TP - Partie 2

Analyse de deux conditions

Author

C.Maugis-Rabusseau et V.Rocher

Published

June 13, 2025

library(Seurat)
library(ggplot2)
library(reshape2)
library(tidyverse)
library(DESeq2)
library(randomForest)
library(randomForestVIP)
library(ggvenn)
library(pheatmap)
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
source("Cours/FunctionsAux.R")

1 Présentation et création de l’objet Seurat

1.1 Présentation des données

  • Les données ifnb disponibles dans seurat-data

  • Données de cellules sanguines humaines PBMC (Peripheral Blood Mononuclear Cells) dans deux conditions (stimulées par interférons/ contrôle). Les deux conditions sont indiquées dans la colonne stim des metadata de l’objet SO.

1.2 Objet Seurat depuis seurat data

SeuratData::InstallData("ifnb")
ifnb <- SeuratData::LoadData("ifnb")
Assays(ifnb)
An object of class "SimpleAssays"
Slot "data":
List of length 1
Layers(ifnb) 
[1] "counts" "data"  
dim(ifnb)    
[1] 14053 13999
table(ifnb$orig.ident)

IMMUNE_CTRL IMMUNE_STIM 
       6548        7451 
table(ifnb$stim)

CTRL STIM 
6548 7451

Remarque :

  • Dans Seurat V5 : un seul objet Seurat contenant les deux conditions, organisé en plusieurs “layers”

  • Dans les versions antérieures, un objet Seurat par condition

On splitte selon les deux conditions (counts.CTRL et counts.STIM)

ifnb[["RNA"]] <- split(ifnb[["RNA"]], f = ifnb$stim)
ifnb
An object of class Seurat 
14053 features across 13999 samples within 1 assay 
Active assay: RNA (14053 features, 0 variable features)
 4 layers present: counts.CTRL, counts.STIM, data.CTRL, data.STIM
dim(ifnb[["RNA"]]$counts.CTRL)
[1] 14053  6548
dim(ifnb[["RNA"]]$counts.STIM)
[1] 14053  7451

Une annotation des cellules est stockée dans meta.data$seurat_annotations :

head(ifnb@meta.data)
                  orig.ident nCount_RNA nFeature_RNA stim seurat_annotations
AAACATACATTTCC.1 IMMUNE_CTRL       3017          877 CTRL          CD14 Mono
AAACATACCAGAAA.1 IMMUNE_CTRL       2481          713 CTRL          CD14 Mono
AAACATACCTCGCT.1 IMMUNE_CTRL       3420          850 CTRL          CD14 Mono
AAACATACCTGGTA.1 IMMUNE_CTRL       3156         1109 CTRL                pDC
AAACATACGATGAA.1 IMMUNE_CTRL       1868          634 CTRL       CD4 Memory T
AAACATACGGCATT.1 IMMUNE_CTRL       1581          557 CTRL          CD14 Mono

1.3 Construction depuis les données de séquençage (?ifnb)

  • Pour la récupération des données de séquençage, on a tout d’abord executé les commandes suivantes. Pour vous, elles sont déjà dans le dossier data.
cd data/
wget https://www.dropbox.com/s/79q6dttg8yl20zg/immune_alignment_expression_matrices.zip
unzip immune_alignment_expression_matrices.zip
rm immune_alignment_expression_matrices.zip
# Lecture des deux fichiers de données
ctrl.data <- data.frame(data.table::fread('data/immune_control_expression_matrix.txt.gz',sep = "\t"),row.names=1)
stim.data <- data.frame(data.table::fread('data/immune_stimulated_expression_matrix.txt.gz',sep = "\t"),row.names=1)
# Création de objet Seurat
ctrl <- Seurat::CreateSeuratObject(counts = ctrl.data, project = 'CTRL', min.cells = 5)
ctrl$stim <- 'CTRL'
ctrl <- subset(x = ctrl, subset = nFeature_RNA > 500)

stim <- Seurat::CreateSeuratObject(counts = stim.data, project = 'STIM', min.cells = 5)
stim$stim <- 'STIM'
stim <- subset(x = stim, subset = nFeature_RNA > 500)

ifnb2 <- merge(x = ctrl, y = stim)
Seurat::Project(object = ifnb2) <- 'ifnb'
# Ajout de l'information d'annotation des cellules
annotations <- readRDS(file = system.file('extdata/annotations/annotations.Rds', package = 'ifnb.SeuratData'))
ifnb2 <- Seurat::AddMetaData(object = ifnb2, metadata = annotations)
ifnb
An object of class Seurat 
14053 features across 13999 samples within 1 assay 
Active assay: RNA (14053 features, 0 variable features)
 4 layers present: counts.CTRL, counts.STIM, data.CTRL, data.STIM
ifnb2
An object of class Seurat 
14053 features across 13999 samples within 1 assay 
Active assay: RNA (14053 features, 0 variable features)
 2 layers present: counts.CTRL, counts.STIM
rm(ifnb2)

2 Normalization, réduction de dimension et clustering

2.1 Normalisation

  • Fait la normalisation comme vu précédemment pour chaque matrice de comptages (counts.CTRL –> data.CTRL et counts.STIM–> data.STIM)
ifnb <- NormalizeData(ifnb)
Normalizing layer: counts.CTRL
Normalizing layer: counts.STIM
ifnb@assays$RNA$data.CTRL[1:10,1:5]
10 x 5 sparse Matrix of class "dgCMatrix"
              AAACATACATTTCC.1 AAACATACCAGAAA.1 AAACATACCTCGCT.1
AL627309.1                   .                .         .       
RP11-206L10.2                .                .         .       
LINC00115                    .                .         .       
NOC2L                        .                .         .       
KLHL17                       .                .         .       
PLEKHN1                      .                .         .       
HES4                         .                .         .       
ISG15                        .                .         1.367106
AGRN                         .                .         .       
C1orf159                     .                .         .       
              AAACATACCTGGTA.1 AAACATACGATGAA.1
AL627309.1            .                       .
RP11-206L10.2         .                       .
LINC00115             .                       .
NOC2L                 .                       .
KLHL17                .                       .
PLEKHN1               .                       .
HES4                  .                       .
ISG15                 1.427573                .
AGRN                  .                       .
C1orf159              .                       .
ifnb@assays$RNA$data.STIM[1:10,1:5]
10 x 5 sparse Matrix of class "dgCMatrix"
              AAACATACCAAGCT.1 AAACATACCCCTAC.1 AAACATACCCGTAA.1
AL627309.1            .                .                .       
RP11-206L10.2         .                .                .       
LINC00115             .                .                .       
NOC2L                 .                .                .       
KLHL17                .                .                .       
PLEKHN1               .                .                .       
HES4                  .                .                .       
ISG15                 4.870775         4.982535         3.960974
AGRN                  .                .                .       
C1orf159              .                .                .       
              AAACATACCCTCGT.1 AAACATACGAGGTG.1
AL627309.1            .                .       
RP11-206L10.2         .                .       
LINC00115             .                .       
NOC2L                 .                .       
KLHL17                .                .       
PLEKHN1               .                .       
HES4                  .                .       
ISG15                 3.902855         4.053323
AGRN                  .                .       
C1orf159              .                .

2.2 HVG

  • HVG : il le fait pour chaque condition puis rend une répondre globale et un graphe global
ifnb <- FindVariableFeatures(ifnb)
Finding variable features for layer counts.CTRL
Finding variable features for layer counts.STIM
head(VariableFeatures(ifnb,assay="RNA",layer="counts.CTRL"))
[1] "HBB"    "HBA2"   "HBA1"   "CCL3"   "CCL4"   "CXCL10"
head(VariableFeatures(ifnb,assay="RNA",layer="counts.STIM"))
[1] "HBB"      "HBA2"     "HBA1"     "CCL4"     "APOBEC3B" "CCL7"    
top10<-head(VariableFeatures(ifnb), 10)
plot1<-VariableFeaturePlot(ifnb)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
When using repel, set xnudge and ynudge to 0 for optimal results
plot2
Warning in scale_x_log10(): log-10 transformation introduced infinite values.

