Skip to content
Marker gene profile estimation method used in NeuroExpresso manuscript
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
R
README_files
data
man
process
tests
.Rbuildignore
.gitignore
.travis.yml
DESCRIPTION
NAMESPACE
README.Rmd
README.md
markerGeneProfile.Rproj

README.md

Build Statuscodecov

markerGeneProfile

This package includes functions responsible for marker gene selection and marker gene profile estimation estimation as described in Mancarci et al. 2017. It also includes a copy of mouse brain cell type markers from the neuroExpressoAnalysis package for convenience along with mock data for easy testing.

Installation

Use devtools to install. Additional github packages needs to be installed for it work.

devtools::install_github('oganm/markerGeneProfile')

In this document additional packages are used that are not package dependencies

install.packages('ggplot2')
install.packages('gplots')
install.packages('viridis')
install.packages('dplyr')
install.packages('knitr')

Usage

Marker genes

A list of marker genes specific to or enriched in a cell type is required in order to estimate cell type profiles. In this package, a copy of mouse brain cell type-specific markers from neuroExpressoAnalysis, the package that summarizes the entire analysis performed in Mancarci et al. 2017 is included (mouseMarkerGenes). If an different marker gene set is needed, steps outlined below can be followed to create one from a new dataset.

Sample data for marker gene selection

This package includes a sample cell type-specific transcriptomic dataset representing expression profiles from multiple purified cell types aimed to demonstrate the minimal information required for the selection of marker genes.

mgp_sampleProfilesMeta includes the basic metadata required for the cell type specific expression dataset.

data(mgp_sampleProfilesMeta)
knitr::kable(head(mgp_sampleProfilesMeta))
sampleName replicate PMID CellType region RegionToParent RegionToChildren
Sample01 1 1 Cell A Region 1 TRUE TRUE
Sample02 1 1 Cell A Region 1 TRUE TRUE
Sample03 1 1 Cell A Region 1 TRUE TRUE
Sample04 2 2 Cell A Region 1 TRUE TRUE
Sample05 2 2 Cell A Region 1 TRUE TRUE
Sample06 2 2 Cell A Region 1 TRUE TRUE

sampleName: name of the samples. This needs to correspond to column names in the expression file.

replicate: A vector marking which samples are replicates of each other.

**PMID: ** A vector marking which samples come from the same study. Normally taking PMIDs of the papers is a good idea.

CellType: A vector marking the cell types that the samples represent.

region: The regions samples are extracted from. Only needed if region specific genes are to be selected.

RegionToParent: If region specific genes are to be selected and a region hierarchy is to be used, this column controls whether or not the sample should be included in the parent regions of the indicated region. If not provided it will default to TRUE. The name of this column is hard coded and should not be changed.

RegionToChildren: Same as above except it controls if the sample should be included in the children regions. If not provided it will default to TRUE. The name of this column is hard coded and should not be changed.

mgp_sampleProfiles is a sample expression data. Gene.Symbol column is the gene identifier that should be composed of unique IDs while the rest are sample names that corresponds to the relevant column in the metadata file. Other columns can be present before the sample data but they should not be of class double.

data(mgp_sampleProfiles)
knitr::kable(mgp_sampleProfiles)
Gene.Symbol Sample01 Sample02 Sample03 Sample04 Sample05 Sample06 Sample07 Sample08 Sample09 Sample10 Sample11 Sample12 Sample13 Sample14 Sample15 Sample16 Sample17 Sample18
Gene1 16 16 16 16 16 16 1 1 1 1 1 1 1 1 1 1 1 1
Gene2 1 1 1 1 1 1 16 16 16 16 16 16 1 1 1 1 1 1
Gene3 1 1 1 1 1 1 1 1 1 1 1 1 16 16 16 16 16 16
Gene4 1 1 1 1 1 1 1 1 1 1 1 1 13 13 13 13 13 13
Gene5 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 9 9 9
Gene6 1 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7

mpg_sampleRegionHiearchy is a sample region hiearchy. It is a nested named list.

