMxSet(dccFiles = DCCFiles,
pkcFiles = PKCFiles,
phenoDataFile = SampleAnnotationFile,
phenoDataSheet = "Template",
phenoDataDccColName = "Sample_ID",
protocolDataColNames = c("aoi", "roi"),
experimentDataColNames = c("panel"))
```
All of the expression, annotation, and probe information are now linked and
stored together into a single data object.
For more details on this object's structure and accessors, please refer to the
"GeoMxSet Object Overview" section at the end of this vignette.
# Study Design
## Modules Used
First let's access the PKC files, to ensure that the expected PKCs have been
loaded for this study. For the demo data we are using the file
`Hsa_WTA_1.0.pkc`.
``` {r modules}
library(knitr)
pkcs <- annotation(demoData)
modules <- gsub(".pkc", "", pkcs)
kable(data.frame(PKCs = pkcs, modules = modules))
```
## Sample Overview
Now that we have loaded the data, we can visually summarize the experimental
design for our dataset to look at the different types of samples and ROI/AOI
segments that have been profiled. We present this information in a Sankey
diagram.
``` {r sampleFlow, message = FALSE, warning = FALSE}
library(dplyr)
library(ggforce)
library(networkD3)
sankeyCols <- c("source", "target", "value")
link1 <- count(pData(demoData), `slide name`, class)
link2 <- count(pData(demoData), class, region)
link3 <- count(pData(demoData), region, segment)
colnames(link1) <- sankeyCols
colnames(link2) <- sankeyCols
colnames(link3) <- sankeyCols
links <- rbind(link1,link2,link3)
nodes <- unique(data.frame(name=c(links$source, links$target)))
# sankeyNetwork is 0 based, not 1 based
links$source <- as.integer(match(links$source,nodes$name)-1)
links$target <- as.integer(match(links$target,nodes$name)-1)
sankeyNetwork(Links = links, Nodes = nodes, Source = "source",
Target = "target", Value = "value", NodeID = "name",
units = "TWh", fontSize = 12, nodeWidth = 30)
```
# QC & Pre-processing
The steps above encompass the standard pre-processing workflow for GeoMx data.
In short, they represent the selection of ROI/AOI segments and genes based on
quality control (QC) or limit of quantification (LOQ) metrics and data
normalization.
Before we begin, we will shift any expression counts with a value of 0 to 1 to
enable in downstream transformations.
``` {r shiftCounts, eval = TRUE}
# Shift counts to one
demoData <- shiftCountsOne(demoData, useDALogic = TRUE)
```
## Segment QC
We first assess sequencing quality and adequate tissue sampling for every
ROI/AOI segment.
Every ROI/AOI segment will be tested for:
* Raw sequencing reads: segments with >1000 raw reads are removed.
* % Aligned,% Trimmed, or % Stitched sequencing reads: segments below ~80% for
one or more of these QC parameters are removed.
* % Sequencing saturation ([1-deduplicated reads/aligned reads]%): segments
below ~50% require additional sequencing to capture full sample diversity and
are not typically analyzed until improved.
* Negative Count: this is the geometric mean of the several unique negative
probes in the GeoMx panel that do not target mRNA and establish the background
count level per segment; segments with low negative counts (1-10) are not
necessarily removed but may be studied closer for low endogenous gene signal
and/or insufficient tissue sampling.
* No Template Control (NTC) count: values >1,000 could indicate contamination
for the segments associated with this NTC; however, in cases where the NTC
count is between 1,000- 10,000, the segments may be used if the NTC data is
uniformly low (e.g. 0-2 counts for all probes).
* Nuclei: >100 nuclei per segment is generally recommended; however, this
cutoff is highly study/tissue dependent and may need to be reduced; what is
most important is consistency in the nuclei distribution for segments within
the study.
* Area: generally correlates with nuclei; a strict cutoff is not generally
applied based on area.
### Select Segment QC
First, we select the QC parameter cutoffs, against which our ROI/AOI segments
will be tested and flagged appropriately. We have selected the appropriate
study-specific parameters for this study. Note: the default QC values
recommended above are advised when surveying a new dataset for the first time.
```{r setqcflagupdated, eval = TRUE}
# Default QC cutoffs are commented in () adjacent to the respective parameters
# study-specific values were selected after visualizing the QC results in more
# detail below
QC_params <-
list(minSegmentReads = 1000, # Minimum number of reads (1000)
percentTrimmed = 80, # Minimum % of reads trimmed (80%)
percentStitched = 80, # Minimum % of reads stitched (80%)
percentAligned = 75, # Minimum % of reads aligned (80%)
percentSaturation = 50, # Minimum sequencing saturation (50%)
minNegativeCount = 1, # Minimum negative control counts (10)
maxNTCCount = 9000, # Maximum counts observed in NTC well (1000)
minNuclei = 20, # Minimum # of nuclei estimated (100)
minArea = 1000) # Minimum segment area (5000)
demoData <-
setSegmentQCFlags(demoData,
qcCutoffs = QC_params)
# Collate QC Results
QCResults <- protocolData(demoData)[["QCFlags"]]
flag_columns <- colnames(QCResults)
QC_Summary <- data.frame(Pass = colSums(!QCResults[, flag_columns]),
Warning = colSums(QCResults[, flag_columns]))
QCResults$QCStatus <- apply(QCResults, 1L, function(x) {
ifelse(sum(x) == 0L, "PASS", "WARNING")
})
QC_Summary["TOTAL FLAGS", ] <-
c(sum(QCResults[, "QCStatus"] == "PASS"),
sum(QCResults[, "QCStatus"] == "WARNING"))
```
### Visualize Segment QC
Before excluding any low-performing ROI/AOI segments, we visualize the
distributions of the data for the different QC parameters. Note that the
"Select Segment QC" and "Visualize Segment QC" sections are performed in
parallel to fully understand low-performing segments for a given study.
Iteration may follow to select the study-specific QC cutoffs.
For QC visualization, we write a quick function to draw histograms of our data.