2.3 Réduction de dimension sans intégration

  • Réduction de dimension sur la matrice des comptages normalisés (composée de deux blocs) centrés réduits
ifnb <- ScaleData(ifnb)
Centering and scaling data matrix
dim(ifnb@assays$RNA$scale.data)
[1]  2000 13999
ifnb <- RunPCA(ifnb)
PC_ 1 
Positive:  NPM1, CCR7, GIMAP7, LTB, CD7, SELL, CD2, TMSB4X, TRAT1, IL7R 
       IL32, RHOH, ITM2A, RGCC, LEF1, CD3G, ALOX5AP, CREM, NHP2, PASK 
       MYC, SNHG8, TSC22D3, GPR171, BIRC3, NOP58, RARRES3, CD27, SRM, CD8B 
Negative:  TYROBP, C15orf48, FCER1G, CST3, SOD2, ANXA5, FTL, TYMP, TIMP1, CD63 
       LGALS1, CTSB, S100A4, LGALS3, KYNU, PSAP, FCN1, NPC2, ANXA2, S100A11 
       IGSF6, LYZ, SPI1, APOBEC3A, CD68, CTSL, SDCBP, NINJ1, HLA-DRA, CCL2 
PC_ 2 
Positive:  ISG15, ISG20, IFIT3, IFIT1, LY6E, TNFSF10, IFIT2, MX1, IFI6, RSAD2 
       CXCL10, OAS1, CXCL11, IFITM3, MT2A, OASL, TNFSF13B, IDO1, IL1RN, APOBEC3A 
       GBP1, CCL8, HERC5, FAM26F, GBP4, HES4, WARS, VAMP5, DEFB1, XAF1 
Negative:  IL8, CLEC5A, CD14, VCAN, S100A8, IER3, MARCKSL1, IL1B, PID1, CD9 
       GPX1, PHLDA1, INSIG1, PLAUR, PPIF, THBS1, OSM, SLC7A11, GAPDH, CTB-61M7.2 
       LIMS1, S100A9, GAPT, CXCL3, ACTB, C19orf59, CEBPB, OLR1, MGST1, FTH1 
PC_ 3 
Positive:  HLA-DQA1, CD83, HLA-DQB1, CD74, HLA-DPA1, HLA-DRA, HLA-DRB1, HLA-DPB1, SYNGR2, IRF8 
       CD79A, MIR155HG, HERPUD1, REL, HLA-DMA, MS4A1, HSP90AB1, FABP5, TVP23A, ID3 
       CCL22, EBI3, TSPAN13, PMAIP1, TCF4, PRMT1, NME1, HSPE1, HSPD1, CD70 
Negative:  ANXA1, GIMAP7, TMSB4X, CD7, CD2, RARRES3, MT2A, IL32, GNLY, PRF1 
       NKG7, CCL5, TRAT1, RGCC, S100A9, KLRD1, CCL2, GZMH, GZMA, CD3G 
       S100A8, CTSW, CCL7, ITM2A, HPSE, FGFBP2, CTSL, GPR171, CCL8, OASL 
PC_ 4 
Positive:  CCR7, LTB, SELL, LEF1, IL7R, ADTRP, TRAT1, PASK, MYC, NPM1 
       SOCS3, TSC22D3, TSHZ2, HSP90AB1, SNHG8, GIMAP7, PIM2, HSPD1, CD3G, TXNIP 
       RHOH, GBP1, C12orf57, CA6, PNRC1, CMSS1, CD27, SESN3, NHP2, BIRC3 
Negative:  NKG7, GZMB, GNLY, CST7, CCL5, PRF1, CLIC3, KLRD1, GZMH, GZMA 
       APOBEC3G, CTSW, FGFBP2, KLRC1, FASLG, C1orf21, HOPX, CXCR3, SH2D1B, LINC00996 
       TNFRSF18, SPON2, RARRES3, GCHFR, SH2D2A, IGFBP7, ID2, C12orf75, XCL2, RAMP1 
PC_ 5 
Positive:  VMO1, FCGR3A, MS4A4A, CXCL16, MS4A7, PPM1N, HN1, LST1, SMPDL3A, ATP1B3 
       CASP5, CDKN1C, CH25H, AIF1, PLAC8, SERPINA1, LRRC25, CD86, HCAR3, GBP5 
       TMSB4X, RP11-290F20.3, RGS19, VNN2, ADA, LILRA5, STXBP2, C3AR1, PILRA, FCGR3B 
Negative:  CCL2, CCL7, CCL8, PLA2G7, LMNA, S100A9, SDS, TXN, CSTB, ATP6V1F 
       CCR1, EMP1, CAPG, CCR5, TPM4, IDO1, MGST1, HPSE, CTSB, LILRB4 
       RSAD2, HSPA1A, VIM, CCNA1, CTSL, GCLM, PDE4DIP, SGTB, SLC7A11, FABP5 
ifnb <- FindNeighbors(ifnb, dims = 1:30, reduction = "pca")
Computing nearest neighbor graph
Computing SNN
ifnb <- RunUMAP(ifnb, dims = 1:30, reduction = "pca", reduction.name = "umap.unintegrated")
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
12:36:27 UMAP embedding parameters a = 0.9922 b = 1.112
12:36:27 Read 13999 rows and found 30 numeric columns
12:36:27 Using Annoy for neighbor search, n_neighbors = 30
12:36:27 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:36:29 Writing NN index file to temp file /tmp/RtmpmRLL44/file38eb61c20997
12:36:29 Searching Annoy index using 1 thread, search_k = 3000
12:36:36 Annoy recall = 100%
12:36:37 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
12:36:40 Initializing from normalized Laplacian + noise (using RSpectra)
12:36:41 Commencing optimization for 200 epochs, with 614378 positive edges
12:36:41 Using rng type: pcg
12:36:51 Optimization finished
DimPlot(ifnb, reduction = "umap.unintegrated", group.by = c("stim"))

3 Intégration des données

3.1 Intégration avec CCA

  • Utilisation de la fonction IntegrateLayers() avec la méthode CCA = Canonical Correlation Analysis
ifnb_cca <- IntegrateLayers(object = ifnb, 
                        method = CCAIntegration, 
                        orig.reduction = "pca", 
                        new.reduction = "integrated.cca",
                        verbose = FALSE)
# re-join layers after integration
ifnb_cca[["RNA"]] <- JoinLayers(ifnb_cca[["RNA"]])
# Dimension reduction
ifnb_cca <- FindNeighbors(ifnb_cca, 
                      reduction = "integrated.cca", 
                      dims = 1:30)
Computing nearest neighbor graph
Computing SNN
ifnb_cca <- RunUMAP(ifnb_cca, 
                dims = 1:30, 
                reduction = "integrated.cca")
12:37:45 UMAP embedding parameters a = 0.9922 b = 1.112
12:37:45 Read 13999 rows and found 30 numeric columns
12:37:45 Using Annoy for neighbor search, n_neighbors = 30
12:37:45 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:37:47 Writing NN index file to temp file /tmp/RtmpmRLL44/file38eb6ce693fe
12:37:47 Searching Annoy index using 1 thread, search_k = 3000
12:37:56 Annoy recall = 100%
12:37:57 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
12:38:00 Initializing from normalized Laplacian + noise (using RSpectra)
12:38:00 Commencing optimization for 200 epochs, with 629246 positive edges
12:38:00 Using rng type: pcg
12:38:11 Optimization finished
p1<-DimPlot(ifnb, reduction = "umap.unintegrated", group.by = c("stim"))
p2<-DimPlot(ifnb_cca, reduction = "umap", group.by = c("stim"))
p1+p2

3.2 Intégration avec Harmony

ifnb_h <- IntegrateLayers(object = ifnb, 
                        method = HarmonyIntegration, 
                        orig.reduction = "pca", 
                        new.reduction = "integrated.harmony",
                        verbose = FALSE)
# re-join layers after integration
ifnb_h[["RNA"]] <- JoinLayers(ifnb_h[["RNA"]])
# Dimension reduction
ifnb_h <- FindNeighbors(ifnb_h, 
                      reduction = "integrated.harmony", 
                      dims = 1:30)
Computing nearest neighbor graph
Computing SNN
ifnb_h <- RunUMAP(ifnb_h, 
                dims = 1:30, 
                reduction = "integrated.harmony")
12:39:13 UMAP embedding parameters a = 0.9922 b = 1.112
12:39:13 Read 13999 rows and found 30 numeric columns
12:39:13 Using Annoy for neighbor search, n_neighbors = 30
12:39:13 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:39:15 Writing NN index file to temp file /tmp/RtmpmRLL44/file38eb77da4176
12:39:15 Searching Annoy index using 1 thread, search_k = 3000
12:39:23 Annoy recall = 100%
12:39:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
12:39:28 Initializing from normalized Laplacian + noise (using RSpectra)
12:39:28 Commencing optimization for 200 epochs, with 620048 positive edges
12:39:28 Using rng type: pcg
12:39:39 Optimization finished
p3<-DimPlot(ifnb_h, reduction = "umap", group.by = c("stim"))
p2+p3

Pour la suite, on poursuit avec l’intégration par CCA

ifnb<-ifnb_cca
rm(ifnb_h,ifnb_cca)

4 Clustering

4.1 Méthode de Louvain

  • Clustering sur les données intégrées avec la résolution \(r=0.5\)
#|message: false
#|warning: false

ifnb <- FindClusters(ifnb, resolution = 0.5)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 13999
Number of edges: 590558

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9021
Number of communities: 14
Elapsed time: 5 seconds
EffPlot(ifnb,clustname = "seurat_clusters",resolution = 0.5)