data(mpg_sampleRegionHiearchy)
mpg_sampleRegionHiearchy
## $All
## $All$`Region 1`
## [1] ""
## 
## $All$`Region 2`
## [1] ""
nametree(mpg_sampleRegionHiearchy)
## All
## ├──Region 1
## └──Region 2

In this example Region 1 and Region 2 are subsets of All region

Selection of marker genes

Marker gene selection is performed using three functions: markerCandidates, pickMarkers and rotateSelect. By default markerCandidates will return files for each cell type in a region that lists the gene that are above a given silhouette and fold change thresholds. Other variables are briefly explained below but see the package documentation for in depth explanations

markerCandidates(design = mgp_sampleProfilesMeta, # the design file
                 expression = mgp_sampleProfiles, # expression file 
                 outLoc = 'README_files/quickSelection', # output directory
                 groupNames = 'CellType', # name of the column with cell types. can be a vector
                 regionNames = 'region', # name of the column with brain regions. leave NULL if no region seperation is desired
                 PMID = 'PMID', # name of the column with study identifiers
                 sampleName = 'sampleName', # name of the column with sample names
                 replicates = 'replicate', # name of the column with replicates
                 foldChangeThresh = 10, # threshold of fold change for gene selection (default is 10)
                 minimumExpression = 8, # minimum expression level that a gene can be considered a marker gene (default is 8)
                 background = 6, # background level of expression (default is 6)
                 regionHierarchy = mpg_sampleRegionHiearchy, # hierarchy of brain regions to be used
                 geneID = 'Gene.Symbol', # column name with with gene idenditifers
                 cores = 8 # number of cores to use in parallelization 
                 )

This creates 3 directories in the output directory

list.files('README_files/quickSelection')
## [1] "All_CellType"      "CellType"          "Region 2_CellType"

The CellType directory is a list of marker genes that disregards all region specifications (redundant with All_CellType in this case) while Region 2_CellType and All_CellType directories inlcude cell types from the relevant region. Note the absence of Region 1_CellType since that region only has a single cell type.

read.table('README_files/quickSelection/All_CellType/Cell C') %>% knitr::kable()
V1 V2 V3 V4
Gene3 15 1 1
Gene4 12 1 1
Gene5 8 1 1

This file shows the candidate genes for cell type Cell C in region All. The first column is the gene identifier, the second is change in expression in log_2 scale and the third one is the silhouette coefficient. Note that Gene6 is absent since its expression level was below the minimum level allowed. markerCandidates function does not apply a threshold for silhouette coefficient it also doesn’t check to see if a gene satisfies fold change threshold for multiple genes. pickMarkers function does that.

pickMarkers('README_files/quickSelection/All_CellType/',
            foldChange = 1,  # this is a fixed fold change threshold that ignores some leniency that comes from markerCandidates. setting it to 1 makes it irrelevant
            silhouette = 0.5)
## $`Cell A`
## [1] "Gene1"
## 
## $`Cell B`
## [1] "Gene2"
## 
## $`Cell C`
## [1] "Gene3" "Gene4" "Gene5"

If all genes for all regions needs to be seen

pickMarkersAll('README_files/quickSelection',
               foldChange = 1,
               silhouette = 0.5)
## $All_CellType
## $All_CellType$`Cell A`
## [1] "Gene1"
## 
## $All_CellType$`Cell B`
## [1] "Gene2"
## 
## $All_CellType$`Cell C`
## [1] "Gene3" "Gene4" "Gene5"
## 
## 
## $CellType
## $CellType$`Cell A`
## [1] "Gene1"
## 
## $CellType$`Cell B`
## [1] "Gene2"
## 
## $CellType$`Cell C`
## [1] "Gene3" "Gene4" "Gene5"
## 
## 
## $`Region 2_CellType`
## $`Region 2_CellType`$`Cell B`
## [1] "Gene2"
## 
## $`Region 2_CellType`$`Cell C`
## [1] "Gene3" "Gene4" "Gene5"

