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GSEA : Gene Set Enrichment Analysis

Enrichment analyses GSEA are based on ranking the genes. In order to take into account both the direction of the deregulation and its significance we order the genes according to :

\[ x = sign(avg.log2FC) \times -log10(pval) \]

Knowing that the sign function in \(R\) returns the sign of the average log2(FC). That is, when the genes are under expressed the value returned by the function is -1, +1 when the FC is positive and 0 when the gene is not deregulated.

Taking into account the significance of the deregulation sometimes leads to complications. Indeed, it happens that the function FindAllMarkers returns p-values so low that they become zero, which produces Inf values that cannot be processed by GSEA. To overcome this problem and only for this case, we replace \(-log10(pval)\) by \(-log10(1e-323)\) because 1e-323 would be the smallest value that could be represented by a computer.

print(1e-323) ## equal 9.881313e-324

[1] 9.881313e-324

print(1e-324) ## equal 0

[1] 0

GSEA will calculate an enrichment score for each gene set analysed from this vector containing genes ordered according to their significance.

For each signature (and for each cluster), GSEA will calculate the enrichment score by running the vector of ordered genes, increasing the score if it encounters a gene from the gene set being analysed and decreasing it if it encounters a gene not in the gene set. The enrichment score (ES) is the maximum value during the increment.

The results of the GSEA function will be a dataframe with the following columns:

  • ID : Identifier of the gene group being analysed
  • setSize : Size of the gene group
  • EnrichmentScore (ES): Enrichment score representing the degree of presence of the gene set in the ordered list of genes
  • NES (Normalized Enrichment Score) : Normalized Enrichment Score such that : \(NES = \frac{actual ES}{mean(ESs Against All Permutations Of The Dataset)}\)
  • p-value : p-value of the enrichment test
  • p.adjust : adjusted p-value of the Benjamini Hochberg test
  • qvalue : q-value after FDR (False Discovery Rate) control
  • rank : Position in the list of genes for which ES is reached
  • leading_edge : Three statistics calculated during the analysis:
    • Tags : Percentage of genes in the gene set before or after the ES peak depending on whether it is positive or negative
    • List : Percentage of genes before or after the ES peak that are positive or negative
    • Signal : Strength of the enrichment signal calculated: \((Tag)(1 - List)(\displaystyle \frac{N}{N - Nh})\)
  • cluster : Name of the cluster for which the gene set has been identified as significant

The format of the gene sets used for the GSEA function must be a mapping table (TERM2GENE) which associates the name of a signature with the genes which compose it. However, this is not like the gmt format where one row corresponds to one signature, here there is one row for each possible (signature/gene) pair. We will use the gene sets from the MSigDB database where we will focus on the molecular signatures of cell types (Category C8) and CellMarker another database which provides us with lists of specific genes for cell types.

The results will be discussed once we have completed the analysis for both databases.

Molecular Signature Database (MSigDB)

The Broad Institute's MSigDB database contains several collections of gene signatures:

  • H or HallMarks gene sets: A set of gene sets that co-express in identified biological processes or states with respect to other collections
  • C1 or Positional gene sets : A set of gene sets based on their cytogenetic and chromosomal position
  • C2 or Curated gene sets : A set of gene sets found in databases and in the scientific literature
    • Biocarta
    • KEGG
    • PID
    • Reactome
    • WikiPathways
  • C3 or Regulatory target gene sets : Set of potential microRNA target genes (MIR) or transcription factors (TFT)
    • MIR: miRDB prediction
    • TFT: prediction based on the work of Kolmykov et al. 2021 and Xie et al. 2005
  • C4 or Computational gene sets : Gene set based on two rather cancer-oriented microarray papers (Subramanian, Tamayo et al. 2005 and Segal et al. 2004) that generated over 800 gene sets.
  • C5 or Ontology gene sets : Gene sets based on ontology databases.
    • Gene Ontology (GO): MF, CC, BP
    • Human Phenotype Ontology (HPO)
  • C6 or Oncogenic signature gene sets : A set of genes based on microarray results mainly concerning pathways that are often deregulated in cancer
  • C7 or Immunologic signature gene sets : Gene sets based on databases of the immune system and its possible perturbations.
    • ImmuneSigDB (human + mouse)
    • VAX: cured by the Human Immunology Project Consortium (HIPC)
  • C8 or Cell type signature gene sets : A set of gene sets corresponding to cell type markers defined mainly in single cell analysis

The R package msigdbr allows to query the database directly. With the msigdbr function we select the C8 collection in order to obtain the gene sets corresponding to human cell lines.