DimPlot(ifnb, 
        reduction = "umap", 
        group.by = c("stim", "seurat_clusters"))

DimPlot(ifnb, reduction = "umap", split.by = "stim")

Aux<-melt(table(Idents(ifnb),ifnb$stim))
colnames(Aux)<-c("Cluster","Condition","len")
ggplot(data=Aux, aes(x=Cluster, y=len, fill=Condition)) +
  geom_bar(stat="identity", position=position_dodge())+
  geom_text(aes(label=len), vjust=1.6, color="white",
            position = position_dodge(0.9), size=3.5)+
  theme_minimal()

4.2 Annotation des cellules

  • Une annotation des cellules est disponible
table(ifnb$seurat_annotations)

   CD14 Mono  CD4 Naive T CD4 Memory T    CD16 Mono            B        CD8 T 
        4362         2504         1762         1044          978          814 
 T activated           NK           DC  B Activated           Mk          pDC 
         633          619          472          388          236          132 
       Eryth 
          55 
DimPlot(ifnb, 
        reduction = "umap", 
        group.by = c("seurat_annotations"))

  • Comparaison avec le clustering obtenu
table(Idents(ifnb),ifnb$seurat_annotations)[c(1:3,5,4,6:10,12,11,14,13),c(1:5,10,6:9,11:13)]
    
     CD14 Mono CD4 Naive T CD4 Memory T CD16 Mono    B B Activated CD8 T
  0       4272           0            0        16    1           1     0
  1          0        2332           47         1    1           0     9
  2          5         132         1643         0    0           0    30
  4         28           0            0      1027    0           0     0
  3          0           0            0         0  975         387     1
  5          1           2           51         0    0           0   765
  6          0          38            8         0    0           0     1
  7          0           0            0         0    0           0     8
  8         48           0            0         0    0           0     0
  9          0           0           13         0    1           0     0
  11         3           0            0         0    0           0     0
  10         0           0            0         0    0           0     0
  13         0           0            0         0    0           0     0
  12         5           0            0         0    0           0     0
    
     T activated   NK   DC   Mk  pDC Eryth
  0            0    0    4    2    0     0
  1           20    0    0    6    0     0
  2           16    1    0    1    0     0
  4            0    0    0   13    0     0
  3           15    0    0    0    0     1
  5            7    9    0    0    0     0
  6          575    0    0    0    1     0
  7            0  608    0    0    0     0
  8            0    0  466    0    1     0
  9            0    1    0  175    0     0
  11           0    0    0    0    0    54
  10           0    0    2    0  106     0
  13           0    0    0    0   24     0
  12           0    0    0   39    0     0

5 Gènes marqueurs des “types cellulaires” conservés

  • Déterminer les gènes marqueurs des “types cellulaires” qui sont conservés entre les conditions

  • Utilisation de la fonction FindConservedMarkers()

  • Exemple : on se focalise sur le type cellulaire NK

Idents(ifnb) <- "seurat_annotations"
NK.markers <- FindConservedMarkers(ifnb, 
                                   ident.1 = "NK", 
                                   grouping.var = "stim", 
                                   verbose = FALSE)
head(NK.markers)
      CTRL_p_val CTRL_avg_log2FC CTRL_pct.1 CTRL_pct.2 CTRL_p_val_adj
GNLY           0        6.854586      0.943      0.046              0
NKG7           0        5.358881      0.953      0.085              0
GZMB           0        5.078135      0.839      0.044              0
CLIC3          0        5.765314      0.601      0.024              0
CTSW           0        5.307246      0.537      0.030              0
KLRD1          0        5.261553      0.507      0.019              0
      STIM_p_val STIM_avg_log2FC STIM_pct.1 STIM_pct.2 STIM_p_val_adj max_pval
GNLY           0        6.435910      0.956      0.059              0        0
NKG7           0        4.971397      0.950      0.081              0        0
GZMB           0        5.151924      0.897      0.060              0        0
CLIC3          0        5.505208      0.623      0.031              0        0
CTSW           0        5.240729      0.592      0.035              0        0
KLRD1          0        4.852457      0.555      0.027              0        0
      minimump_p_val
GNLY               0
NKG7               0
GZMB               0
CLIC3              0
CTSW               0
KLRD1              0
NK.markers<-NK.markers%>%
  arrange(minimump_p_val)

DotPlot(ifnb, features = rownames(NK.markers)[1:10], 
        cols = c("blue", "red"), dot.scale = 8, split.by = "stim") +
    RotatedAxis()

6 Analyse différentielle

6.1 Informations complémentaires

D’abord ajout d’informations complémentaires pour avoir des “réplicats”

# load the inferred sample IDs of each cell
ctrl <- read.table(url("https://raw.githubusercontent.com/yelabucsf/demuxlet_paper_code/master/fig3/ye1.ctrl.8.10.sm.best"), head = T, stringsAsFactors = F)
stim <- read.table(url("https://raw.githubusercontent.com/yelabucsf/demuxlet_paper_code/master/fig3/ye2.stim.8.10.sm.best"), head = T, stringsAsFactors = F)
info <- rbind(ctrl, stim)

# rename the cell IDs by substituting the '-' into '.'
info$BARCODE <- gsub(pattern = "\\-", replacement = "\\.", info$BARCODE)

# only keep the cells with high-confidence sample ID
info <- info[grep(pattern = "SNG", x = info$BEST), ]

# remove cells with duplicated IDs in both ctrl and stim groups
info <- info[!duplicated(info$BARCODE) & !duplicated(info$BARCODE, fromLast = T), ]

# now add the sample IDs to ifnb 
rownames(info) <- info$BARCODE
info <- info[, c("BEST"), drop = F]
names(info) <- c("donor_id")
ifnb <- AddMetaData(ifnb, metadata = info)

# remove cells without donor IDs
ifnb$donor_id[is.na(ifnb$donor_id)] <- "unknown"
ifnb <- subset(ifnb, subset = donor_id != "unknown")
table(ifnb$donor_id)

 SNG-101 SNG-1015 SNG-1016 SNG-1039  SNG-107 SNG-1244 SNG-1256 SNG-1488 
    1197     3116     1438      663      652     1998     2363     2241

Pour la suite, création d’une colonne celltype.stim combinant le type cellulaire et la condition.

ifnb$celltype.stim <- paste(ifnb$seurat_annotations, ifnb$stim, sep = "_")
table(ifnb$celltype.stim)

 B Activated_CTRL  B Activated_STIM            B_CTRL            B_STIM 
              176               198               403               554 
   CD14 Mono_CTRL    CD14 Mono_STIM    CD16 Mono_CTRL    CD16 Mono_STIM 
             2167              2086               494               521 
CD4 Memory T_CTRL CD4 Memory T_STIM  CD4 Naive T_CTRL  CD4 Naive T_STIM 
              849               876               960              1497 
       CD8 T_CTRL        CD8 T_STIM           DC_CTRL           DC_STIM 
              349               451               255               208 
       Eryth_CTRL        Eryth_STIM           Mk_CTRL           Mk_STIM 
               22                32               110               114 
          NK_CTRL           NK_STIM          pDC_CTRL          pDC_STIM 
              295               310                49                79 
 T activated_CTRL  T activated_STIM 
              291               322

Création d’une colonne donor.stim combinant le donneur et la condition

ifnb$donor.stim<-paste0(ifnb$donor_id,"_",ifnb$stim)

6.2 Exemple 1

Question 1: déterminer les gènes dont l’expression varie entre les deux conditions (types cellulaires confondus)

6.2.1 Par méthode naive (Wilcoxon)

Idents(ifnb)<-ifnb$stim
res1_Wilcox <- FindMarkers(object = ifnb, 
                           ident.1 = "STIM", 
                           ident.2 = "CTRL",
                           test.use = "wilcox")

sum(res1_Wilcox$p_val_adj<0.01)
[1] 2384
round(100*mean(res1_Wilcox$p_val_adj<0.01),2)
[1] 34.67
genetop_wilcox1<-res1_Wilcox %>%
   filter(p_val_adj<0.01)%>%
   arrange(desc(abs(avg_log2FC)))%>%
   top_n(200)
DotPlot(ifnb,features=rownames(genetop_wilcox1)[1:10],group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

DotPlot(ifnb,features=rownames(genetop_wilcox1)[1:10],group.by="celltype.stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