Better selection of marker genes

The above method is a quick way to pick markers but it does not handle bimodality in expression distribution well. To ensure robustness of the results it is better to perform multiple selections with permutations. markerCandidates function has variables to handle permutations for you. rotate controls what is the percentage of samples that should be removed every time. seed controls the random seed and is there to ensure reproducibility.

for (i in 1:10){
    markerCandidates(design = mgp_sampleProfilesMeta, # the design file
                     expression = mgp_sampleProfiles, # expression file 
                     outLoc = file.path('README_files/Rotation',i), # output directory
                     groupNames = 'CellType', # name of the column with cell types. can be a vector
                     regionNames = 'region', # name of the column with brain regions. leave NULL if no region seperation is desired
                     PMID = 'PMID', # name of the column with study identifiers
                     sampleName = 'sampleName', # name of the column with sample names
                     replicates = 'replicate', # name of the column with replicates
                     foldChangeThresh = 10, # threshold of fold change for gene selection (default is 10)
                     minimumExpression = 8, # minimum expression level that a gene can be considered a marker gene (default is 8)
                     background = 6, # background level of expression (default is 6)
                     regionHierarchy = mpg_sampleRegionHiearchy, # hierarchy of brain regions to be used
                     geneID = 'Gene.Symbol', # column name with with gene idenditifers
                     cores = 8, # number of cores to use in parallelization 
                     rotate = 0.33,
                     seed = i
    )
}

This creates multiple selection directories. rotateSelect can be used to count the number of times a gene is selected for each cell type in each region. This creates another directory similar to the output of markerCandidates. Again, valid markers can be acquired using pickMarkers

rotateSelect(rotationOut='README_files/Rotation',
                 rotSelOut='README_files/RotSel',
                 cores = 8,
                 foldChange = 1 # this is a fixed fold change threshold that ignores some leniency that comes from markerCandidates. setting it to 1 makes it irrelevant
             )
pickMarkers('README_files/RotSel/All_CellType/',rotationThresh = 0.95)
## $`Cell A`
## [1] "Gene1"
## 
## $`Cell B`
## [1] "Gene2"
## 
## $`Cell C`
## [1] "Gene3" "Gene4" "Gene5"
pickMarkersAll('README_files/RotSel',rotationThresh = 0.95)
## $All_CellType
## $All_CellType$`Cell A`
## [1] "Gene1"
## 
## $All_CellType$`Cell B`
## [1] "Gene2"
## 
## $All_CellType$`Cell C`
## [1] "Gene3" "Gene4" "Gene5"
## 
## 
## $CellType
## $CellType$`Cell A`
## [1] "Gene1"
## 
## $CellType$`Cell B`
## [1] "Gene2"
## 
## $CellType$`Cell C`
## [1] "Gene3" "Gene4" "Gene5"
## 
## 
## $`Region 2_CellType`
## $`Region 2_CellType`$`Cell B`
## [1] "Gene2"
## 
## $`Region 2_CellType`$`Cell C`
## [1] "Gene3" "Gene4" "Gene5"

Marker gene profiles (MGP)

###Sample data for MGP estimation

The package includes mouse brain cell type markers published in Mancarci et al. 2017 and gene expression data from substantia nigra samples from healthy donors and Parkinson’s disease patients by Lesnick et al. 2007.

Mouse marker genes is available in mouseMarkerGenes object as a nested list.