``` {r qcflagHistogramsCode, eval = TRUE, warning = FALSE, message = FALSE}
library(ggplot2)
col_by <- "segment"
# Graphical summaries of QC statistics plot function
QC_histogram <- function(assay_data = NULL,
annotation = NULL,
fill_by = NULL,
thr = NULL,
scale_trans = NULL) {
plt <- ggplot(assay_data,
aes_string(x = paste0("unlist(`", annotation, "`)"),
fill = fill_by)) +
geom_histogram(bins = 50) +
geom_vline(xintercept = thr, lty = "dashed", color = "black") +
theme_bw() + guides(fill = "none") +
facet_wrap(as.formula(paste("~", fill_by)), nrow = 4) +
labs(x = annotation, y = "Segments, #", title = annotation)
if(!is.null(scale_trans)) {
plt <- plt +
scale_x_continuous(trans = scale_trans)
}
plt
}
```
Now we explore each of the QC metrics for the segments.
``` {r plotQCHist, warning = FALSE, message = FALSE}
QC_histogram(sData(demoData), "Trimmed (%)", col_by, 80)
QC_histogram(sData(demoData), "Stitched (%)", col_by, 80)
QC_histogram(sData(demoData), "Aligned (%)", col_by, 75)
QC_histogram(sData(demoData), "Saturated (%)", col_by, 50) +
labs(title = "Sequencing Saturation (%)",
x = "Sequencing Saturation (%)")
QC_histogram(sData(demoData), "area", col_by, 1000, scale_trans = "log10")
QC_histogram(sData(demoData), "nuclei", col_by, 20)
# calculate the negative geometric means for each module
negativeGeoMeans <-
esBy(negativeControlSubset(demoData),
GROUP = "Module",
FUN = function(x) {
assayDataApply(x, MARGIN = 2, FUN = ngeoMean, elt = "exprs")
})
protocolData(demoData)[["NegGeoMean"]] <- negativeGeoMeans
# explicitly copy the Negative geoMeans from sData to pData
negCols <- paste0("NegGeoMean_", modules)
pData(demoData)[, negCols] <- sData(demoData)[["NegGeoMean"]]
for(ann in negCols) {
plt <- QC_histogram(pData(demoData), ann, col_by, 2, scale_trans = "log10")
print(plt)
}
# detatch neg_geomean columns ahead of aggregateCounts call
pData(demoData) <- pData(demoData)[, !colnames(pData(demoData)) %in% negCols]
# show all NTC values, Freq = # of Segments with a given NTC count:
kable(table(NTC_Count = sData(demoData)$NTC),
col.names = c("NTC Count", "# of Segments"))
```
Finally we plot all of the QC Summary information in a table.
```{r QCSummaryTable, results = "asis"}
kable(QC_Summary, caption = "QC Summary Table for each Segment")
```
### Remove flagged segments
As the final step in Segment QC, we remove flagged segments that do not meet
our QC cutoffs.
```{r removeQCSampleProbe, eval = TRUE}
demoData <- demoData[, QCResults$QCStatus == "PASS"]
# Subsetting our dataset has removed samples which did not pass QC
dim(demoData)
```
## Probe QC
Before we summarize our data into gene-level count data, we will remove
low-performing probes. In short, this QC is an outlier removal process, whereby
probes are either removed entirely from the study (global) or from specific
segments (local). The QC applies to gene targets for which there are multiple
distinct probes representing the count for a gene per segment. In WTA data, one
specific probe exists per target gene; thus, Probe QC does not apply to the
endogenous genes in the panel. Rather, it is performed on the negative control
probes; there are multiple probes representing our negative controls, which do
not target any sequence in the genome. These probes enable calculation of the
background per segment and will be important for determining gene detection
downstream.
After Probe QC, there will always remain at least one probe representing every
gene target. In other words, Probe QC never removes genes from your data.
### Set Probe QC Flags
A probe is removed globally from the dataset if either of the following is true:
* the geometric mean of that probe's counts from all segments divided by the
geometric mean of all probe counts representing the target from all segments is
less than 0.1
* the probe is an outlier according to the Grubb's test in at least 20% of the
segments
A probe is removed locally (from a given segment) if the probe is an outlier
according to the Grubb's test in that segment.
We do not typically adjust these QC parameters.
```{r setbioprobeqcflag, eval = TRUE}
# Generally keep the qcCutoffs parameters unchanged. Set removeLocalOutliers to
# FALSE if you do not want to remove local outliers
demoData <- setBioProbeQCFlags(demoData,
qcCutoffs = list(minProbeRatio = 0.1,
percentFailGrubbs = 20),
removeLocalOutliers = TRUE)
ProbeQCResults <- fData(demoData)[["QCFlags"]]
# Define QC table for Probe QC
qc_df <- data.frame(Passed = sum(rowSums(ProbeQCResults[, -1]) == 0),
Global = sum(ProbeQCResults$GlobalGrubbsOutlier),
Local = sum(rowSums(ProbeQCResults[, -2:-1]) > 0
& !ProbeQCResults$GlobalGrubbsOutlier))
```
We report the number of global and local outlier probes.
```{r bioprobeQCTable, echo = FALSE, results = "asis"}
kable(qc_df, caption = "Probes flagged or passed as outliers")
```
### Exclude Outlier Probes
```{r excludeOutlierProbes}
#Subset object to exclude all that did not pass Ratio & Global testing
ProbeQCPassed <-
subset(demoData,
fData(demoData)[["QCFlags"]][,c("LowProbeRatio")] == FALSE &
fData(demoData)[["QCFlags"]][,c("GlobalGrubbsOutlier")] == FALSE)
dim(ProbeQCPassed)
demoData <- ProbeQCPassed
```
## Create Gene-level Count Data
With our Probe QC steps complete, we will generate a gene-level count matrix.
The count for any gene with multiple probes per segment is calculated as the
geometric mean of those probes.