6.2.2 Par pseudo-bulk (DESeq2)

  • On regroupe par condition et donor_id pour la création des données de pseudoBulk
pseudo_ifnb1 <- AggregateExpression(ifnb, 
                                   assays = "RNA", 
                                   return.seurat = T, 
                                   group.by = c("stim", "donor_id"))
Centering and scaling data matrix
head(pseudo_ifnb1)
                 orig.ident stim donor_id
CTRL_SNG-101   CTRL_SNG-101 CTRL  SNG-101
CTRL_SNG-1015 CTRL_SNG-1015 CTRL SNG-1015
CTRL_SNG-1016 CTRL_SNG-1016 CTRL SNG-1016
CTRL_SNG-1039 CTRL_SNG-1039 CTRL SNG-1039
CTRL_SNG-107   CTRL_SNG-107 CTRL  SNG-107
CTRL_SNG-1244 CTRL_SNG-1244 CTRL SNG-1244
CTRL_SNG-1256 CTRL_SNG-1256 CTRL SNG-1256
CTRL_SNG-1488 CTRL_SNG-1488 CTRL SNG-1488
STIM_SNG-101   STIM_SNG-101 STIM  SNG-101
STIM_SNG-1015 STIM_SNG-1015 STIM SNG-1015
  • DESeq2 depuis Seurat
Idents(pseudo_ifnb1)<-"stim"
bulk.cond1 <- FindMarkers(object = pseudo_ifnb1, 
                         ident.1 = "STIM", 
                         ident.2 = "CTRL",
                         test.use = "DESeq2")
converting counts to integer mode
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
head(bulk.cond1)
               p_val avg_log2FC pct.1 pct.2     p_val_adj
NT5C3A 7.029232e-232   3.554826     1     1 9.878179e-228
IFI16  5.065325e-212   2.243370     1     1 7.118302e-208
ISG20  2.122045e-205   4.018798     1     1 2.982110e-201
DDX58  1.126106e-175   3.548703     1     1 1.582516e-171
RTP4   6.577415e-171   2.989407     1     1 9.243241e-167
TRIM22 1.565830e-162   2.864154     1     1 2.200461e-158
  • En passant par le package DESeq2
cts1<-as.matrix(pseudo_ifnb1@assays$RNA$counts)
coldata1<-pseudo_ifnb1@meta.data
dds1 <- DESeqDataSetFromMatrix(countData = cts1,
                              colData = coldata1,
                              design= ~ donor_id + stim) 
dds1 <- DESeq(dds1,fitType = c("local"))                      
resultsNames(dds1) # lists the coefficients
[1] "Intercept"                    "donor_id_SNG.1015_vs_SNG.101"
[3] "donor_id_SNG.1016_vs_SNG.101" "donor_id_SNG.1039_vs_SNG.101"
[5] "donor_id_SNG.107_vs_SNG.101"  "donor_id_SNG.1244_vs_SNG.101"
[7] "donor_id_SNG.1256_vs_SNG.101" "donor_id_SNG.1488_vs_SNG.101"
[9] "stim_STIM_vs_CTRL"           
resDEseq1<-results(dds1,name="stim_STIM_vs_CTRL")

round(100*length(which(resDEseq1$padj<0.01))/nrow(resDEseq1),2) 
[1] 17.47
genetopDEseq1<-as.data.frame(resDEseq1) %>%
   filter(padj<0.01)%>%
   arrange(desc(abs(log2FoldChange)))%>%
   top_n(200)
Selecting by padj
DotPlot(pseudo_ifnb1,features=rownames(genetopDEseq1)[1:10],group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

DotPlot(ifnb,features=rownames(genetopDEseq1)[1:10],group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

pheatmap(pseudo_ifnb1@assays$RNA$scale.data[rownames(genetopDEseq1),],
         cluster_cols = FALSE,       
         scale = "column",              
         show_rownames = FALSE,
         show_colnames = TRUE)

6.2.3 Par une méthode spécifique single-cell (ex: NEBULA)

library(nebula)
ifnb_neb1<-scToNeb(obj=ifnb,assay="RNA",id="donor_id",pred=c("stim","seurat_annotations"))
df1 = model.matrix(~stim+seurat_annotations, data=ifnb_neb1$pred)
data_g = group_cell(count=ifnb_neb1$count,id=ifnb_neb1$id,pred=df1) # il faut grouper les cellules par indiv avant de faire nebula
#re1 = nebula(data_g$count,data_g$id,pred=data_g$pred)
re1<-readRDS("Exercices/ResNebulaQ1.rds")

round(100*mean(p.adjust(re1$summary$p_stimSTIM,method="BH")<0.01),2)
[1] 49.1
ifnbNebSumm1<-re1$summary
ifnbNebSumm1$padjStim<-p.adjust(re1$summary$p_stimSTIM,method="BH")
ifnbNebSumm1<-ifnbNebSumm1%>%
            filter(padjStim <0.01)%>%
            arrange(desc(abs(logFC_stimSTIM)))  
head(ifnbNebSumm1)
  logFC_(Intercept) logFC_stimSTIM logFC_seurat_annotationsCD4 Naive T
1        -3.0231869       6.387177                           -5.955392
2        -3.4110331       5.913719                           -5.838174
3        -0.6599773       5.293059                           -4.835198
4        -5.8264308       5.277627                           -2.160834
5        -6.5605195       5.195983                           -5.509365
6        -8.9263424       4.943081                           -5.356895
  logFC_seurat_annotationsCD4 Memory T logFC_seurat_annotationsCD16 Mono
1                           -5.5038664                       -2.43210273
2                           -5.4847981                       -0.18167624
3                           -4.6971026                        1.39648553
4                           -0.4273964                        0.34525472
5                           -4.7460927                       -0.02139147
6                           -5.5182972                       -2.57423478
  logFC_seurat_annotationsB logFC_seurat_annotationsCD8 T
1                 -5.829592                     -5.937029
2                 -3.103156                     -5.441283
3                 -2.371866                     -5.481890
4                 -3.952811                     -2.094269
5                 -4.301807                     -4.791603
6                 -5.050143                    -20.136702
  logFC_seurat_annotationsT activated logFC_seurat_annotationsNK
1                           -5.819184                  -5.371862
2                           -5.476636                  -5.791665
3                           -3.136328                  -5.336081
4                           -2.961456                  -3.402649
5                          -19.491487                  -5.131678
6                          -20.047400                 -20.101178
  logFC_seurat_annotationsDC logFC_seurat_annotationsB Activated
1                -2.59802435                           -4.942419
2                 0.47567977                           -2.465261
3                 0.08834702                           -1.994659
4                 0.58122480                          -18.227450
5                 0.25232950                           -4.651815
6                -0.31818241                           -1.900209
  logFC_seurat_annotationsMk logFC_seurat_annotationspDC
1                  -2.287668                 -5.61437729
2                  -2.220169                 -2.69192426
3                  -1.687551                 -1.92545848
4                  -1.226571                  0.00605799
5                  -1.670717                 -0.46379865
6                  -2.357705                 -3.04958468
  logFC_seurat_annotationsEryth se_(Intercept) se_stimSTIM
1                     -1.674200     0.40184447  0.08563252
2                     -1.514332     0.09623187  0.11141246
3                     -1.432106     0.05063235  0.05214685
4                     -1.150273    24.90068402  0.50189670
5                     -1.097119    36.39034351  0.33471753
6                     -1.118504   120.18996859  0.45268993
  se_seurat_annotationsCD4 Naive T se_seurat_annotationsCD4 Memory T
1                       0.09080246                        0.09750737
2                       0.15805632                        0.16873458
3                       0.06314074                        0.07421262
4                       0.19506552                        0.11763804
5                       0.57898811                        0.50156695
6                       0.71298736                        1.00429979
  se_seurat_annotationsCD16 Mono se_seurat_annotationsB
1                     0.07969934             0.13337741
2                     0.04792581             0.07658355
3                     0.06692279             0.07115839
4                     0.11110584             0.71063771
5                     0.07474477             0.50181858
6                     0.32102251             1.00568863
  se_seurat_annotationsCD8 T se_seurat_annotationsT activated
1                  0.1578810                     1.747862e-01
2                  0.2289437                     2.829292e-01
3                  0.1187099                     9.415266e-02
4                  0.3106483                     5.829233e-01
5                  0.7085561                     8.113770e+02
6               1299.0548676                     1.475353e+03
  se_seurat_annotationsNK se_seurat_annotationsDC
1               0.1513727              0.11611478
2               0.3213261              0.06749747
3               0.1371742              0.08497132
4               0.7116677              0.15267928
5               1.0011740              0.10159948
6            1479.0206709              0.21714832
  se_seurat_annotationsB Activated se_seurat_annotationsMk
1                        0.1657077               0.1523154
2                        0.1021583               0.1248050
3                        0.1072164               0.1481700
4                      909.9582229               0.4231231
5                        1.0015331               0.3089855
6                        0.3889408               0.6117675
  se_seurat_annotationspDC se_seurat_annotationsEryth p_(Intercept)
1                0.3217026                  0.2760968  5.342043e-14
2                0.1719185                  0.1970072 3.345229e-275
3                0.1715396                  0.2515428  7.767610e-39
4                0.2970370                  0.7316917  8.149952e-01
5                0.2150128                  0.4238227  8.569313e-01
6                1.0290549                  0.6833769  9.407967e-01
    p_stimSTIM p_seurat_annotationsCD4 Naive T p_seurat_annotationsCD4 Memory T
1 0.000000e+00                    0.000000e+00                     0.000000e+00
2 0.000000e+00                   1.164713e-298                    8.922565e-232
3 0.000000e+00                    0.000000e+00                     0.000000e+00
4 7.339587e-26                    1.613556e-28                     2.799844e-04
5 2.406183e-54                    1.808304e-21                     3.005789e-21
6 9.315778e-28                    5.765069e-14                     3.914389e-08
  p_seurat_annotationsCD16 Mono p_seurat_annotationsB p_seurat_annotationsCD8 T
1                 1.600099e-204          0.000000e+00             1.817356e-309
2                  1.501744e-04          0.000000e+00             7.347369e-125
3                  1.065760e-96         1.318880e-243              0.000000e+00
4                  1.887155e-03          2.661755e-08              1.566451e-11
5                  7.747299e-01          1.013205e-17              1.356407e-11
6                  1.067305e-15          5.124893e-07              9.876325e-01
  p_seurat_annotationsT activated p_seurat_annotationsNK p_seurat_annotationsDC
1                   4.850575e-243          7.622319e-276          6.954368e-111
2                    1.782461e-83           1.256825e-72           1.823288e-12
3                   2.667416e-243           0.000000e+00           2.984665e-01
4                    3.767330e-07           1.742226e-06           1.407567e-04
5                    9.808345e-01           2.964967e-07           1.300725e-02
6                    9.891585e-01           9.891564e-01           1.428454e-01
  p_seurat_annotationsB Activated p_seurat_annotationsMk
1                   1.790624e-195           5.489659e-51
2                   1.159895e-128           8.584632e-71
3                    2.979775e-77           4.728367e-30
4                    9.840186e-01           3.745330e-03
5                    3.405813e-06           6.405090e-08
6                    1.031141e-06           1.162403e-04
  p_seurat_annotationspDC p_seurat_annotationsEryth gene_id   gene     padjStim
1            3.319516e-68              1.329305e-09   10969   CCL8 0.000000e+00
2            2.923012e-55              1.509971e-14    3282 CXCL11 0.000000e+00
3            3.088817e-29              1.246159e-08    3281 CXCL10 0.000000e+00
4            9.837285e-01              1.159334e-01    9199   TGM1 8.239827e-25
5            3.099986e-02              9.635954e-03    2620  HESX1 4.620373e-53
6            3.041807e-03              1.016869e-01    8963  CCNA1 1.100814e-26
genetopNeb1<-ifnbNebSumm1%>%
   top_n(200)
Selecting by padjStim
DotPlot(ifnb,features=genetopNeb1$gene[1:30],group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