We will then apply the GSEA function to each cluster using a lapply. For each cluster :

  • We filter the dataframe of the unfiltered markers result to get only the lines concerning the genes deregulated by the cells of the cluster.
  • Run the GSEA function to get the enriched signatures in the marker gene set
  • Add the cluster name in a new column to the resulting dataframe
  • We visualize the results with the function gseaplot2 of the package enrichR which allows to visualize the first 3 signatures
## Retrieve MSigDB Database for human cell types signatures gene sets
C8_t2g <- msigdbr(species = "Homo sapiens", category = "C8") %>%
  dplyr::select(gs_name, ensembl_gene)

  kable(head(C8_t2g, 10))
gs_name ensembl_gene
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000123146
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000205336
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000106948
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000132965
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000244509
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000239713
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000163219
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000186517
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000180448
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 ENSG00000123329 
## Apply GSEA for each cluster
GSEA_list <- lapply(levels(pbmc_markers_annotated$cluster), function(cluster_name){

  res_markers <- subset(pbmc_markers_annotated, cluster == cluster_name)                   #Filter markers dataframe by cluster

  ## Generate named vector of ranked gene mandatory for GSEA analysis that take into account DE and significativity
  geneList_byclus <- sign(res_markers$avg_log2FC) * -log10(ifelse(res_markers$p_val == 0,  #Deal with pval = 0
                                                                  1e-323,                  #Smallest interpretable number
                                                                  res_markers$p_val))
  names(geneList_byclus) <- res_markers$gene

  ## Order by avg log FC and significativity
  geneList_byclus <- sort(geneList_byclus, decreasing = TRUE)

  ## Perform GSEA analysis
  gseaC8 <- GSEA(geneList_byclus, TERM2GENE = C8_t2g)
  gseaC8@result$cluster <- cluster_name #add cluster name as column

  ## Add plot
  # print(ridgeplot(gseaC8,
  #                 showCategory = 3,
  #                 orderBy = "NES") +
  #         ggtitle(paste("Cluster", cluster_name)) +
  #         theme(axis.text.y = element_text(size = 10),
  #               legend.key.size = unit(0.2, 'cm')))

  print(gseaplot2(gseaC8,
                  geneSetID = rownames(gseaC8@result %>%
                                         arrange(desc(NES)))[1:ifelse(nrow(gseaC8) < 3,
                                                                      nrow(gseaC8),
                                                                      3)],
                  base_size = 8,
                  pvalue_table = TRUE,
                  subplots = 1:2,
                  title = paste("Cluster", cluster_name)))

  return(gseaC8@result) #Return dataframe result
})