```{r aggregateCounts, eval = TRUE}
# Check how many unique targets the object has
length(unique(featureData(demoData)[["TargetName"]]))
# collapse to targets
target_demoData <- aggregateCounts(demoData)
dim(target_demoData)
exprs(target_demoData)[1:5, 1:2]
```
## Limit of Quantification
In addition to Segment and Probe QC, we also determine the limit of
quantification (LOQ) per segment. The LOQ is calculated based on the
distribution of negative control probes and is intended to approximate the
quantifiable limit of gene expression per segment. Please note that this
process is more stable in larger segments. Likewise, the LOQ may not be as
accurately reflective of true signal detection rates in segments with low
negative probe counts (ex: <2). The formula for calculating the LOQ in the
$i^{th}$ segment is:
$$LOQ_{i} = geomean(NegProbe_{i}) * geoSD(NegProbe_{i})^{n}$$
We typically use 2 geometric standard deviations ($n = 2$) above the geometric
mean as the LOQ, which is reasonable for most studies. We also recommend that a
minimum LOQ of 2 be used if the LOQ calculated in a segment is below this
threshold.
``` {r calculateLOQ, eval = TRUE}
# Define LOQ SD threshold and minimum value
cutoff <- 2
minLOQ <- 2
# Calculate LOQ per module tested
LOQ <- data.frame(row.names = colnames(target_demoData))
for(module in modules) {
vars <- paste0(c("NegGeoMean_", "NegGeoSD_"),
module)
if(all(vars[1:2] %in% colnames(pData(target_demoData)))) {
LOQ[, module] <-
pmax(minLOQ,
pData(target_demoData)[, vars[1]] *
pData(target_demoData)[, vars[2]] ^ cutoff)
}
}
pData(target_demoData)$LOQ <- LOQ
```
## Filtering
After determining the limit of quantification (LOQ) per segment, we recommend
filtering out either segments and/or genes with abnormally low signal.
Filtering is an important step to focus on the true biological data of interest.
We determine the number of genes detected in each segment across the dataset.
```{r LOQMat, eval = TRUE}
LOQ_Mat <- c()
for(module in modules) {
ind <- fData(target_demoData)$Module == module
Mat_i <- t(esApply(target_demoData[ind, ], MARGIN = 1,
FUN = function(x) {
x > LOQ[, module]
}))
LOQ_Mat <- rbind(LOQ_Mat, Mat_i)
}
# ensure ordering since this is stored outside of the geomxSet
LOQ_Mat <- LOQ_Mat[fData(target_demoData)$TargetName, ]
```
### Segment Gene Detection
We first filter out segments with exceptionally low signal. These segments will
have a small fraction of panel genes detected above the LOQ relative to the
other segments in the study. Let's visualize the distribution of segments with
respect to their % genes detected:
```{r segDetectionBarplot}
# Save detection rate information to pheno data
pData(target_demoData)$GenesDetected <-
colSums(LOQ_Mat, na.rm = TRUE)
pData(target_demoData)$GeneDetectionRate <-
pData(target_demoData)$GenesDetected / nrow(target_demoData)
# Determine detection thresholds: 1%, 5%, 10%, 15%, >15%
pData(target_demoData)$DetectionThreshold <-
cut(pData(target_demoData)$GeneDetectionRate,
breaks = c(0, 0.01, 0.05, 0.1, 0.15, 1),
labels = c("<1%", "1-5%", "5-10%", "10-15%", ">15%"))
# stacked bar plot of different cut points (1%, 5%, 10%, 15%)
ggplot(pData(target_demoData),
aes(x = DetectionThreshold)) +
geom_bar(aes(fill = region)) +
geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
theme_bw() +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
labs(x = "Gene Detection Rate",
y = "Segments, #",
fill = "Segment Type")
```
We can also create a table to review what kidney tissue type (DKD vs normal) is
going to be impacted by each threshold:
```{r segTable}
# cut percent genes detected at 1, 5, 10, 15
kable(table(pData(target_demoData)$DetectionThreshold,
pData(target_demoData)$class))
```
In this example, we choose to remove segments with less than 10% of the genes
detected. Generally, 5-10% detection is a reasonable segment filtering
threshold. However, based on the experimental design (e.g. segment types, size,
nuclei) and tissue characteristics (e.g. type, age), these guidelines may
require adjustment.
```{r filterSegments}
target_demoData <-
target_demoData[, pData(target_demoData)$GeneDetectionRate >= .1]
dim(target_demoData)
```
### Gene Detection Rate
Next, we determine the detection rate for genes across the study. To illustrate
this idea, we create a small gene list (`goi`) to review.
``` {r goi detection}
library(scales) # for percent
# Calculate detection rate:
LOQ_Mat <- LOQ_Mat[, colnames(target_demoData)]
fData(target_demoData)$DetectedSegments <- rowSums(LOQ_Mat, na.rm = TRUE)
fData(target_demoData)$DetectionRate <-
fData(target_demoData)$DetectedSegments / nrow(pData(target_demoData))
# Gene of interest detection table
goi <- c("PDCD1", "CD274", "IFNG", "CD8A", "CD68", "EPCAM",
"KRT18", "NPHS1", "NPHS2", "CALB1", "CLDN8")
goi_df <- data.frame(
Gene = goi,
Number = fData(target_demoData)[goi, "DetectedSegments"],
DetectionRate = percent(fData(target_demoData)[goi, "DetectionRate"]))
```
```{r tableGOI, echo = FALSE, results = "asis"}
kable(goi_df, caption = "Detection rate for Genes of Interest", align = "c",
col.names = c("Gene", "Detection, # Segments", "Detection Rate, % of Segments"))
```
We can see that individual genes are detected to varying degrees in the
segments, which leads us to the next QC we will perform across the dataset.
### Gene Filtering
We will graph the total number of genes detected in different percentages of
segments. Based on the visualization below, we can better understand global
gene detection in our study and select how many low detected genes to filter
out of the dataset. Gene filtering increases performance of downstream
statistical tests and improves interpretation of true biological signal.