pheatmap(pseudo_ifnb1@assays$RNA$scale.data[genetopNeb1$gene,],
         cluster_cols = FALSE,       
         scale = "column",              
         show_rownames = FALSE,
         show_colnames = TRUE)

6.2.4 Comparaison

ggvenn(list(DEseq2=rownames(genetopDEseq1),
            Nebula=genetopNeb1$gene,
            Wilcox=rownames(genetop_wilcox1)))

6.3 Exemple 2

Question 2 : déterminer les gènes dont l’expression varie entre les deux conditions pour le type cellulaire “CD14 Mono” fixé.

6.3.1 Test de Wilcoxon

ICD14Mono<-which(ifnb$seurat_annotations=="CD14 Mono")

Idents(ifnb)<-ifnb$celltype.stim
res2_Wilcox <- FindMarkers(object = ifnb, 
                           ident.1 = "CD14 Mono_STIM", 
                           ident.2 = "CD14 Mono_CTRL",
                           test.use = "wilcox")

round(100*mean(res2_Wilcox$p_val_adj<0.01),2)
[1] 32.24
genetop_wilcox2<-res2_Wilcox %>%
   filter(p_val_adj<0.01)%>%
   arrange(desc(abs(avg_log2FC)))%>%
   top_n(100)
Selecting by p_val_adj
DotPlot(ifnb[,ICD14Mono],features=rownames(genetop_wilcox2)[1:10],
        group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

DotPlot(ifnb[,ICD14Mono],features=rownames(genetop_wilcox2)[1:10],
        group.by="donor.stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

6.3.2 Test de DESeq2

pseudo_ifnb2 <- AggregateExpression(ifnb[,ICD14Mono], 
                                   assays = "RNA", 
                                   return.seurat = T, 
                                   group.by = c("stim", "donor_id"))

cts2<-as.matrix(pseudo_ifnb2@assays$RNA$counts)
coldata2<-pseudo_ifnb2@meta.data
dds2 <- DESeqDataSetFromMatrix(countData = cts2,
                              colData = coldata2,
                              design= ~ donor_id + stim) 
dds2 <- DESeq(dds2,fitType = c("local"))                      
resultsNames(dds2) # lists the coefficients
[1] "Intercept"                    "donor_id_SNG.1015_vs_SNG.101"
[3] "donor_id_SNG.1016_vs_SNG.101" "donor_id_SNG.1039_vs_SNG.101"
[5] "donor_id_SNG.107_vs_SNG.101"  "donor_id_SNG.1244_vs_SNG.101"
[7] "donor_id_SNG.1256_vs_SNG.101" "donor_id_SNG.1488_vs_SNG.101"
[9] "stim_STIM_vs_CTRL"           
resDEseq2<-results(dds2,name="stim_STIM_vs_CTRL")
length(which(resDEseq2$padj<0.01)) / nrow(resDEseq2)
[1] 0.1761901
genetopDEseq2<-as.data.frame(resDEseq2) %>%
   filter(padj<0.01)%>%
   arrange(desc(abs(log2FoldChange)))%>%
   top_n(100)
Selecting by padj
DotPlot(pseudo_ifnb2,features=rownames(genetopDEseq2)[1:10],group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

DotPlot(ifnb[,ICD14Mono],features=rownames(genetopDEseq2)[1:10],group.by="donor.stim")+
     theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

pheatmap(pseudo_ifnb2@assays$RNA$scale.data[rownames(genetopDEseq2),],
         cluster_cols = FALSE,       
         scale = "column",              
         show_rownames = FALSE,
         show_colnames = TRUE)

6.3.3 Test avec Nebula

Idents(ifnb)<-ifnb$seurat_annotations
ifnb_neb2<-scToNeb(obj=ifnb[,ICD14Mono],
                   assay="RNA",
                   id="donor_id",
                   pred=c("stim"))
df2 = model.matrix(~stim, data=ifnb_neb2$pred)
data_g2 = group_cell(count=ifnb_neb2$count,id=ifnb_neb2$id,pred=df2) # il faut grouper les cellules par indiv avant de faire nebula

#re2 = nebula(data_g2$count,data_g2$id,pred=data_g2$pred)
re2<-readRDS("Exercices/ResNebulaQ2.rds")

round(100*mean(p.adjust(re2$summary$p_stimSTIM,method="BH")<0.01),2)
[1] 47.75
ifnbNebSumm2<-re2$summary
ifnbNebSumm2$padjStim<-p.adjust(re2$summary$p_stimSTIM,method="BH")
ifnbNebSumm2<-ifnbNebSumm2%>%
            filter(padjStim <0.01)%>%
            arrange(desc(abs(logFC_stimSTIM)))  
head(ifnbNebSumm2)
  logFC_(Intercept) logFC_stimSTIM se_(Intercept) se_stimSTIM p_(Intercept)
1        -4.5536120       6.439125     0.51901138  1.00283914  1.730022e-18
2         0.6440147       6.390013     0.09225028  0.08222706  2.927358e-12
3        -0.8221986       6.099575     0.09229319  0.14361676  5.170400e-19
4        -3.7176773       5.960548     0.30092123  0.57837054  4.616411e-35
5         1.3395450       5.796196     0.05378646  0.05694309 6.588350e-137
6        -0.4725841       5.224608     0.07248106  0.09935159  7.025822e-11
    p_stimSTIM gene_id   gene     padjStim
1 1.354757e-10    8963  CCNA1 7.830196e-10
2 0.000000e+00   10969   CCL8 0.000000e+00
3 0.000000e+00    3282 CXCL11 0.000000e+00
4 6.636461e-25    2620  HESX1 7.608229e-24
5 0.000000e+00    3281 CXCL10 0.000000e+00
6 0.000000e+00    7188  IFIT2 0.000000e+00
genetopNeb2<-ifnbNebSumm2%>%
   top_n(100)
Selecting by padjStim
DotPlot(ifnb[,ICD14Mono],features=genetopNeb2$gene[1:10],group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

DotPlot(ifnb[,ICD14Mono],features=genetopNeb2$gene[1:10],group.by="donor.stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

pheatmap(pseudo_ifnb2@assays$RNA$scale.data[genetopNeb2$gene,],
         cluster_cols = FALSE,       
         scale = "row",              
         show_rownames = FALSE,
         show_colnames = TRUE)