data(mouseMarkerGenes)
names(mouseMarkerGenes)
##  [1] "All"             "Amygdala"        "BasalForebrain" 
##  [4] "Brainstem"       "Cerebellum"      "Cerebrum"       
##  [7] "Cortex"          "Hippocampus"     "LocusCoeruleus" 
## [10] "Midbrain"        "SpinalCord"      "Striatum"       
## [13] "Subependymal"    "SubstantiaNigra" "Thalamus"
lapply(mouseMarkerGenes$Midbrain[1:3],head, 14)
## $Astrocyte
##  [1] "Aass"     "Acsbg1"   "Acsl6"    "Acss1"    "Add3"     "Adhfe1"  
##  [7] "AI464131" "Aldh1l1"  "Aldh6a1"  "Antxr1"   "Aox1"     "Apoe"    
## [13] "Aqp4"     "Axl"     
## 
## $BrainstemCholin
##  [1] "2310030G06Rik" "Anxa2"         "Cabp1"         "Calca"        
##  [5] "Calcb"         "Cd24a"         "Cd55"          "Cda"          
##  [9] "Chodl"         "Ecel1"         "Fxyd7"         "Hebp2"        
## [13] "Hspb1"         "Hspb8"        
## 
## $Dopaminergic
##  [1] "Cacna2d2" "Cadps2"   "Chrna6"   "Mapk8ip2" "Nr4a2"    "Ntn1"    
##  [7] "Prkcg"    "Rian"     "Scn2a"    "Slc6a3"   "Snhg11"   "Tenm1"   
## [13] "Th"       "Zim3"

Available Lesnick et al. data is stored in mgp_LesnickParkinsonsExp and mgp_LesnickParkinsonsMeta objects

library(dplyr)
## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:testthat':
## 
##     matches

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data(mgp_LesnickParkinsonsExp)
mgp_LesnickParkinsonsExp %>%
    dplyr::select(-GeneNames) %>%
    head %>% {.[,1:6]}
##           Probe Gene.Symbol   NCBIids GSM184354.cel GSM184355.cel
## 3366  1007_s_at        DDR1       780     10.236880      9.891552
## 44099   1053_at        RFC2      5982      5.421790      5.280541
## 14880    117_at HSPA7|HSPA6 3311|3310      5.164445      4.651754
## 11594    121_at        PAX8      7849      7.076004      7.035090
## 11907 1255_g_at      GUCA1A      2978      3.107388      3.418976
## 2069    1294_at        UBA7      7318      6.644858      6.182664
##       GSM184356.cel
## 3366      10.498371
## 44099      5.852467
## 14880      4.729189
## 11594      6.698765
## 11907      3.491832
## 2069       5.982642
data(mgp_LesnickParkinsonsMeta)
mgp_LesnickParkinsonsMeta %>% head
##         GSM disease
## 1 GSM184354 Control
## 2 GSM184355 Control
## 3 GSM184356 Control
## 4 GSM184357 Control
## 5 GSM184358 Control
## 6 GSM184359 Control

Before MGP estimation, it is important to filter expression data of low expressed genes and make sure all genes are represented only once in the dataset. While there are many probeset summarization and methods, for this work we chose the most variable probeset and remove all probes with a median expression below the median expression of the dataset.

unfilteredParkinsonsExp = mgp_LesnickParkinsonsExp # keep this for later
medExp = mgp_LesnickParkinsonsExp %>% 
    sepExpr() %>% {.[[2]]} %>%
    unlist %>% median

# mostVariable function is part of this package that does probe selection and filtering for you
mgp_LesnickParkinsonsExp = mostVariable(mgp_LesnickParkinsonsExp,
                                        threshold = medExp, 
                                        threshFun= median)

MGP estimation

MGPs are simply the first principal component of marker gene expression. This method of marker gene profile estimation is similar to the methodology of multiple previous works that aim to estimate relative abundance of cell types in a whole tissue sample (Chikina et al., 2015; Westra et al., 2015; Xu et al., 2013).

Primary function that deals with MGP estimation is mgpEstimate. This function will take in expression data and marker gene lists to return MGPs. exprData is the expression matrix which should include gene identifiers as a column. Other columns can be present at the beginning of the data frame but should not be of class double. genes is the list of markers. It is assumed to be a list of character vectors, each vector containing gene names for a cell type. geneColName is the name of the column where gene names are found. geneTransform is a function that will be applied to the gene names provided in genes. Note that this by default tranforms mouse gene names to human gene names. Set it to NULL if this is not desired.