## Concatenate all results in one dataframe
GSEA_res <- do.call("rbind", GSEA_list)

## Group result by cluster (easier to manipulate with dplyr)
GSEA_res <- GSEA_res %>%
  group_by(cluster)

## Visualize first 3 signatures for each cluster (removing the vector of genes just for the visualization and the description that match ID column for this dataset MSigDB)
kable(top_n(x= GSEA_res, n = 3, wt = NES)[, -c(2,11)])
First Enriched MSigDB gene sets for each cluster
ID setSize enrichmentScore NES pvalue p.adjust qvalues rank leading_edge cluster
HAY_BONE_MARROW_NAIVE_T_CELL 208 0.9209917 3.046758 0.0e+00 0.0000000 0.0000000 216 tags=76%, list=17%, signal=75% 0
RUBENSTEIN_SKELETAL_MUSCLE_T_CELLS 153 0.8553262 2.739346 0.0e+00 0.0000000 0.0000000 146 tags=67%, list=11%, signal=67% 0
TRAVAGLINI_LUNG_CD4_NAIVE_T_CELL 116 0.9155134 2.824719 0.0e+00 0.0000000 0.0000000 135 tags=75%, list=11%, signal=74% 0
AIZARANI_LIVER_C23_KUPFFER_CELLS_3 142 0.8455834 2.003393 0.0e+00 0.0000000 0.0000000 257 tags=68%, list=16%, signal=62% 1
DURANTE_ADULT_OLFACTORY_NEUROEPITHELIUM_DENDRITIC_CELLS 101 0.8823161 2.065142 0.0e+00 0.0000000 0.0000000 192 tags=72%, list=12%, signal=68% 1
TRAVAGLINI_LUNG_CLASSICAL_MONOCYTE_CELL 168 0.8365088 1.997979 0.0e+00 0.0000000 0.0000000 265 tags=69%, list=16%, signal=64% 1
HAY_BONE_MARROW_NAIVE_T_CELL 230 0.7672855 3.015408 0.0e+00 0.0000000 0.0000000 260 tags=67%, list=25%, signal=64% 2
RUBENSTEIN_SKELETAL_MUSCLE_T_CELLS 145 0.7263295 2.725064 0.0e+00 0.0000000 0.0000000 192 tags=58%, list=19%, signal=55% 2
TRAVAGLINI_LUNG_CD4_NAIVE_T_CELL 107 0.7627843 2.748389 0.0e+00 0.0000000 0.0000000 180 tags=64%, list=17%, signal=59% 2
AIZARANI_LIVER_C34_MHC_II_POS_B_CELLS 83 0.9034082 2.063760 0.0e+00 0.0000000 0.0000000 152 tags=80%, list=13%, signal=75% 3
FAN_EMBRYONIC_CTX_BRAIN_B_CELL 70 0.9140261 2.070592 0.0e+00 0.0000000 0.0000000 103 tags=66%, list=9%, signal=64% 3
TRAVAGLINI_LUNG_B_CELL 112 0.9092172 2.119503 0.0e+00 0.0000000 0.0000000 121 tags=68%, list=10%, signal=67% 3
AIZARANI_LIVER_C1_NK_NKT_CELLS_1 70 0.8838737 2.284841 0.0e+00 0.0000000 0.0000000 90 tags=64%, list=13%, signal=62% 4
HAY_BONE_MARROW_NK_CELLS 105 0.8450920 2.260853 0.0e+00 0.0000000 0.0000000 132 tags=68%, list=18%, signal=65% 4
TRAVAGLINI_LUNG_CD8_NAIVE_T_CELL 92 0.8842939 2.338596 0.0e+00 0.0000000 0.0000000 90 tags=62%, list=13%, signal=62% 4
AIZARANI_LIVER_C31_KUPFFER_CELLS_5 53 0.8510682 1.948702 0.0e+00 0.0000000 0.0000000 191 tags=68%, list=10%, signal=63% 5
HAY_BONE_MARROW_MONOCYTE 191 0.8332887 2.001325 0.0e+00 0.0000000 0.0000000 295 tags=73%, list=16%, signal=68% 5
TRAVAGLINI_LUNG_NONCLASSICAL_MONOCYTE_CELL 163 0.8581938 2.050371 0.0e+00 0.0000000 0.0000000 301 tags=81%, list=16%, signal=75% 5
DURANTE_ADULT_OLFACTORY_NEUROEPITHELIUM_NK_CELLS 72 0.8853806 2.022495 0.0e+00 0.0000000 0.0000000 131 tags=71%, list=13%, signal=66% 6
HAY_BONE_MARROW_NK_CELLS 236 0.8420024 1.990762 0.0e+00 0.0000000 0.0000000 196 tags=56%, list=19%, signal=59% 6
TRAVAGLINI_LUNG_NATURAL_KILLER_CELL 108 0.8994932 2.104728 0.0e+00 0.0000000 0.0000000 124 tags=69%, list=12%, signal=67% 6
HAY_BONE_MARROW_DENDRITIC_CELL 96 0.8483908 2.413478 0.0e+00 0.0000000 0.0000000 99 tags=49%, list=6%, signal=49% 7
DESCARTES_FETAL_LUNG_MYELOID_CELLS 62 0.8072714 2.240695 0.0e+00 0.0000000 0.0000000 145 tags=55%, list=8%, signal=52% 7
TRAVAGLINI_LUNG_PLASMACYTOID_DENDRITIC_CELL 41 0.8240860 2.252436 8.0e-07 0.0000107 0.0000073 128 tags=51%, list=7%, signal=49% 7
DESCARTES_FETAL_LIVER_MEGAKARYOCYTES 55 0.8328324 1.392413 2.1e-06 0.0000795 0.0000704 83 tags=56%, list=13%, signal=54% 8
DESCARTES_FETAL_ADRENAL_MEGAKARYOCYTES 43 0.8505604 1.416959 4.6e-06 0.0001235 0.0001092 74 tags=63%, list=11%, signal=60% 8
DESCARTES_FETAL_KIDNEY_MEGAKARYOCYTES 32 0.8561275 1.417434 5.7e-05 0.0010710 0.0009475 69 tags=66%, list=11%, signal=62% 8