6.3.4 Par une approche supervisée (ex: Random Forest)

  • Random Forest pour prédire STIM / CTRL
datatrain<-t(as.matrix(ifnb@assays$RNA$scale.data[,ICD14Mono]))
dataRF<-data.frame(datatrain,
                   Cl = as.factor(ifnb$stim[ICD14Mono]))

#resRF<-randomForest(Cl ~ ., data = dataRF,importance=T,proximity=T)
resRF<-readRDS("Exercices/resNF.rds")

table(resRF$predicted,dataRF$Cl)
      
       CTRL STIM
  CTRL 2158    1
  STIM    9 2085
  • Importance des variables
p1<-ggvip(resRF,num_var = 20,type=1)
p2<-ggvip(resRF,num_var = 20,type=2)

p1$vip + p2$vip

genetopRF2<-as.data.frame(resRF$importance)%>%
      arrange(desc(MeanDecreaseGini))%>%
      top_n(100)
Selecting by MeanDecreaseGini
DotPlot(ifnb[,ICD14Mono],features=rownames(genetopRF2)[1:10],group.by="stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
Warning: Scaling data with a low number of groups may produce misleading
results

DotPlot(ifnb[,ICD14Mono],features=rownames(genetopRF2)[1:10],group.by="donor.stim")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

II<-which(rownames(pseudo_ifnb2@assays$RNA$scale.data) %in% rownames(genetopRF2))
pheatmap(pseudo_ifnb2@assays$RNA$scale.data[II,],
         cluster_cols = FALSE,       
         scale = "row",              
         show_rownames = FALSE,
         show_colnames = TRUE)

6.3.5 Comparaison

ggvenn(list(DEseq2=rownames(genetopDEseq2),
            Nebula=genetopNeb2$gene,
            Wilcox=rownames(genetop_wilcox2),
            RF=rownames(genetopRF2)))

7 Analyse fonctionnelle des gènes

7.1 Choix de la liste de gènes à analyser

  • Question 1 : déterminer les gènes dont l’expression varie entre les deux conditions (types cellulaires confondus) :
    • Test de Wilcoxon : res1_Wilcox
    • Avec DESeq2 dans FindMarkers : bulk.cond1
    • DESeq2 : resDEseq1
    • NEBULA : ifnbNebSumm1
  • Question2 : déterminer les gènes dont l’expression varie entre les deux conditions pour le type cellulaire “CD14 Mono” fixé :
    • Test de Wilcoxon : res2_Wilcox
    • Avec DESeq2 dans FindMarkers : bulk.cond2
    • DESeq2 : resDEseq2
    • NEBULA : ifnbNebSumm2

On choisi les gènes différentiellement exprimés avec méthode DESeq2 de Seurat pour la Question 1 :