Below a basic estimation performed with other important variables briefly explained.

estimations =  mgpEstimate(exprData=mgp_LesnickParkinsonsExp,
                           genes=mouseMarkerGenes$Midbrain,
                           geneColName='Gene.Symbol',
                           outlierSampleRemove=F, # should outlier samples removed. This is done using boxplot stats.
                           geneTransform =function(x){homologene::mouse2human(x)$humanGene}, # this is the default option for geneTransform
                           groups=mgp_LesnickParkinsonsMeta$disease, #if there are experimental groups provide them here. if not desired set to NULL
                           seekConsensus = FALSE, # ensures gene rotations are positive in both of the groups
                           removeMinority = TRUE) # removes genes if they are the minority in terms of rotation sign from estimation process

Dopamine rgic cell loss is a known effect of Parkinson’s Disease. To see if this effect can be observed we can look at dopaminergic MGPs in healthy donors vs Parkinson’s Disease patients

library(ggplot2)

ls(estimations)
##  [1] "allMarkerExpression"      "estimates"               
##  [3] "fullPCAs"                 "groups"                  
##  [5] "meanUsedMarkerExpression" "removedMarkerRatios"     
##  [7] "rotations"                "simpleScaledEstimation"  
##  [9] "trimmedPCAs"              "usedMarkerExpression"
ls(estimations$estimates)
## [1] "Astrocyte"              "BrainstemCholin"       
## [3] "Dopaminergic"           "Microglia"             
## [5] "Microglia_activation"   "Microglia_deactivation"
## [7] "Oligo"                  "Serotonergic"
dopaminergicFrame =
    data.frame(`Dopaminergic MGP` = estimations$estimates$Dopaminergic, 
               state = estimations$groups$Dopaminergic, # note that unless outlierSampleRemove is TRUE this will be always the same as the groups input
               check.names=FALSE)

ggplot2::ggplot(dopaminergicFrame, 
                aes(x = state, y = `Dopaminergic MGP`)) + 
    geom_boxvio() + geom_jitter(width = .05) # this is just a convenience function that outputs a list of ggplot elements.

To see if the difference between the two groups is statistically significant we use wilcoxon test (Mann-Whitney test). Wilcoxon-test is a non parametric tests that does not make assumptions about the distribution of the data. We chose to use it because marker gene profiles are not normally distributed.

group1 = estimations$estimates$Dopaminergic[estimations$groups$Dopaminergic %in% "Control"]
group2 = estimations$estimates$Dopaminergic[estimations$groups$Dopaminergic %in% "PD"]
wilcox.test(group1,group2)
## 
##  Wilcoxon rank sum test
## 
## data:  group1 and group2
## W = 119, p-value = 0.006547
## alternative hypothesis: true location shift is not equal to 0

Based on these results we can say that there is indeed a significant difference between doparminergic marker gene profiles between control and parkinson’s disease patients.

This difference can be also observed by looking at gene expression of markers

estimations$usedMarkerExpression$Dopaminergic%>%
    as.matrix %>%
    gplots::heatmap.2(trace = 'none',
                      scale='row',Rowv = FALSE,Colv = FALSE, dendrogram = 'none',
                      col= viridis::viridis(10),cexRow = 1.5, cexCol = 1,
                      ColSideColors = estimations$groups$Dopaminergic %>% 
                          toColor(palette = c('Control' = 'blue',
                                                     "PD" = "red")) %$% cols ,
                      margins = c(7,8))

Indeed in general most marker genes have a high expression is control samples which are shown in green.