CellMarkers

CellMarker is a hand-curated database of scientific literature and other resources to describe over 400 cell types (human and mouse only). Human cell markers will be retrieved directly from the Cell Marker site.

After manipulating the data to format a two column dataframe where the first column is the term name and the second column is the gene name associated with that term.

We will then apply the GSEA function for each of the clusters using a lapply. For each cluster :

  • We filter the unfiltered marker result dataframe to retrieve only the rows concerning the genes deregulated by the cluster cells.
  • Run the GSEA function to get the enriched signatures in the marker gene set
  • Add the cluster name in a new column to the resulting dataframe
  • We visualize the results with the function gseaplot2 of the package enrichR which allows to visualize the first 5 signatures
## Retrieve Cell Markers Database for human cell types signatures gene sets
cell_marker_data <- vroom::vroom('http://xteam.xbio.top/CellMarker/download/Human_cell_markers.txt')

## Instead of `cellName`, users can use other features (e.g. `cancerType`)
cells <- cell_marker_data %>%                                           
    dplyr::select(cellName, geneSymbol) %>%                            #Select only the two columns
    dplyr::mutate(geneSymbol = strsplit(geneSymbol, ', ')) %>%         #Split gene names based on the comma
    tidyr::unnest()                                                    #Flatten gene vector in order to have a line for each gene in terme

## Remove [ and ] found in gene names due to the Cell Marker annotation
cells$geneSymbol <- gsub("\\[|\\]",
                         "",
                         cells$geneSymbol,
                         fixed = FALSE)

kable(head(cell_marker_data, 10))
speciesType tissueType UberonOntologyID cancerType cellType cellName CellOntologyID cellMarker geneSymbol geneID proteinName proteinID markerResource PMID Company
Human Kidney UBERON_0002113 Normal Normal cell Proximal tubular cell NA Intestinal Alkaline Phosphatase ALPI 248 PPBI P09923 Experiment 9263997 NA
Human Liver UBERON_0002107 Normal Normal cell Ito cell (hepatic stellate cell) CL_0000632 Synaptophysin SYP 6855 SYPH P08247 Experiment 10595912 NA
Human Endometrium UBERON_0001295 Normal Normal cell Trophoblast cell CL_0000351 CEACAM1 CEACAM1 634 CEAM1 P13688 Experiment 10751340 NA
Human Germ UBERON_0000923 Normal Normal cell Primordial germ cell CL_0000670 VASA DDX4 54514 DDX4 Q9NQI0 Experiment 10920202 NA
Human Corneal epithelium UBERON_0001772 Normal Normal cell Epithelial cell CL_0000066 KLF6 KLF6 1316 KLF6 Q99612 Experiment 12407152 NA
Human Placenta UBERON_0001987 Normal Normal cell Cytotrophoblast CL_0000351 FGF10 FGF10 2255 FGF10 O15520 Experiment 15950061 NA
Human Periosteum UBERON_0002515 Normal Normal cell Periosteum-derived progenitor cell NA CD166, CD45, CD9, CD90 ALCAM, PTPRC, CD9, THY1 214, 5788, 928, 7070 CD166, PTPRC, CD9, THY1 Q13740, P08575, P21926, P04216 Experiment 15977065 NA
Human Amniotic membrane UBERON_0009742 Normal Normal cell Amnion epithelial cell CL_0002536 NANOG, OCT¾ NANOG, POU5F1 79923, 5460 NANOG, PO5F1 Q9H9S0, Q01860 Experiment 16081662 NA
Human Primitive streak UBERON_0004341 Normal Normal cell Primitive streak cell NA LHX1, MIXL1 LHX1, MIXL1 3975, 83881 LHX1, MIXL1 P48742, Q9H2W2 Experiment 16258519 NA
Human Adipose tissue UBERON_0001013 Normal Normal cell Stromal vascular fraction cell CL_0000499 CD34 CD34 947 CD34 P28906 Experiment 16322640 NA 
## Apply GSEA for each cluster
GSEA_CM_list <- lapply(levels(pbmc_markers_annotated$cluster), function(cluster_name){

