GeomxTools 3.0.0
R version: R version 4.2.0 RC (2022-04-19 r82224)
Bioconductor version: 3.15
Package: 3.0.0
<>
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. 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.
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:
Let’s install and load the GeoMx packages we need:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes most up to date version of Bioc
BiocManager::install(version="3.15")
BiocManager::install("NanoStringNCTools")
BiocManager::install("GeomxTools")
BiocManager::install("GeoMxWorkflows")
library(NanoStringNCTools)
library(GeomxTools)
library(GeoMxWorkflows)
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
}
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
The key data files are:
We first locate the downloaded files:
# 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
# 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$",
full.names = TRUE, recursive = TRUE)
We then load the data to create a data object using the
readNanoStringGeoMxSet
function.
# load data
demoData <-
readNanoStringGeoMxSet(dccFiles = DCCFiles,
pkcFiles = PKCFiles,
phenoDataFile = SampleAnnotationFile,
phenoDataSheet = "Template",
phenoDataDccColName = "Sample_ID",
protocolDataColNames = c("aoi", "roi"),
experimentDataColNames = c("panel"))
All of the expression, annotation, and probe information are now linked and stored together into a single data object.
For more details on this object’s structure and accessors, please refer to the “GeoMxSet Object Overview” section at the end of this vignette.
First let’s access the PKC files, to ensure that the expected PKCs have been
loaded for this study. For the demo data we are using the file
Hsa_WTA_1.0.pkc
.
library(knitr)
pkcs <- annotation(demoData)
modules <- gsub(".pkc", "", pkcs)
kable(data.frame(PKCs = pkcs, modules = modules))
PKCs | modules |
---|---|
Hsa_WTA_v1.0.pkc | Hsa_WTA_v1.0 |
Now that we have loaded the data, we can visually summarize the experimental design for our dataset to look at the different types of samples and ROI/AOI segments that have been profiled. We present this information in a Sankey diagram.
library(dplyr)
library(ggforce)
# select the annotations we want to show, use `` to surround column names with
# spaces or special symbols
count_mat <- count(pData(demoData), `slide name`, class, region, segment)
# simplify the slide names
count_mat$`slide name` <- gsub("disease", "d",
gsub("normal", "n", count_mat$`slide name`))
# gather the data and plot in order: class, slide name, region, segment
test_gr <- gather_set_data(count_mat, 1:4)
test_gr$x <- factor(test_gr$x,
levels = c("class", "slide name", "region", "segment"))
# plot Sankey
ggplot(test_gr, aes(x, id = id, split = y, value = n)) +
geom_parallel_sets(aes(fill = region), alpha = 0.5, axis.width = 0.1) +
geom_parallel_sets_axes(axis.width = 0.2) +
geom_parallel_sets_labels(color = "white", size = 5) +
theme_classic(base_size = 17) +
theme(legend.position = "bottom",
axis.ticks.y = element_blank(),
axis.line = element_blank(),
axis.text.y = element_blank()) +
scale_y_continuous(expand = expansion(0)) +
scale_x_discrete(expand = expansion(0)) +
labs(x = "", y = "") +
annotate(geom = "segment", x = 4.25, xend = 4.25,
y = 20, yend = 120, lwd = 2) +
annotate(geom = "text", x = 4.19, y = 70, angle = 90, size = 5,
hjust = 0.5, label = "100 segments")
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.
# Shift counts to one
demoData <- shiftCountsOne(demoData, useDALogic = TRUE)
We first assess sequencing quality and adequate tissue sampling for every ROI/AOI segment.
Every ROI/AOI segment will be tested for:
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.
# 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"))
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.
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.
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)$NTC),
col.names = c("NTC Count", "# of Segments"))
NTC Count | # of Segments |
---|---|
3 | 36 |
113 | 71 |
397 | 34 |
8704 | 94 |
Finally we plot all of the QC Summary information in a table.
kable(QC_Summary, caption = "QC Summary Table for each Segment")
Pass | Warning | |
---|---|---|
LowReads | 231 | 4 |
LowTrimmed | 235 | 0 |
LowStitched | 235 | 0 |
LowAligned | 229 | 6 |
LowSaturation | 231 | 4 |
LowNegatives | 235 | 0 |
HighNTC | 235 | 0 |
LowNuclei | 235 | 0 |
LowArea | 235 | 0 |
TOTAL FLAGS | 229 | 6 |
As the final step in Segment QC, we remove flagged segments that do not meet our QC cutoffs.
demoData <- demoData[, QCResults$QCStatus == "PASS"]
# Subsetting our dataset has removed samples which did not pass QC
dim(demoData)
#> Features Samples
#> 18642 229
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.
A probe is removed globally from the dataset if either of the following is true:
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.