```{r plotDetectionRate, eval = TRUE}
# Plot detection rate:
plot_detect <- data.frame(Freq = c(1, 5, 10, 20, 30, 50))
plot_detect$Number <-
unlist(lapply(c(0.01, 0.05, 0.1, 0.2, 0.3, 0.5),
function(x) {sum(fData(target_demoData)$DetectionRate >= x)}))
plot_detect$Rate <- plot_detect$Number / nrow(fData(target_demoData))
rownames(plot_detect) <- plot_detect$Freq
ggplot(plot_detect, aes(x = as.factor(Freq), y = Rate, fill = Rate)) +
geom_bar(stat = "identity") +
geom_text(aes(label = formatC(Number, format = "d", big.mark = ",")),
vjust = 1.6, color = "black", size = 4) +
scale_fill_gradient2(low = "orange2", mid = "lightblue",
high = "dodgerblue3", midpoint = 0.65,
limits = c(0,1),
labels = scales::percent) +
theme_bw() +
scale_y_continuous(labels = scales::percent, limits = c(0,1),
expand = expansion(mult = c(0, 0))) +
labs(x = "% of Segments",
y = "Genes Detected, % of Panel > LOQ")
```
We typically set a % Segment cutoff ranging from 5-20% based on the biological
diversity of our dataset. For this study, we will select 10% as our cutoff. In
other words, we will focus on the genes detected in at least 10% of our
segments; we filter out the remainder of the targets.
Note: if we know that a key gene is represented in only a small number of
segments (<10%) due to biological diversity, we may select a different cutoff
or keep the target gene by manually selecting it for inclusion in the data
object.
``` {r subsetGenes, eval = TRUE}
# Subset to target genes detected in at least 10% of the samples.
# Also manually include the negative control probe, for downstream use
negativeProbefData <- subset(fData(target_demoData), CodeClass == "Negative")
neg_probes <- unique(negativeProbefData$TargetName)
target_demoData <-
target_demoData[fData(target_demoData)$DetectionRate >= 0.1 |
fData(target_demoData)$TargetName %in% neg_probes, ]
dim(target_demoData)
# retain only detected genes of interest
goi <- goi[goi %in% rownames(target_demoData)]
```
# Normalization
We will now normalize the GeoMx data for downstream visualizations and
differential expression. The two common methods for normalization of DSP-NGS
RNA data are i) quartile 3 (Q3) or ii) background normalization.
Both of these normalization methods estimate a normalization factor per segment
to bring the segment data distributions together. More advanced methods for
normalization and modeling are under active development. However, for most
studies, these methods are sufficient for understanding differences between
biological classes of segments and samples.
Q3 normalization is typically the preferred normalization strategy for most
DSP-NGS RNA studies. Given the low negative probe counts in this particular
dataset as shown during Segment QC, we would further avoid background
normalization as it may be less stable.
Before normalization, we will explore the relationship between the upper
quartile (Q3) of the counts in each segment with the geometric mean of the
negative control probes in the data. Ideally, there should be a separation
between these two values to ensure we have stable measure of Q3 signal. If you
do not see sufficient separation between these values, you may consider more
aggressive filtering of low signal segments/genes.
``` {r, previewNF, fig.width = 8, fig.height = 8, fig.wide = TRUE, eval = TRUE, warning = FALSE, message = FALSE}
library(reshape2) # for melt
library(cowplot) # for plot_grid
# Graph Q3 value vs negGeoMean of Negatives
ann_of_interest <- "region"
Stat_data <-
data.frame(row.names = colnames(exprs(target_demoData)),
Segment = colnames(exprs(target_demoData)),
Annotation = pData(target_demoData)[, ann_of_interest],
Q3 = unlist(apply(exprs(target_demoData), 2,
quantile, 0.75, na.rm = TRUE)),
NegProbe = exprs(target_demoData)[neg_probes, ])
Stat_data_m <- melt(Stat_data, measure.vars = c("Q3", "NegProbe"),
variable.name = "Statistic", value.name = "Value")
plt1 <- ggplot(Stat_data_m,
aes(x = Value, fill = Statistic)) +
geom_histogram(bins = 40) + theme_bw() +
scale_x_continuous(trans = "log2") +
facet_wrap(~Annotation, nrow = 1) +
scale_fill_brewer(palette = 3, type = "qual") +
labs(x = "Counts", y = "Segments, #")
plt2 <- ggplot(Stat_data,
aes(x = NegProbe, y = Q3, color = Annotation)) +
geom_abline(intercept = 0, slope = 1, lty = "dashed", color = "darkgray") +
geom_point() + guides(color = "none") + theme_bw() +
scale_x_continuous(trans = "log2") +
scale_y_continuous(trans = "log2") +
theme(aspect.ratio = 1) +
labs(x = "Negative Probe GeoMean, Counts", y = "Q3 Value, Counts")
plt3 <- ggplot(Stat_data,
aes(x = NegProbe, y = Q3 / NegProbe, color = Annotation)) +
geom_hline(yintercept = 1, lty = "dashed", color = "darkgray") +
geom_point() + theme_bw() +
scale_x_continuous(trans = "log2") +
scale_y_continuous(trans = "log2") +
theme(aspect.ratio = 1) +
labs(x = "Negative Probe GeoMean, Counts", y = "Q3/NegProbe Value, Counts")
btm_row <- plot_grid(plt2, plt3, nrow = 1, labels = c("B", ""),
rel_widths = c(0.43,0.57))
plot_grid(plt1, btm_row, ncol = 1, labels = c("A", ""))
```
As expected, we see separation of the Q3 and negative probe counts at both the
distribution (A) and per segment (B) levels.
For additional conceptual guidance, please refer to our
[Data Analysis White Paper for DSP-NGS Assays](https://www.nanostring.com/resources/geomx-cta-data-whitepaper/).
Next, we normalize our data. We will use Q3 normalized data moving forward. We
use the `normalize` function from `NanoStringNCTools` to create normalization
factors reflecting each data type. Upper quartile (Q3) normalization is
performed using `norm_method = "quant"` setting the `desiredQuantile` flag to
0.75. Other quantiles could be specified by changing that value. We save the
normalized data to a specific slot using `toELT = "q_norm"`. Similarly
background normalization is performed by setting `norm_method = "neg"` and
`toElt = "neg_norm"`.