It is important to note that not all marker genes are used in the estimation of marker gene profiles. Below we see all genes that are identified as dopaminergic cell markers with human orthologues. Yet not all these genes appear in the heatmap above

mouseHumanGeneTable = mouseMarkerGenes$Midbrain$Dopaminergic %>% homologene::mouse2human()
allHumanDopaGenes = mouseHumanGeneTable %$% humanGene
mouseHumanGeneTable
##    mouseGene humanGene mouseID humanID
## 1   Cacna2d2  CACNA2D2   56808    9254
## 2     Cadps2    CADPS2  320405   93664
## 3     Chrna6    CHRNA6   11440    8973
## 4   Mapk8ip2  MAPK8IP2   60597   23542
## 5      Nr4a2     NR4A2   18227    4929
## 6       Ntn1      NTN1   18208    9423
## 7      Prkcg     PRKCG   18752    5582
## 8     Slc6a3    SLC6A3   13162    6531
## 9      Tenm1     TENM1   23963   10178
## 10        Th        TH   21823    7054

Some of these genes are removed because they are not included in our dataset, either because they are not in the platform, or because the gene was filtered in the pre-processing stage due to low expression.

allHumanDopaGenes[!allHumanDopaGenes %in% mgp_LesnickParkinsonsExp$Gene.Symbol]
## [1] "CHRNA6"
allGenes = allHumanDopaGenes[allHumanDopaGenes %in% mgp_LesnickParkinsonsExp$Gene.Symbol]

Other genes can be removed because mgpEstimate thinks they don’t correlate well with the rest of the genes. Details of this process can be found in Mancarci et al. 2017 manuscript

allGenes[!allGenes %in% rownames(estimations$usedMarkerExpression$Dopaminergic)]
## [1] "PRKCG"
genesUsed =  rownames(estimations$usedMarkerExpression$Dopaminergic)

By looking at the unfiltered expression data, we can see how these two genes were unsuitable for MGP estimation

toPlot = 
    unfilteredParkinsonsExp[unfilteredParkinsonsExp$Gene.Symbol %in%
                                     homologene::mouse2human(mouseMarkerGenes$Midbrain$Dopaminergic)$humanGene,] %>%
    mostVariable(threshold = 0) 

toPlot %>%
    mostVariable(threshold = 0) %>% 
    sepExpr() %>%
    {.[[2]]} %>% as.matrix() %>% 
    apply(1,function(x){scale01(x)}) %>%
    t %>%
    {rownames(.) = 
        toPlot$Gene.Symbol[toPlot$Gene.Symbol %in% 
                               homologene::mouse2human(mouseMarkerGenes$Midbrain$Dopaminergic)$humanGene];.} %>%
    reshape2::melt() %>% 
    {colnames(.) = c('Gene','Sample','Expression');.} %>% 
    dplyr::mutate(`Is used?` = rep('used',length(Gene)) %>%
                      {.[Gene %in% 'CHRNA6'] = '(CHRNA6) not expressed';.[Gene %in% 'PRKCG'] = '(PRKCG) not correlated';.}) %>% 
    ggplot(aes(y = Expression, x = Sample, group = Gene, color = `Is used?`)) + 
    geom_line() + 
    cowplot::theme_cowplot() +
    theme( axis.text.x= element_blank(),
           axis.title = element_text(size = 20),
           legend.text = element_text(size = 17),
           legend.title = element_text(size=22),
           plot.title = element_text(size = 20)
           ) + 
    ggtitle('Scaled expression of markers')

toPlot %>%
    mostVariable(threshold = 0) %>% 
    sepExpr() %>%
    {.[[2]]} %>% as.matrix()%>%
    {rownames(.) = 
        toPlot$Gene.Symbol[toPlot$Gene.Symbol %in% 
                               homologene::mouse2human(mouseMarkerGenes$Midbrain$Dopaminergic)$humanGene];.} %>%
    reshape2::melt() %>% 
    {colnames(.) = c('Gene','Sample','Expression');.} %>% 
    dplyr::mutate(`Is used?` = rep('used',length(Gene)) %>%
                      {.[Gene %in% 'CHRNA6'] = 'CHRNA6 - not expressed';.[Gene %in% 'PRKCG'] = 'PRKCG - not correlated';.}) %>% 
    ggplot(aes(y = Expression, x = Sample, group = Gene, color = `Is used?`)) + 
    geom_line() + 
    cowplot::theme_cowplot() +
       theme( axis.text.x= element_blank(),
           axis.title = element_text(size = 20),
           legend.text = element_text(size = 17),
           legend.title = element_text(size=22),
           plot.title = element_text(size = 20)
           )+ 
    ggtitle('Nonscaled expression of markers')

Both of these genes seem to be non-expressed though PRKCG managed to be just above the removal threshold. Luckily lack of correlation reveals that it is not behaving as other marker genes.