  res_markers <- subset(pbmc_markers_annotated, cluster == cluster_name)                     #Filter markers dataframe by cluster

  ## Generate named vector of ranked gene mandatory for GSEA analysis that take into account DE importance and significativity
  geneList_byclus <- sign(res_markers$avg_log2FC) * -log10(ifelse(res_markers$p_val == 0,    #Deal with pval = 0
                                                                  1e-323,                    #Smallest interpretable number
                                                                  res_markers$p_val))
  names(geneList_byclus) <- res_markers$external_gene_name

  ## Order by avg log FC and significativity
  geneList_byclus <- sort(geneList_byclus, decreasing = TRUE)

  ## Perform GSEA analysis
  gseaCM <- GSEA(geneList_byclus, TERM2GENE = cells)
  gseaCM@result$cluster <- cluster_name #add cluster name as column

  ## Add plot
  # print(ridgeplot(gseaCM, 
  #                 showCategory = 5, 
  #                 orderBy = "NES") +
  #         ggtitle(paste("Cluster", cluster_name)) +
  #         theme(axis.text.y = element_text(size = 10),
  #               legend.key.size = unit(0.2, 'cm')))

  print(gseaplot2(gseaCM, 
                  geneSetID = rownames(gseaCM@result %>% 
                                         arrange(desc(NES)))[1:ifelse(nrow(gseaCM) < 3, 
                                                                      nrow(gseaCM), 
                                                                      3)],
                  base_size = 8,
                  pvalue_table = TRUE, 
                  subplots = 1:2,
                  title = paste("Cluster", cluster_name)))

  return(gseaCM@result) #Return dataframe result
})