```{r normalizeObject, eval = TRUE}
# Q3 norm (75th percentile) for WTA/CTA with or without custom spike-ins
target_demoData <- normalize(target_demoData ,
norm_method = "quant",
desiredQuantile = .75,
toElt = "q_norm")
# Background normalization for WTA/CTA without custom spike-in
target_demoData <- normalize(target_demoData ,
norm_method = "neg",
fromElt = "exprs",
toElt = "neg_norm")
```
To demonstrate the effects of normalization, we graph representative box plots
of the data for individual segments before and after normalization.
```{r normplot, fig.small = TRUE}
# visualize the first 10 segments with each normalization method
boxplot(exprs(target_demoData)[,1:10],
col = "#9EDAE5", main = "Raw Counts",
log = "y", names = 1:10, xlab = "Segment",
ylab = "Counts, Raw")
boxplot(assayDataElement(target_demoData[,1:10], elt = "q_norm"),
col = "#2CA02C", main = "Q3 Norm Counts",
log = "y", names = 1:10, xlab = "Segment",
ylab = "Counts, Q3 Normalized")
boxplot(assayDataElement(target_demoData[,1:10], elt = "neg_norm"),
col = "#FF7F0E", main = "Neg Norm Counts",
log = "y", names = 1:10, xlab = "Segment",
ylab = "Counts, Neg. Normalized")
```
# Unsupervised Analysis
## UMAP & t-SNE
One common approach to understanding high-plex data is dimension reduction. Two
common methods are UMAP and tSNE, which are non-orthogonally constrained
projections that cluster samples based on overall gene expression. In this
study, we see by either UMAP (from the `umap` package) or tSNE (from the
`Rtsne` package), clusters of segments related to structure (glomeruli or
tubules) and disease status (normal or diabetic kidney disease).
``` {r dimReduction, eval = TRUE}
library(umap)
library(Rtsne)
# update defaults for umap to contain a stable random_state (seed)
custom_umap <- umap::umap.defaults
custom_umap$random_state <- 42
# run UMAP
umap_out <-
umap(t(log2(assayDataElement(target_demoData , elt = "q_norm"))),
config = custom_umap)
pData(target_demoData)[, c("UMAP1", "UMAP2")] <- umap_out$layout[, c(1,2)]
ggplot(pData(target_demoData),
aes(x = UMAP1, y = UMAP2, color = region, shape = class)) +
geom_point(size = 3) +
theme_bw()
# run tSNE
set.seed(42) # set the seed for tSNE as well
tsne_out <-
Rtsne(t(log2(assayDataElement(target_demoData , elt = "q_norm"))),
perplexity = ncol(target_demoData)*.15)
pData(target_demoData)[, c("tSNE1", "tSNE2")] <- tsne_out$Y[, c(1,2)]
ggplot(pData(target_demoData),
aes(x = tSNE1, y = tSNE2, color = region, shape = class)) +
geom_point(size = 3) +
theme_bw()
```
## Clustering high CV Genes
Another approach to explore the data is to calculate the coefficient of
variation (CV) for each gene ($g$) using the formula $CV_g = SD_g/mean_g$. We
then identify genes with high CVs that should have large differences across
the various profiled segments. This unbiased approach can reveal highly
variable genes across the study.
We plot the results using unsupervised hierarchical clustering, displayed as a
heatmap.
``` {r CVheatmap, eval = TRUE, echo = TRUE, fig.width = 8, fig.height = 6.5, fig.wide = TRUE}
library(pheatmap) # for pheatmap
# create a log2 transform of the data for analysis
assayDataElement(object = target_demoData, elt = "log_q") <-
assayDataApply(target_demoData, 2, FUN = log, base = 2, elt = "q_norm")
# create CV function
calc_CV <- function(x) {sd(x) / mean(x)}
CV_dat <- assayDataApply(target_demoData,
elt = "log_q", MARGIN = 1, calc_CV)
# show the highest CD genes and their CV values
sort(CV_dat, decreasing = TRUE)[1:5]
# Identify genes in the top 3rd of the CV values
GOI <- names(CV_dat)[CV_dat > quantile(CV_dat, 0.8)]
pheatmap(assayDataElement(target_demoData[GOI, ], elt = "log_q"),
scale = "row",
show_rownames = FALSE, show_colnames = FALSE,
border_color = NA,
clustering_method = "average",
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
breaks = seq(-3, 3, 0.05),
color = colorRampPalette(c("purple3", "black", "yellow2"))(120),
annotation_col =
pData(target_demoData)[, c("class", "segment", "region")])
```
# Differential Expression
A central method for exploring differences between groups of segments or
samples is to perform differential gene expression analysis. A common
statistical approach is to use a linear mixed-effect model (LMM). The LMM
allows the user to account for the subsampling per tissue; in other words, we
adjust for the fact that the multiple regions of interest placed per tissue
section are not independent observations, as is the assumption with other
traditional statistical tests. The formulation of the LMM model depends on the
scientific question being asked.
Overall, there are two flavors of the LMM model when used with GeoMx data: i)
with and ii) without random slope.
When comparing features that co-exist in a given tissue section (e.g. glomeruli
vs tubules in DKD kidneys), a random slope is included in the LMM model. When
comparing features that are mutually exclusive in a given tissue section
(healthy glomeruli versus DKD glomeruli) the LMM model does not require a
random slope. We represent the two variations on the LMM in the schematic below:
For more details on the LMM, please refer to the [lme4 package](https://cran.r-project.org/web/packages/lme4/) and the [lmerTest package](https://cran.r-project.org/web/packages/lmerTest/).