The ratio of genesUsed and allGenes (all markers available in the study) can be used as a confidence metric. If a significant portion of the genes do not correlate well with each other that may point to presence of non cell type specific signal (regulation or noise). For all cell types this ratio is outputted. You’ll see a warning if this ratio ever exceeds 0.4

estimations$removedMarkerRatios
##              Astrocyte        BrainstemCholin           Dopaminergic 
##             0.12264151             0.35294118             0.11111111 
##              Microglia   Microglia_activation Microglia_deactivation 
##             0.09734513             0.08333333             0.12149533 
##                  Oligo           Serotonergic 
##             0.05882353             0.00000000

The ratio for dopaminergic cells is fairly low. We can also look at the amount of varience explained in the first PC of marker gene expression. If this value is low, it can point to higher amounts of confounding noise or regulation.

estimations$trimmedPCAs$Dopaminergic %>% summary()
## Importance of components:
##                           PC1    PC2     PC3     PC4     PC5     PC6
## Standard deviation     2.3056 1.0829 0.87149 0.53287 0.45796 0.38075
## Proportion of Variance 0.6645 0.1466 0.09494 0.03549 0.02622 0.01812
## Cumulative Proportion  0.6645 0.8110 0.90597 0.94147 0.96768 0.98580
##                            PC7     PC8
## Standard deviation     0.28376 0.18177
## Proportion of Variance 0.01006 0.00413
## Cumulative Proportion  0.99587 1.00000

For instance unlike dopaminergic cells, cholinergic cells seem to have a higher proportion of their marker genes removed. Looking at the variation explained by first PC reveals that first PC only explains 28% of all variance.

estimations$trimmedPCAs$BrainstemCholin %>% summary()
## Importance of components:
##                           PC1    PC2    PC3    PC4    PC5     PC6     PC7
## Standard deviation     1.7194 1.4430 1.2543 1.1992 0.8893 0.82035 0.77928
## Proportion of Variance 0.2688 0.1893 0.1430 0.1307 0.0719 0.06118 0.05521
## Cumulative Proportion  0.2688 0.4581 0.6011 0.7318 0.8037 0.86491 0.92012
##                            PC8     PC9    PC10    PC11
## Standard deviation     0.59534 0.48891 0.44505 0.29527
## Proportion of Variance 0.03222 0.02173 0.01801 0.00793
## Cumulative Proportion  0.95234 0.97407 0.99207 1.00000

Such a result can occur if

  • Marker genes are highly regulated.
  • There is little difference in cell type proportions or wholescale regulation of marker genes and MGP is driven by noise and other confounds.

Note about homologene

In this tutorial homologene database is used through homologene package. Note that the default version of this database is somewhat old which means some annotations may have been changed. The homologene package also includes an updated version of the homologene package to make sure you get all the correct matches.

If we look at dopaminergic cell markers for instance we could have gotten an extra gene (SCN2A) if we used the updated version.

homologene::mouse2human(mouseMarkerGenes$Midbrain$Dopaminergic) %$% humanGene
##  [1] "CACNA2D2" "CADPS2"   "CHRNA6"   "MAPK8IP2" "NR4A2"    "NTN1"    
##  [7] "PRKCG"    "SLC6A3"   "TENM1"    "TH"
homologene::mouse2human(mouseMarkerGenes$Midbrain$Dopaminergic,
                        db = homologene::homologeneData2) %$% humanGene
##  [1] "CACNA2D2" "CADPS2"   "CHRNA6"   "MAPK8IP2" "NR4A2"    "NTN1"    
##  [7] "PRKCG"    "SCN2A"    "SLC6A3"   "TENM1"    "TH"
You can’t perform that action at this time.