## Concatenate all results in one dataframe
GSEA_CM_res <- do.call("rbind", GSEA_CM_list)

## Group result by cluster (easier to manipulate with dplyr)
GSEA_CM_res <- GSEA_CM_res %>% 
  group_by(cluster)

## Visualise first 3 signatures for each cluster (removing the vector of genes just for the visualisation and the description that match ID column for this dataset MSigDB)
kable(top_n(x= GSEA_CM_res, n = 3, wt = NES)[, -c(2,11)])
First Enriched CellMarker gene sets for each cluster
ID setSize enrichmentScore NES pvalue p.adjust qvalues rank leading_edge cluster
Leydig precursor cell 44 0.8602093 2.256066 0.0000000 0.0000000 0.0000000 132 tags=70%, list=10%, signal=65% 0
Mitotic fetal germ cell 164 0.6500399 2.095496 0.0000000 0.0000000 0.0000000 135 tags=35%, list=11%, signal=36% 0
Naive CD8+ T cell 45 0.8009130 2.108249 0.0000000 0.0000003 0.0000002 212 tags=78%, list=17%, signal=67% 0
Monocyte 423 0.8131596 1.974827 0.0000000 0.0000000 0.0000000 332 tags=54%, list=21%, signal=58% 1
Paneth cell 119 0.7580558 1.785342 0.0000000 0.0000000 0.0000000 327 tags=61%, list=20%, signal=53% 1
Neutrophil 41 0.8413770 1.817697 0.0000002 0.0000036 0.0000025 172 tags=66%, list=11%, signal=60% 1
CD4+ T cell 14 0.8825202 2.213082 0.0000002 0.0000023 0.0000013 52 tags=64%, list=5%, signal=62% 2
T helper cell 12 0.8630525 2.103741 0.0000084 0.0000529 0.0000292 106 tags=75%, list=10%, signal=68% 2
Activated T cell 10 0.8687549 2.035541 0.0000261 0.0001498 0.0000826 101 tags=70%, list=10%, signal=64% 2
B cell 178 0.8779346 2.075372 0.0000000 0.0000000 0.0000000 167 tags=61%, list=14%, signal=61% 3
Secretory cell 18 0.9223015 1.811398 0.0000023 0.0000758 0.0000572 32 tags=61%, list=3%, signal=60% 3
Plasma cell 15 0.8797950 1.678179 0.0012256 0.0117304 0.0088462 78 tags=60%, list=7%, signal=57% 3
CD4+ cytotoxic T cell 37 0.8900805 2.090355 0.0000000 0.0000000 0.0000000 55 tags=59%, list=8%, signal=58% 4
Natural killer cell 30 0.8370655 1.896569 0.0000201 0.0001404 0.0000871 72 tags=60%, list=10%, signal=56% 4
CD8+ T cell 13 0.9231380 1.880140 0.0000652 0.0004054 0.0002515 40 tags=69%, list=6%, signal=67% 4
CD1C-CD141- dendritic cell 234 0.8010932 1.915014 0.0000000 0.0000000 0.0000000 314 tags=59%, list=17%, signal=56% 5
Monocyte 452 0.7167580 1.731494 0.0000000 0.0000000 0.0000000 427 tags=50%, list=23%, signal=51% 5
Paneth cell 143 0.7375016 1.742889 0.0000000 0.0000000 0.0000000 204 tags=41%, list=11%, signal=40% 5
CD4+ cytotoxic T cell 56 0.8789105 1.987043 0.0000000 0.0000000 0.0000000 116 tags=66%, list=11%, signal=62% 6
Effector CD8+ memory T (Tem) cell 48 0.8512421 1.908905 0.0000000 0.0000002 0.0000002 116 tags=54%, list=11%, signal=50% 6
Natural killer cell 41 0.8669408 1.918940 0.0000000 0.0000005 0.0000003 55 tags=49%, list=5%, signal=48% 6
CD1C+_A dendritic cell 16 0.9085712 2.285287 0.0000043 0.0001444 0.0001021 36 tags=44%, list=2%, signal=43% 7
Secretory cell 16 0.8755798 2.202305 0.0001511 0.0033737 0.0023852 79 tags=69%, list=4%, signal=66% 7
Specialist antigen presenting cell 18 0.8389319 2.138434 0.0004965 0.0066529 0.0047035 144 tags=50%, list=8%, signal=46% 7
Morula cell (Blastomere) 10 -0.7380276 -2.257087 0.0000162 0.0005186 0.0004606 76 tags=80%, list=12%, signal=72% 8

Analyzing enrichment results

The different GSEA plots represent the first three signatures for which we had the highest enrichment scores. The increment of the score can be followed on the top panel. The position of the signature genes is shown on the bottom panel. For each of the three signatures the p-value and the adjusted p-value are also shown in a table.

Many of the results confirm what was already observed with the over-representation analyses:

  • The cluster 0 would be composed of native T cells (either CD4+ for MSigDB or CD8+ for CellMarker)
  • cluster 1 would represent monocytes as well as cluster 5 which is close on the UMAP however the enrichment analyses cannot detect their difference, it would be necessary to investigate the entire ClusterProfiler results to differentiate them
  • cluster 2 would be composed of CD4+ T cells considered as native for MSigDB (again cluster 2 cells are very close on the UMAP to those of cluster 0)
  • cluster 3 is always related to B cells
  • cluster 4 and cluster 6 (very close on the UMAP) are still considered to be composed of NK cells. However, both databases also detected that cluster 4 could be composed of CD8+ T cells
  • cluster 7 cells would be dentritic cells.
  • cluster 8 would be composed of megakaryocytes which are defined as cells at the origin of platelet formation which confirms the previous results. However, CellMarker does not allow us to rule out this cluster since only one signature is significant and the enrichment of this one is negative.

Enrichment analyses are highly dependent on the signature whose enrichment or over-representation is being measured. If the signatures are too general or based on something too far from our dataset (here we were on blood cells) then it will be difficult to get results that make sense the first time. You will have to rely on your own signatures or marker genes to identify your population.