## Within Slide Analysis: Glomeruli vs Tubules
One informative exploration is to study differences between morphological
structures. In this example, we can study differential expression between
glomeruli and tubules. We will focus on the diseased kidney tissues. Because we
are comparing structures that co-exist within the a given tissue we will use
the LMM model with a random slope. Morphological structure (`Region`) is our
test variable. We control for tissue subsampling with `slide name` using a
random slope and intercept; the intercept adjusts for the multiple regions
placed per unique tissue, since we have one tissue per slide. If multiple
tissues are placed per slide, we would change the intercept variable to the
unique tissue name (ex: tissue name, Block ID, etc).
In this analysis we save log~2~ fold change estimates and P-values across all
levels in the factor of interest. We also apply a Benjamini-Hochberg multiple
test correction.
``` {r deNativeComplex, eval = TRUE, message = FALSE, warning = FALSE}
# convert test variables to factors
pData(target_demoData)$testRegion <-
factor(pData(target_demoData)$region, c("glomerulus", "tubule"))
pData(target_demoData)[["slide"]] <-
factor(pData(target_demoData)[["slide name"]])
assayDataElement(object = target_demoData, elt = "log_q") <-
assayDataApply(target_demoData, 2, FUN = log, base = 2, elt = "q_norm")
# run LMM:
# formula follows conventions defined by the lme4 package
results <- c()
for(status in c("DKD", "normal")) {
ind <- pData(target_demoData)$class == status
mixedOutmc <-
mixedModelDE(target_demoData[, ind],
elt = "log_q",
modelFormula = ~ testRegion + (1 + testRegion | slide),
groupVar = "testRegion",
nCores = parallel::detectCores(),
multiCore = FALSE)
# format results as data.frame
r_test <- do.call(rbind, mixedOutmc["lsmeans", ])
tests <- rownames(r_test)
r_test <- as.data.frame(r_test)
r_test$Contrast <- tests
# use lapply in case you have multiple levels of your test factor to
# correctly associate gene name with it's row in the results table
r_test$Gene <-
unlist(lapply(colnames(mixedOutmc),
rep, nrow(mixedOutmc["lsmeans", ][[1]])))
r_test$Subset <- status
r_test$FDR <- p.adjust(r_test$`Pr(>|t|)`, method = "fdr")
r_test <- r_test[, c("Gene", "Subset", "Contrast", "Estimate",
"Pr(>|t|)", "FDR")]
results <- rbind(results, r_test)
}
```
Note that the example uses `nCores = parallel:detectCores()` and
`multiCore = FALSE` to implement the `parallel` package clustering of all
available cores. If working in a Windows environment use `multicore = FALSE`.
If in a UNIX-based environment setting `multicore = TRUE` will parallelize
using the `mcapply` package. If you do not want to use all available cores,
change the `nCores` variable to the desired number to use.
## Interpreting the results table
Let's review the results from the analysis of glomeruli to tubules in
healthy (normal) patients. We saved the LMM outputs into a table (`results`)
containing three of the key features for differential expression: the log~2~
fold change value (`Estimate`), P-value (`Pr(>|t|)`), and false-discovery
adjusted P-values (`FDR`). Let's take a look at a few genes of interest. The
contrast column is used to interpret the log~2~ fold change value as it
specifies which levels are compared (e.g. positive fold change values when
comparing `glomerulus - tubule` indicates an enrichment in the glomerulus;
negative indicates enrichment in tubules).
We can display these results by subsetting the results table.
``` {r DEtable, echo = TRUE, results = "asis"}
kable(subset(results, Gene %in% goi & Subset == "normal"), digits = 3,
caption = "DE results for Genes of Interest",
align = "lc", row.names = FALSE)
```
## Between Slide Analysis: Diabetic Kidney Disease vs Healthy
Another informative exploration is to compare tissue cohorts. In this case, we
would like to compare diseased versus healthy kidneys. We will focus on
glomeruli as our structure. Because we are comparing disease status, which is
specific to the entire kidney, we will use the LMM model without a random
slope. Disease (`testClass`) is our test variable. Like our previous LMM
example, we control for tissue subsampling with `slide name` as the intercept.
``` {r DEsimple, eval = TRUE, echo = TRUE, message = FALSE, warning = FALSE}
# convert test variables to factors
pData(target_demoData)$testClass <-
factor(pData(target_demoData)$class, c("normal", "DKD"))
# run LMM:
# formula follows conventions defined by the lme4 package
results2 <- c()
for(region in c("glomerulus", "tubule")) {
ind <- pData(target_demoData)$region == region
mixedOutmc <-
mixedModelDE(target_demoData[, ind],
elt = "log_q",
modelFormula = ~ testClass + (1 | slide),
groupVar = "testClass",
nCores = parallel::detectCores(),
multiCore = FALSE)
# format results as data.frame
r_test <- do.call(rbind, mixedOutmc["lsmeans", ])
tests <- rownames(r_test)
r_test <- as.data.frame(r_test)
r_test$Contrast <- tests
# use lapply in case you have multiple levels of your test factor to
# correctly associate gene name with it's row in the results table
r_test$Gene <-
unlist(lapply(colnames(mixedOutmc),
rep, nrow(mixedOutmc["lsmeans", ][[1]])))
r_test$Subset <- region
r_test$FDR <- p.adjust(r_test$`Pr(>|t|)`, method = "fdr")
r_test <- r_test[, c("Gene", "Subset", "Contrast", "Estimate",
"Pr(>|t|)", "FDR")]
results2 <- rbind(results2, r_test)
}
```
We can review our genes of interest for this comparison as well. Let's focus on
results from analysis of tubules.
``` {r DEtable2, echo = TRUE, results = "asis"}
kable(subset(results2, Gene %in% goi & Subset == "tubule"), digits = 3,
caption = "DE results for Genes of Interest",
align = "lc", row.names = FALSE)
```
``` {r glomindex, eval = TRUE, echo = FALSE}
# since we didn't execute above, ID the glomeruli for later use
```
# Visualizing DE Genes
## Volcano Plots
A canonical visualization for interpreting differential gene expression results
is the volcano plot.
Let's look at the LMM results from our diseased glomeruli versus tubules
comparison.