genes <- bulk.cond1  |> filter(p_val_adj<0.01)  |> rownames()
genes
   [1] "NT5C3A"        "IFI16"         "ISG20"         "DDX58"        
   [5] "RTP4"          "TRIM22"        "C19orf66"      "PPM1K"        
   [9] "SP110"         "IL1RN"         "TREX1"         "PHF11"        
  [13] "HERC5"         "SAMD9"         "CCL8"          "DHX58"        
  [17] "MT2A"          "IDO1"          "HSH2D"         "SOCS1"        
  [21] "IFITM2"        "EIF2AK2"       "PARP9"         "XRN1"         
  [25] "IFITM1"        "IFIT5"         "RBCK1"         "NEXN"         
  [29] "RABGAP1L"      "CD164"         "HES4"          "NMI"          
  [33] "HERC6"         "SPATS2L"       "NCOA7"         "PNPT1"        
  [37] "CXCL11"        "PSMA2.1"       "SP100"         "IFIH1"        
  [41] "TRADD"         "DDX60"         "NUB1"          "IFI35"        
  [45] "HELZ2"         "PARP14"        "IRF7"          "DRAP1"        
  [49] "TMEM140"       "IRF2"          "RNF213"        "SLC38A5"      
  [53] "TMSB10"        "STAT1"         "PARP12"        "C5orf56"      
  [57] "MX2"           "SLAMF7"        "SSB"           "BAG1"         
  [61] "TMEM123"       "CXCL10"        "TNFSF10"       "PARP10"       
  [65] "HAPLN3"        "IFIT2"         "FAM46A"        "ZBP1"         
  [69] "SAMD9L"        "CFLAR"         "IFIT3"         "GBP4"         
  [73] "CHST12"        "OAS1"          "CMPK2"         "ADAR"         
  [77] "FBXO6"         "DCK"           "TRAFD1"        "SP140"        
  [81] "ELF1"          "RTCB"          "GCH1"          "TRIM14"       
  [85] "IRF8"          "EPSTI1"        "DTX3L"         "UBE2L6"       
  [89] "PARP11"        "OASL"          "ISG15"         "DDX60L"       
  [93] "SMCHD1"        "USP15"         "DYNLT1"        "IL27"         
  [97] "BST2"          "VCAN"          "PSMA4"         "BLZF1"        
 [101] "LY6E"          "CD38"          "STAT2"         "STOM"         
 [105] "HSPA1A"        "TNFSF13B"      "NAPA"          "MOV10"        
 [109] "GMPR"          "HESX1"         "APOL3"         "OPTN"         
 [113] "IL15RA"        "PLSCR1"        "SAMD4A"        "OAS3"         
 [117] "CNP"           "APOL6"         "XAF1"          "TRIM5"        
 [121] "GSDMD"         "IFIT1"         "GPBP1"         "MX1"          
 [125] "TAP1"          "KIAA0040"      "PI4K2B"        "PML"          
 [129] "KPNB1"         "NADK"          "GBP1"          "CHMP5"        
 [133] "TARBP1"        "ZNFX1"         "OAS2"          "FAS"          
 [137] "SNX6"          "AIM2"          "IRF9"          "CD47"         
 [141] "HLA-E"         "TRIM21"        "RSAD2"         "ODF2L"        
 [145] "WARS"          "PRR5"          "TAPBP"         "USP30-AS1"    
 [149] "LGALS9"        "PCGF5"         "PPP2R2A"       "BBX"          
 [153] "PSMA5"         "CLEC5A"        "LTA4H"         "LMNB1"        
 [157] "PSMB9"         "NDUFA9"        "DECR1"         "IFI44"        
 [161] "INSIG1"        "TMX1"          "CTD-2521M24.9" "IRG1"         
 [165] "MDK"           "ADPRHL2"       "BLVRA"         "CD274"        
 [169] "LAMP3"         "C1GALT1"       "PSMB8"         "CXorf21"      
 [173] "TAP2.1"        "MARCKS"        "NBN"           "MMP9"         
 [177] "TRIM38"        "CD40"          "SP140L"        "DEFB1"        
 [181] "BAZ1A"         "LYSMD2"        "IL8"           "NUPR1"        
 [185] "HSPB1"         "APOBEC3A"      "GLIPR1"        "MYL12A"       
 [189] "BRK1"          "CASP10"        "N4BP1"         "IL4I1"        
 [193] "MTHFD2"        "CD14"          "DUSP5"         "BCL2L14"      
 [197] "WIPF1"         "CASP8"         "NUCB1"         "TDRD7"        
 [201] "MB21D1"        "OGFR"          "IFI6"          "TGM1"         
 [205] "GBP2"          "ARL6IP6"       "RNF114"        "MAX"          
 [209] "GNB4"          "CBWD2"         "ARL5B"         "CD48"         
 [213] "CCNA1"         "GBP3"          "IQGAP2"        "CBR1"         
 [217] "TRAM1"         "SIGLEC1"       "PSME2"         "TMEM110"      
 [221] "IFITM3"        "B2M"           "SAT1"          "LAG3"         
 [225] "HSP90AA1"      "PRKD2"         "RBM7"          "PGAP1"        
 [229] "LPIN2"         "SUB1"          "RP11-701P16.5" "RAB8A"        
 [233] "TRANK1"        "DENND1B"       "TBXAS1"        "LAP3"         
 [237] "JUP"           "CLEC2B"        "SGTB"          "SAP18"        
 [241] "ZC3HAV1"       "AMICA1"        "PRDX4"         "DNAJA1"       
 [245] "RARRES3"       "ACOT9"         "CNDP2"         "UNC93B1"      
 [249] "PID1"          "CD80"          "MASTL"         "IGFBP4"       
 [253] "TXN"           "DNPEP"         "CXCL9"         "DAPP1"        
 [257] "VAMP5"         "MKRN1"         "CAST"          "PDGFRL"       
 [261] "C1GALT1C1"     "FOS"           "TMEM50A"       "ETV7"         
 [265] "FAM72B"        "FBXO7"         "SCIN"          "DEK"          
 [269] "S100A8"        "TBC1D1"        "IL1B"          "RAD9A"        
 [273] "MNDA"          "CMKLR1"        "RIN2"          "GCNT2"        
 [277] "CH25H"         "KLF6"          "ATP2B1"        "ATG3"         
 [281] "GTF2B"         "CYP27A1"       "MIA3"          "GCA"          
 [285] "GPR141"        "ARHGEF3"       "CD69"          "SPTLC2"       
 [289] "BATF2"         "EIF3L"         "EXOC3L1"       "APOL1"        
 [293] "ANKRD22"       "QPCT"          "ATF3"          "ICAM2"        
 [297] "CIR1"          "C3orf38"       "C19orf59"      "SNX10"        
 [301] "MYCBP2"        "IL13RA1"       "C4orf3"        "SRGAP2"       
 [305] "SNN"           "HSP90AB1"      "B3GNT2"        "TOR1B"        
 [309] "MLKL"          "CASP4"         "SLC31A2"       "LYN"          
 [313] "SORL1"         "NAGK"          "CD2AP"         "KARS"         
 [317] "TINF2"         "ATF5"          "SPSB1"         "PRF1"         
 [321] "TMEM60"        "PMAIP1"        "MCL1"          "MVP"          
 [325] "TFG"           "FYTTD1"        "TAGAP"         "ARHGAP25"     
 [329] "FERMT3"        "ATP5F1"        "GALM"          "HELB"         
 [333] "SNX2"          "IFI44L"        "HSPA8"         "ARL8B"        
 [337] "TFEC"          "TRIM69"        "FAM8A1"        "PLAC8"        
 [341] "ADAM19"        "GTF2E2"        "NT5C2"         "SFT2D2"       
 [345] "CMC2"          "LILRA5"        "SAMHD1"        "RNF138"       
 [349] "APOBEC3F"      "TMEM126B"      "EHD4"          "TANK"         
 [353] "RRBP1"         "RP11-343N15.5" "FUT4"          "CLTB"         
 [357] "HRASLS2"       "DRAM1"         "ACTA2"         "ENDOD1"       
 [361] "CASP7"         "AIDA"          "SLC25A28"      "SERPING1"     
 [365] "CLIC2"         "RIN3"          "FAM26F"        "SLC7A11"      
 [369] "PRLR"          "HBEGF"         "CCR5"          "GIMAP4"       
 [373] "DOCK8"         "RASGRP3"       "TIA1"          "NUDCD1"       
 [377] "CXXC5"         "PLXDC2"        "CD53"          "SYNE2"        
 [381] "HEXDC"         "OSM"           "GPR180"        "TNFAIP6"      
 [385] "TKT"           "MTSS1"         "MRPL17"        "PIK3AP1"      
 [389] "TPST1"         "CDKN2D"        "STX17"         "CST6"         
 [393] "IL15"          "PLIN2"         "GNG5"          "POLR2E"       
 [397] "HERPUD2"       "CALCOCO2"      "STAP1"         "CTSZ"         
 [401] "RGCC"          "TLR7"          "YBX1"          "CLEC2D"       
 [405] "HNMT"          "DNAJB4"        "CAMK1"         "RIPK2"        
 [409] "SHISA5"        "IRF1"          "ZNHIT1"        "CASP3"        
 [413] "CKB"           "SOBP"          "FAM72A"        "UBE2F"        
 [417] "FAM50A"        "CTD-2037K23.2" "CXorf38"       "PSME1"        
 [421] "SCARB2"        "UBXN2A"        "OSBPL1A"       "MCOLN2"       
 [425] "MSR1"          "RP11-445H22.3" "EXOSC9"        "ORAI3"        
 [429] "DERL1"         "SCLT1"         "NCF1"          "CASP1"        
 [433] "ARID5A"        "PLEK"          "ITM2B"         "GPR171"       
 [437] "RNASEH2B"      "DAB2"          "STX11"         "BAK1"         
 [441] "CLIC4"         "MAT2B"         "GOLGA4"        "UBE2Z"        
 [445] "CACNA1A"       "SDC2"          "MYD88"         "PARP8"        
 [449] "THBD"          "BRCA2"         "CHRNB1"        "HDAC1"        
 [453] "TWF2"          "CSRNP1"        "ST3GAL5"       "YPEL3"        
 [457] "JAK2"          "MORC3"         "EDEM1"         "TCF4"         
 [461] "PELI1"         "NASP"          "UBE2D3"        "ATP6V1H"      
 [465] "RP11-420G6.4"  "MSL3"          "CMTR1"         "USP18"        
 [469] "UBA7"          "ARL6IP4"       "RPS6KA1"       "TYMP"         
 [473] "GATSL3"        "LMO2"          "APOBEC3G"      "MLTK"         
 [477] "AC007036.6"    "ZFP36L1"       "GADD45B"       "TPI1"         
 [481] "TRIM25"        "NINJ1"         "PRKX"          "DNAAF1"       
 [485] "ENG"           "AC004988.1"    "NUMB"          "CUL1"         
 [489] "CDS2"          "PPA1"          "C9orf72"       "IER3"         
 [493] "PIM3"          "ARHGAP18"      "CATSPER1"      "PAPD7"        
 [497] "IRGQ"          "PTPLAD2"       "TUBA4A"        "ABCC3"        
 [501] "PTGS1"         "AP5B1"         "SLFN12"        "CTB-61M7.2"   
 [505] "CFB"           "QSOX1"         "UTRN"          "GPR155"       
 [509] "FFAR2"         "TRIM56"        "RBM43"         "PANK2"        
 [513] "APPL1"         "ID3"           "GIMAP2"        "KCNJ15"       