# 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.
Passed | Global | Local |
---|---|---|
18619 | 1 | 22 |
#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)
#> Features Samples
#> 18641 229
demoData <- ProbeQCPassed
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.
# Check how many unique targets the object has
length(unique(featureData(demoData)[["TargetName"]]))
#> [1] 18504
# collapse to targets
target_demoData <- aggregateCounts(demoData)
dim(target_demoData)
#> Features Samples
#> 18504 229
exprs(target_demoData)[1:5, 1:2]
#> DSP-1001250007851-H-A02.dcc DSP-1001250007851-H-A03.dcc
#> A2M 485 262
#> NAT2 15 18
#> ACADM 31 15
#> ACADS 27 17
#> ACAT1 29 24
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.
# 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)$LOQ <- LOQ
After determining the limit of quantification (LOQ) per segment, we recommend filtering out either segments and/or genes with abnormally low signal. Filtering is an important step to focus on the true biological data of interest.
We determine the number of genes detected in each segment across the dataset.
LOQ_Mat <- c()
for(module in modules) {
ind <- fData(target_demoData)$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)$TargetName, ]
We first filter out segments with exceptionally low signal. These segments will have a small fraction of panel genes detected above the LOQ relative to the other segments in the study. Let’s visualize the distribution of segments with respect to their % genes detected:
# Save detection rate information to pheno data
pData(target_demoData)$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:
# cut percent genes detected at 1, 5, 10, 15
kable(table(pData(target_demoData)$DetectionThreshold,
pData(target_demoData)$class))
DKD | normal | |
---|---|---|
<1% | 0 | 1 |
1-5% | 0 | 0 |
5-10% | 6 | 1 |
10-15% | 21 | 4 |
>15% | 102 | 94 |
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.
target_demoData <-
target_demoData[, pData(target_demoData)$GeneDetectionRate >= .1]
dim(target_demoData)
#> Features Samples
#> 18504 221
Let’s re-plot the Sankey diagram showing our current working dataset. This is now a dataset that no longer contains segments flagged by Segment QC or that have low gene detection rates.
# select the annotations we want to show, use `` to surround column names with
# spaces or special symbols
count_mat <- count(pData(demoData), `slide name`, class, region, segment)
# simplify the slide names
count_mat$`slide name` <-
gsub("disease", "d",
gsub("normal", "n", count_mat$`slide name`))
# gather the data and plot in order: class, slide name, region, segment
test_gr <- gather_set_data(count_mat, 1:4)
test_gr$x <-
factor(test_gr$x,
levels = c("class", "slide name", "region", "segment"))
# plot Sankey
ggplot(test_gr, aes(x, id = id, split = y, value = n)) +
geom_parallel_sets(aes(fill = region), alpha = 0.5, axis.width = 0.1) +
geom_parallel_sets_axes(axis.width = 0.2) +
geom_parallel_sets_labels(color = "white", size = 5) +
theme_classic(base_size = 17) +
theme(legend.position = "bottom",
axis.ticks.y = element_blank(),
axis.line = element_blank(),
axis.text.y = element_blank()) +
scale_y_continuous(expand = expansion(0)) +
scale_x_discrete(expand = expansion(0)) +
labs(x = "", y = "") +
annotate(geom = "segment", x = 4.25, xend = 4.25, y = 20,
yend = 120, lwd = 2) +
annotate(geom = "text", x = 4.19, y = 70, angle = 90, size = 5,
hjust = 0.5, label = "100 segments")
Next, we determine the detection rate for genes across the study. To illustrate
this idea, we create a small gene list (goi
) to review.
library(scales) # for percent
# Calculate detection rate:
LOQ_Mat <- LOQ_Mat[, colnames(target_demoData)]
fData(target_demoData)$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"]))
Gene | Detection, # Segments | Detection Rate, % of Segments |
---|---|---|
PDCD1 | 1 | 0.5% |
CD274 | 75 | 33.9% |
IFNG | 9 | 4.1% |
CD8A | 33 | 14.9% |
CD68 | 160 | 72.4% |
EPCAM | 64 | 29.0% |
KRT18 | 217 | 98.2% |
NPHS1 | 142 | 64.3% |
NPHS2 | 142 | 64.3% |
CALB1 | 41 | 18.6% |
CLDN8 | 47 | 21.3% |
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.
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.