``` {r volcanoPlot, fig.width = 11, fig.height = 7, fig.wide = TRUE, warning = FALSE, message = FALSE}
library(ggrepel)
# Categorize Results based on P-value & FDR for plotting
results$Color <- "NS or FC < 0.5"
results$Color[results$`Pr(>|t|)` < 0.05] <- "P < 0.05"
results$Color[results$FDR < 0.05] <- "FDR < 0.05"
results$Color[results$FDR < 0.001] <- "FDR < 0.001"
results$Color[abs(results$Estimate) < 0.5] <- "NS or FC < 0.5"
results$Color <- factor(results$Color,
levels = c("NS or FC < 0.5", "P < 0.05",
"FDR < 0.05", "FDR < 0.001"))
# pick top genes for either side of volcano to label
# order genes for convenience:
results$invert_P <- (-log10(results$`Pr(>|t|)`)) * sign(results$Estimate)
top_g <- c()
for(cond in c("DKD", "normal")) {
ind <- results$Subset == cond
top_g <- c(top_g,
results[ind, 'Gene'][
order(results[ind, 'invert_P'], decreasing = TRUE)[1:15]],
results[ind, 'Gene'][
order(results[ind, 'invert_P'], decreasing = FALSE)[1:15]])
}
top_g <- unique(top_g)
results <- results[, -1*ncol(results)] # remove invert_P from matrix
# Graph results
ggplot(results,
aes(x = Estimate, y = -log10(`Pr(>|t|)`),
color = Color, label = Gene)) +
geom_vline(xintercept = c(0.5, -0.5), lty = "dashed") +
geom_hline(yintercept = -log10(0.05), lty = "dashed") +
geom_point() +
labs(x = "Enriched in Tubules <- log2(FC) -> Enriched in Glomeruli",
y = "Significance, -log10(P)",
color = "Significance") +
scale_color_manual(values = c(`FDR < 0.001` = "dodgerblue",
`FDR < 0.05` = "lightblue",
`P < 0.05` = "orange2",
`NS or FC < 0.5` = "gray"),
guide = guide_legend(override.aes = list(size = 4))) +
scale_y_continuous(expand = expansion(mult = c(0,0.05))) +
geom_text_repel(data = subset(results, Gene %in% top_g & FDR < 0.001),
size = 4, point.padding = 0.15, color = "black",
min.segment.length = .1, box.padding = .2, lwd = 2,
max.overlaps = 50) +
theme_bw(base_size = 16) +
theme(legend.position = "bottom") +
facet_wrap(~Subset, scales = "free_y")
```
The volcano plot shows several genes that are significantly differentially
expressed between glomeruli and tubules, though some are specific to the
disease status of the sample. Note that because we use the linear mixed effect
model to account for tissue specific variation, the volcano plot shape may look
less typical than one generated with a linear regression model. There are some
genes for which we see high fold change, but lower significance, because these
genes appear to be behaving in a sample-specific manner rather than
consistently across all kidney samples.
In the next section, we will explore the expression of select gene targets to
demonstrate their dynamics.
## Plotting Genes of Interest
A simple and effective plot to view individual genes is the violin plot. This
visualization reveals both the dynamic range and shape of the distribution for
a gene target. We selected a few genes for which we validated
structure-specific expression from the
[Human Protein Atlas](https://www.proteinatlas.org). We will plot a gene
enriched within the glomeruli,
[ITGB1](https://www.proteinatlas.org/ENSG00000150093-ITGB1/tissue/kidney) and a
gene enriched within tubules,
[PDHA1](https://www.proteinatlas.org/ENSG00000131828-PDHA1/tissue/kidney).
First let's review the model results for these targets:
``` {r targetTable, eval = TRUE, as.is = TRUE}
kable(subset(results, Gene %in% c('PDHA1','ITGB1')), row.names = FALSE)
```
Here we see that while these genes are significantly enriched in a structure,
the structure-specific expression is also specific to the healthy tissue.
Let's look at the distribution across tissue structures for PDHA1.
``` {r targetExprs, eval = TRUE}
# show expression for a single target: PDHA1
ggplot(pData(target_demoData),
aes(x = region, fill = region,
y = as.numeric(assayDataElement(target_demoData["PDHA1", ],
elt = "q_norm")))) +
geom_violin() +
geom_jitter(width = .2) +
labs(y = "PDHA1 Expression") +
scale_y_continuous(trans = "log2") +
facet_wrap(~class) +
theme_bw()
```
Now we can plot these two targets against each other to show the mutually
exclusive expression pattern that can be used to easily distinguish the two
structures. The dashed vertical line represents the maximum observed PDHA1
expression in glomeruli and the horizontal line represents the maximum observed
ITGB1 expression in the tubules.
``` {r targetExprs2, fig.width = 8, fig.wide = TRUE, eval = TRUE}
glom <- pData(target_demoData)$region == "glomerulus"
# show expression of PDHA1 vs ITGB1
ggplot(pData(target_demoData),
aes(x = as.numeric(assayDataElement(target_demoData["PDHA1", ],
elt = "q_norm")),
y = as.numeric(assayDataElement(target_demoData["ITGB1", ],
elt = "q_norm")),
color = region)) +
geom_vline(xintercept =
max(assayDataElement(target_demoData["PDHA1", glom],
elt = "q_norm")),
lty = "dashed", col = "darkgray") +
geom_hline(yintercept =
max(assayDataElement(target_demoData["ITGB1", !glom],
elt = "q_norm")),
lty = "dashed", col = "darkgray") +
geom_point(size = 3) +
theme_bw() +
scale_x_continuous(trans = "log2") +
scale_y_continuous(trans = "log2") +
labs(x = "PDHA1 Expression", y = "ITGB1 Expression") +
facet_wrap(~class)
```
These results suggest both of these genes become less specific during the
disease process.
## Heatmap of Significant Genes
In addition to generating individual gene box plots or volcano plots, we can
again create a heatmap from our data. This time rather than utilizing CV to
select genes, we can use the P-value or FDR values to select genes. Here, we
plot all genes with an FDR < 0.001.
``` {r heatmap, eval = TRUE, fig.width = 8, fig.height = 6.5, fig.wide = TRUE}
# select top significant genes based on significance, plot with pheatmap
GOI <- unique(subset(results, `FDR` < 0.001)$Gene)
pheatmap(log2(assayDataElement(target_demoData[GOI, ], elt = "q_norm")),
scale = "row",
show_rownames = FALSE, show_colnames = FALSE,
border_color = NA,
clustering_method = "average",
clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation",
cutree_cols = 2, cutree_rows = 2,
breaks = seq(-3, 3, 0.05),
color = colorRampPalette(c("purple3", "black", "yellow2"))(120),
annotation_col = pData(target_demoData)[, c("region", "class")])
```
# Additional Resources
While this vignette has focused on the full data analysis workflow, we have
created additional vignettes to describe in detail the functionalaties and
tools built into the `GeomxTools` package that more advanced users may be
interested in. Please see the
[GeomxTools Vignette](https://bioconductor.org/packages/release/bioc/vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html)
for more detailed information on all `GeomxTools` documentation.
## GeoMxSet Object Overview
In the section 'Loading the Demo Data', we see that the `demoData` object is
stored as a `GeoMxSet Object`. `GeoMxSet` objects are based on `ExpressionSet`
objects, and have many similar functions as those described
[on Bioconductor](https://www.bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf).
All expression, annotation, and probe information are linked and stored
together as shown in the schematic below. There are a few key ways to access
the data after we create a GeoMxSet Object. We use these throughout the
vignette.
<center>
</center>
As is shown above the GeoMxSet object consists of 3 or more elements.
* Expression count matrices - accessed with `exprs(object)` to return the
matrix.
+ As we progress through the analysis we will add new expression matrices
to expression slots, or elements (`elt`), which can be accessed with
`assayDataElement(object, elt = ...)`
* Segment & Sample annotations - accessed with `pData(object)`
* Probe & Target information - accessed with `fData(object)`
* To learn more about other accessory functions type: `?GeomxTools::sData`
Both eSets and GeoMxSets use `esApply` to extend the
[`apply`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/apply)
function to such objects. In addition to the `esApply` function defined by the
`ExpressionSet` class, we have built the `assayDataApply` function to allow you
to select which expression matrix `elt` you wish to use with the `esApply`
function.
## Links & References
GeoMx Background:
* [Kidney Geomx Demo Dataset](http://nanostring-public-share.s3-website-us-west-2.amazonaws.com/GeoScriptHub/Kidney_Dataset_for_GeomxTools.zip)
* [Merritt et al., 2020](https://pubmed.ncbi.nlm.nih.gov/32393914/) -
Manuscript describing GeoMx Workflow and technology
* [Data Analysis White Paper for DSP-NGS Assays](https://www.nanostring.com/resources/geomx-cta-data-whitepaper/)
* [Zimmerman et al., 2022](https://genome.cshlp.org/content/early/2022/10/18/gr.276206.121.full.pdf+html) - Whole Transcriptome Assay on GeoMx
Statistics & Packages:
* [Bioconductor Wesbite for GeomxTools](https://www.bioconductor.org/packages/release/bioc/html/GeomxTools.html)
* [Introduction to ExpressionSets on Bioconductor](https://www.bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf)
* [Lme4 package documentation](https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf)
* [Benjamini-Hochberg FDR Manuscript](http://www.math.tau.ac.il/~ybenja/MyPapers/benjamini_hochberg1995.pdf)
Other NanoString R packages:
* SpatialDecon - [Bioconductor](https://bioconductor.org/packages/release/bioc/html/SpatialDecon.html), [manuscript](https://www.nature.com/articles/s41467-022-28020-5)
* SpatialOmicsOverlay - [Bioconductor](https://bioconductor.org/packages/release/bioc/html/SpatialOmicsOverlay.html), [NanoString's GeoScriptHub](https://nanostring.com/products/geomx-digital-spatial-profiler/geoscript-hub/)
## Additional Analysis Methods
### MA Plot
Another visualization for differential expression is an MA plot. With this
plot, we look for differences relative to expression within the baseline
feature. As we have filtered out genes with low expression, the MA plot will
not exhibit a traditional shape, but it can be useful in identifying targets
with relatively high fold change and baseline expression. In the plot below we
keep the labeled genes within the top 90th percentile of expression on average
with an FDR of < 0.001 and a log~2~ FC > 0.5.
``` {r maPlot, fig.width = 8, fig.height = 12, fig.wide = TRUE, warning = FALSE, message = FALSE}
results$MeanExp <-
rowMeans(assayDataElement(target_demoData,
elt = "q_norm"))
top_g2 <- results$Gene[results$Gene %in% top_g &
results$FDR < 0.001 &
abs(results$Estimate) > .5 &
results$MeanExp > quantile(results$MeanExp, 0.9)]
ggplot(subset(results, !Gene %in% neg_probes),
aes(x = MeanExp, y = Estimate,
size = -log10(`Pr(>|t|)`),
color = Color, label = Gene)) +
geom_hline(yintercept = c(0.5, -0.5), lty = "dashed") +
scale_x_continuous(trans = "log2") +
geom_point(alpha = 0.5) +
labs(y = "Enriched in Glomeruli <- log2(FC) -> Enriched in Tubules",
x = "Mean Expression",
color = "Significance") +
scale_color_manual(values = c(`FDR < 0.001` = "dodgerblue",
`FDR < 0.05` = "lightblue",
`P < 0.05` = "orange2",
`NS or FC < 0.5` = "gray")) +
geom_text_repel(data = subset(results, Gene %in% top_g2),
size = 4, point.padding = 0.15, color = "black",
min.segment.length = .1, box.padding = .2, lwd = 2) +
theme_bw(base_size = 16) +
facet_wrap(~Subset, nrow = 2, ncol = 1)
```
# Session Information
```{r sessInfo}
sessionInfo()
```