# 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$Freq
ggplot(plot_detect, aes(x = as.factor(Freq), y = Rate, fill = Rate)) +
geom_bar(stat = "identity") +
geom_text(aes(label = formatC(Number, format = "d", big.mark = ",")),
vjust = 1.6, color = "black", size = 4) +
scale_fill_gradient2(low = "orange2", mid = "lightblue",
high = "dodgerblue3", midpoint = 0.65,
limits = c(0,1),
labels = scales::percent) +
theme_bw() +
scale_y_continuous(labels = scales::percent, limits = c(0,1),
expand = expansion(mult = c(0, 0))) +
labs(x = "% of Segments",
y = "Genes Detected, % of Panel > LOQ")
We typically set a % Segment cutoff ranging from 5-20% based on the biological diversity of our dataset. For this study, we will select 10% as our cutoff. In other words, we will focus on the genes detected in at least 10% of our segments; we filter out the remainder of the targets.
Note: if we know that a key gene is represented in only a small number of segments (<10%) due to biological diversity, we may select a different cutoff or keep the target gene by manually selecting it for inclusion in the data object.
# Subset to target genes detected in at least 10% of the samples.
# Also manually include the negative control probe, for downstream use
negativeProbefData <- subset(fData(target_demoData), CodeClass == "Negative")
neg_probes <- unique(negativeProbefData$TargetName)
target_demoData <-
target_demoData[fData(target_demoData)$DetectionRate >= 0.1 |
fData(target_demoData)$TargetName %in% neg_probes, ]
dim(target_demoData)
#> Features Samples
#> 10131 221
# retain only detected genes of interest
goi <- goi[goi %in% rownames(target_demoData)]
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.
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.
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"
.
# 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.
# 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")
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).
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()
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.
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]
#> CAMK2N1 AKR1C1 AQP2 GDF15 REN
#> 0.5886006 0.5114973 0.4607206 0.4196469 0.4193216
# 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")])
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 and the lmerTest package.
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 log2 fold change estimates and P-values across all levels in the factor of interest. We also apply a Benjamini-Hochberg multiple test correction.
# 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.
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 log2
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 log2 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.
kable(subset(results, Gene %in% goi & Subset == "normal"), digits = 3,
caption = "DE results for Genes of Interest",
align = "lc", row.names = FALSE)
Gene | Subset | Contrast | Estimate | Pr(>|t|) | FDR |
---|---|---|---|---|---|
KRT18 | normal | glomerulus - tubule | -1.169 | 0.076 | 0.237 |
CD68 | normal | glomerulus - tubule | -0.153 | 0.289 | 0.483 |
CD8A | normal | glomerulus - tubule | -0.227 | 0.147 | 0.332 |
NPHS1 | normal | glomerulus - tubule | 3.809 | 0.001 | 0.012 |
CALB1 | normal | glomerulus - tubule | -2.014 | 0.027 | 0.138 |
CD274 | normal | glomerulus - tubule | 0.223 | 0.031 | 0.147 |
NPHS2 | normal | glomerulus - tubule | 5.430 | 0.002 | 0.025 |
CLDN8 | normal | glomerulus - tubule | -1.961 | 0.001 | 0.011 |
EPCAM | normal | glomerulus - tubule | -2.297 | 0.000 | 0.006 |
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.
# 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.
kable(subset(results2, Gene %in% goi & Subset == "tubule"), digits = 3,
caption = "DE results for Genes of Interest",
align = "lc", row.names = FALSE)
Gene | Subset | Contrast | Estimate | Pr(>|t|) | FDR |
---|---|---|---|---|---|
KRT18 | tubule | normal - DKD | -0.096 | 0.748 | 0.997 |
CD68 | tubule | normal - DKD | -0.726 | 0.124 | 0.965 |
CD8A | tubule | normal - DKD | -0.057 | 0.826 | 0.998 |
NPHS1 | tubule | normal - DKD | -0.131 | 0.624 | 0.997 |
CALB1 | tubule | normal - DKD | 1.408 | 0.013 | 0.652 |
CD274 | tubule | normal - DKD | -0.252 | 0.522 | 0.997 |
NPHS2 | tubule | normal - DKD | -0.128 | 0.730 | 0.997 |
CLDN8 | tubule | normal - DKD | 0.979 | 0.000 | 0.058 |
EPCAM | tubule | normal - DKD | 0.339 | 0.382 | 0.997 |
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.
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.
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. We will plot a gene enriched within the glomeruli, ITGB1 and a gene enriched within tubules, PDHA1.
First let’s review the model results for these targets:
kable(subset(results, Gene %in% c('PDHA1','ITGB1')), row.names = FALSE)
Gene | Subset | Contrast | Estimate | Pr(>|t|) | FDR | Color |
---|---|---|---|---|---|---|
PDHA1 | DKD | glomerulus - tubule | -1.0657650 | 0.0065159 | 0.1122655 | P < 0.05 |
ITGB1 | DKD | glomerulus - tubule | 0.6769474 | 0.0307688 | 0.2192658 | P < 0.05 |
PDHA1 | normal | glomerulus - tubule | -1.5416314 | 0.0000000 | 0.0000000 | FDR < 0.001 |
ITGB1 | normal | glomerulus - tubule | 1.5042831 | 0.0000000 | 0.0000000 | FDR < 0.001 |
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.
# show expression for a single target: PDHA1
ggplot(pData(target_demoData),
aes(x = region, fill = region,
y = 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.
glom <- pData(target_demoData)$region == "glomerulus"
# show expression of PDHA1 vs ITGB1
ggplot(pData(target_demoData),
aes(x = assayDataElement(target_demoData["PDHA1", ],
elt = "q_norm"),
y = 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.
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.
# 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")])
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
for more detailed information on all GeomxTools
documentation.
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.
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.
As is shown above the GeoMxSet object consists of 3 or more elements.
exprs(object)
to return the
matrix.
elt
), which can be accessed with
assayDataElement(object, elt = ...)
pData(object)
fData(object)
?GeomxTools::sData
Both eSets and GeoMxSets use esApply
to extend the
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.
GeoMx Background:
Statistics & Packages:
Other NanoString R packages:
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 log2 FC > 0.5.
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)
sessionInfo()
#> R version 4.2.0 RC (2022-04-19 r82224)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] ggrepel_0.9.1 pheatmap_1.0.12 Rtsne_0.16
#> [4] umap_0.2.8.0 cowplot_1.1.1 reshape2_1.4.4
#> [7] scales_1.2.0 ggforce_0.3.3 dplyr_1.0.9
#> [10] knitr_1.39 GeoMxWorkflows_1.2.0 GeomxTools_3.0.0
#> [13] NanoStringNCTools_1.4.0 ggplot2_3.3.5 S4Vectors_0.34.0
#> [16] Biobase_2.56.0 BiocGenerics_0.42.0 BiocStyle_2.24.0
#>
#> loaded via a namespace (and not attached):
#> [1] nlme_3.1-157 bitops_1.0-7 EnvStats_2.7.0
#> [4] RColorBrewer_1.1-3 GenomeInfoDb_1.32.1 numDeriv_2016.8-1.1
#> [7] tools_4.2.0 bslib_0.3.1 utf8_1.2.2
#> [10] R6_2.5.1 vipor_0.4.5 DBI_1.1.2
#> [13] colorspace_2.0-3 withr_2.5.0 tidyselect_1.1.2
#> [16] GGally_2.1.2 compiler_4.2.0 cli_3.3.0
#> [19] labeling_0.4.2 bookdown_0.26 sass_0.4.1
#> [22] askpass_1.1 systemfonts_1.0.4 stringr_1.4.0
#> [25] digest_0.6.29 minqa_1.2.4 rmarkdown_2.14
#> [28] XVector_0.36.0 pkgconfig_2.0.3 htmltools_0.5.2
#> [31] lme4_1.1-29 highr_0.9 fastmap_1.1.0
#> [34] htmlwidgets_1.5.4 rlang_1.0.2 ggthemes_4.2.4
#> [37] readxl_1.4.0 farver_2.1.0 jquerylib_0.1.4
#> [40] generics_0.1.2 jsonlite_1.8.0 RCurl_1.98-1.6
#> [43] magrittr_2.0.3 GenomeInfoDbData_1.2.8 Matrix_1.4-1
#> [46] Rcpp_1.0.8.3 ggbeeswarm_0.6.0 munsell_0.5.0
#> [49] fansi_1.0.3 reticulate_1.24 lifecycle_1.0.1
#> [52] stringi_1.7.6 yaml_2.3.5 MASS_7.3-57
#> [55] zlibbioc_1.42.0 plyr_1.8.7 grid_4.2.0
#> [58] parallel_4.2.0 crayon_1.5.1 lattice_0.20-45
#> [61] Biostrings_2.64.0 splines_4.2.0 magick_2.7.3
#> [64] pillar_1.7.0 uuid_1.1-0 boot_1.3-28
#> [67] rjson_0.2.21 glue_1.6.2 ggiraph_0.8.2
#> [70] evaluate_0.15 outliers_0.15 SeuratObject_4.0.4
#> [73] data.table_1.14.2 BiocManager_1.30.17 png_0.1-7
#> [76] tweenr_1.0.2 vctrs_0.4.1 nloptr_2.0.0
#> [79] cellranger_1.1.0 openssl_2.0.0 polyclip_1.10-0
#> [82] gtable_0.3.0 purrr_0.3.4 reshape_0.8.9
#> [85] assertthat_0.2.1 xfun_0.30 RSpectra_0.16-1
#> [88] tibble_3.1.6 lmerTest_3.1-3 beeswarm_0.4.0
#> [91] IRanges_2.30.0 ellipsis_0.3.2