 [517] "UQCRB"         "ANKFY1"        "RDH14"         "LYPD2"        
 [521] "DCP1A"         "POLD4"         "C21orf91"      "FAR2"         
 [525] "GCNT1"         "AHSA1"         "KCNA3"         "CYP2J2"       
 [529] "CTD-2336O2.1"  "MESDC2"        "WDFY1"         "SCPEP1"       
 [533] "SRSF9"         "ATXN1"         "CD9"           "AC009948.5"   
 [537] "RP11-288L9.1"  "HAVCR2"        "PHACTR4"       "CCRL2"        
 [541] "HTRA1"         "JARID2"        "NR4A3"         "UBAC1"        
 [545] "HBP1"          "KCNN4"         "PANX1"         "EXT1"         
 [549] "FKBP11"        "SLC25A11"      "STAT3"         "PCMT1"        
 [553] "SMOX"          "SPN"           "RAB4A"         "GBP5"         
 [557] "MRPS18C"       "TNK2"          "BRIP1"         "NDFIP1"       
 [561] "UBQLN2"        "SERPINB1"      "EDN1"          "CCL4"         
 [565] "PABPC4"        "CARD16"        "OSBPL9"        "GPX1"         
 [569] "SMPDL3A"       "CCDC85B"       "TRIM44"        "HIPK2"        
 [573] "MAP1LC3A"      "AP2S1"         "CXCL1"         "C4orf32"      
 [577] "RPS6KB2"       "TPMT"          "CALM1"         "LGALS3BP"     
 [581] "OAZ2"          "ZCCHC2"        "RRAGC"         "BATF3"        
 [585] "SLC18B1"       "GLRX"          "TRIM26"        "MARCKSL1"     
 [589] "LIPA"          "PHLDA1"        "PKD2L1"        "MS4A6A"       
 [593] "RAB7L1"        "H3F3B"         "RP11-326I11.3" "RPS6KA5"      
 [597] "RAB31"         "FOXN2"         "BARD1"         "CD82"         
 [601] "FGD2"          "FARP1"         "SH3GLB1"       "LILRB1"       
 [605] "GPD2"          "GTPBP1"        "RFX2"          "RBMS1"        
 [609] "DESI1"         "PDCD6"         "STK24"         "CCDC50"       
 [613] "KIAA0226"      "AC093673.5"    "KIFC3"         "ST14"         
 [617] "NMB"           "GBP7"          "CYTH4"         "TPST2"        
 [621] "JTB"           "PPARG"         "GRIN3A"        "VCPIP1"       
 [625] "TMEM14C"       "TXNIP"         "EMC2"          "SETX"         
 [629] "MICB"          "MEF2A"         "RP11-274B18.2" "HIP1"         
 [633] "RGL1"          "PDLIM7"        "ADAMDEC1"      "HINT3"        
 [637] "DLAT"          "SLC38A6"       "CYLD"          "MOB3C"        
 [641] "GMDS"          "PRDX5"         "TNFSF18"       "CNPPD1"       
 [645] "CAPN2"         "BLNK"          "SDHA"          "WDR41"        
 [649] "AC010226.4"    "ARHGAP27"      "EXOC2"         "PPM1M"        
 [653] "SCIMP"         "RBX1"          "EEPD1"         "PGD"          
 [657] "CNPY3"         "SNX3"          "PDCD1LG2"      "CKS1B"        
 [661] "WBP1L"         "C9orf91"       "ATP10A"        "FAM177A1"     
 [665] "PAIP1"         "C14orf159"     "CECR1"         "MIER1"        
 [669] "TPM3"          "ARAP2"         "TMEM229B"      "MRPL32"       
 [673] "RAB8B"         "SIVA1"         "FXYD6"         "STK38L"       
 [677] "EXOSC4"        "DDX5"          "CTSA"          "MIR142"       
 [681] "SECTM1"        "RNF19B"        "C17orf67"      "OSBPL8"       
 [685] "RP11-65J3.1"   "FKBP5"         "NECAP2"        "BCKDK"        
 [689] "RBMS2"         "FAM122C"       "RAPGEF2"       "MAGT1"        
 [693] "SRSF5"         "YWHAQ"         "SRP54"         "ARRDC2"       
 [697] "FAM19A2"       "AP000640.2"    "IGSF6"         "CD163"        
 [701] "ASL"           "SPG20"         "CD93"          "CKAP4"        
 [705] "HIAT1"         "PATL2"         "ZNF277"        "ADPRH"        
 [709] "CCSER2"        "MOB1A"         "DKFZP761J1410" "CBWD5"        
 [713] "TMEM187"       "C11orf31"      "CITED2"        "EIF4E3"       
 [717] "HLA-A"         "PNPLA6"        "TBC1D7"        "TNFSF14"      
 [721] "CD44"          "PKM"           "IKBKE"         "RAD23A"       
 [725] "SEC62"         "SSTR2"         "GSTP1"         "EPB41L3"      
 [729] "MKKS"          "GNA15"         "CTSS"          "UBC"          
 [733] "SLC25A3"       "CTNNBIP1"      "PHACTR2"       "KLF5"         
 [737] "ATP13A3"       "RILPL2"        "BAD"           "THBS1"        
 [741] "GAPT"          "ZMIZ1"         "NLRC4"         "CCR1"         
 [745] "AKAP7"         "ENPP2"         "KCNAB2"        "AC009133.12"  
 [749] "LMO4"          "HLA-F"         "RIPK1"         "SLC35A5"      
 [753] "RP1-197B17.3"  "GIMAP6"        "CTNNBL1"       "PLEK2"        
 [757] "BCAT1"         "SPG21"         "SIRT2"         "RALB"         
 [761] "HM13"          "CLEC4A"        "B4GALT5"       "ITGAE"        
 [765] "NLRC5"         "SLC27A3"       "EEF1A1"        "TNFAIP8L1"    
 [769] "GRIPAP1"       "RPL6"          "SLC25A39"      "IDS"          
 [773] "SAT2"          "CORO1B"        "TM6SF1"        "FAM89B"       
 [777] "IDO2"          "EIF3D"         "GNAI3"         "HSPA1B"       
 [781] "CDC73"         "HNRNPDL"       "RXFP1"         "ASCL2"        
 [785] "ATP1A1"        "MR1"           "LRP1"          "KCTD14"       
 [789] "H2AFY"         "MAD2L1BP"      "PLAU"          "BTG3"         
 [793] "GIMAP8"        "PSTPIP1"       "ACTN1"         "PPIF"         
 [797] "ETNK1"         "DOK3"          "EMD"           "FRMD3"        
 [801] "ERO1L"         "AZI2"          "MED25"         "MORF4L1"      
 [805] "N4BP2L1"       "LINC00158"     "POMP"          "NABP1"        
 [809] "ATP6AP1"       "RAB34"         "NUP214"        "GUCY1A3"      
 [813] "RP11-408H1.3"  "CHORDC1"       "TAX1BP3"       "UQCRC1"       
 [817] "PSMA3"         "NHLRC3"        "VEGFA"         "UFD1L"        
 [821] "TMEM219"       "MAP2K6"        "SRSF4"         "ANKIB1"       
 [825] "MEFV"          "LNPEP"         "TCN2"          "CNIH4"        
 [829] "NISCH"         "HIST1H2AC"     "HNRNPM"        "GGA1"         
 [833] "MCOLN1"        "ABCD1"         "ALCAM"         "ARL3"         
 [837] "PSMG2"         "PLEKHF2"       "ARHGDIB"       "APOL2"        
 [841] "C18orf25"      "ERCC1"         "AXL"           "EMC3"         
 [845] "AAED1"         "OTOF"          "CGGBP1"        "PSMB10"       
 [849] "UTP11L"        "GLIS3"         "ARMCX1"        "VNN1"         
 [853] "PARP4"         "HNRNPF"        "RASSF4"        "CR1L"         
 [857] "PARP15"        "TMEM19"        "AGO1"          "TSPAN13"      
 [861] "S100A2"        "GSTK1"         "CXCL5"         "ITGB1"        
 [865] "HIST2H2BE"     "RNF19A"        "SMARCA5"       "CTSC"         
 [869] "GOLM1"         "UBE2D1"        "IER2"          "EMP1"         
 [873] "SLC25A5"       "TMEM170A"      "SSU72"         "BCL11A"       
 [877] "SLC16A6"       "LILRB2"        "BEST1"         "P2RY6"        
 [881] "RERE"          "GZMB"          "SDS"           "ACSL1"        
 [885] "ARF5"          "LINC00984"     "KDSR"          "NEDD1"        
 [889] "SLC11A1"       "RASGEF1B"      "GPR108"        "RNF130"       
 [893] "TOP1"          "CXCL13"        "GTPBP2"        "SERPINB9"     
 [897] "ESD"           "CCND2"         "MMP14"         "FAM43A"       
 [901] "SLC41A2"       "RNASE2"        "RP11-737O24.1" "COL7A1"       
 [905] "SCT"           "BBC3"          "AKR7A2"        "MRPL44"       
 [909] "SLC16A10"      "HCLS1"         "RPA3"          "L3HYPDH"      
 [913] "ALPK1"         "CTSH"          "ZNF620"        "BCL2L13"      
 [917] "BLMH"          "H2AFV"         "PDE7A"         "NDUFB10"      
 [921] "SLC25A6"       "PFN1"          "SQRDL"         "GIMAP7"       
 [925] "FAM111A"       "HLA-C"         "CD1D"          "HAX1"         
 [929] "ST8SIA4"       "RBMX"          "CLDND1"        "ACTB"         
 [933] "LILRB4"        "GNA12"         "SIGLEC9"       "UBE2S"        
 [937] "RBBP6"         "RPS19BP1"      "TLR4"          "FCAR"         
 [941] "ARID4B"        "WHSC1L1"       "FNIP2"         "BTN3A1"       
 [945] "SH3BP5"        "GLIPR2"        "PAPSS2"        "NLN"          
 [949] "DOK2"          "ANXA1"         "RNF168"        "NFE2L3"       
 [953] "CCS"           "C7orf49"       "ARL6IP5"       "APH1B"        
 [957] "FTH1"          "PKIB"          "XIAP"          "RAB3GAP1"     
 [961] "RABGGTA"       "STAB1"         "IL16"          "KCTD20"       
 [965] "NCKAP1L"       "CEBPA"         "RP11-532F6.3"  "STRA13"       
 [969] "EVI5"          "CD101"         "ZNF106"        "IPCEF1"       
 [973] "MOB3A"         "RBM11"         "PLXND1"        "SAYSD1"       
 [977] "RP11-480C16.1" "TALDO1"        "E2F5"          "SELL"         
 [981] "CDC42EP1"      "YEATS2"        "TNFRSF12A"     "SMPD3"        
 [985] "LINC00926"     "JADE2"         "FGL2"          "TMEM167A"     
 [989] "CNTRL"         "EFR3A"         "CCL7"          "AQP9"         
 [993] "PDE4B"         "NDUFA2"        "ANXA4"         "PARK7"        
 [997] "SLC37A1"       "DPEP2"         "CORO1A"        "TANC2"        
[1001] "HP1BP3"        "CTNND1"        "PIK3R5"        "SLC35A4"      
[1005] "SYNJ2BP"       "SSR4"          "KAT2A"         "STX18"        
[1009] "RNF7"          "ATP6V0B"       "RICTOR"        "MAPK1"        
[1013] "RP11-10J5.1"   "C10orf54"      "ZRSR2"         "COTL1"        
[1017] "PRPS2"         "ZBTB43"        "FANCA"         "CAPZB"        
[1021] "MS4A7"         "TMEM251"       "GALNT1"        "IFI27"        
[1025] "EHD1"          "SMC6"          "ATL3"          "PREX1"        
[1029] "ABHD8"         "HNRNPA1"       "DEDD2"         "CREG1"        
[1033] "RBM14"         "UBE2B"         "RP2"           "USP33"        
[1037] "RP11-63P12.6"  "PTGFRN"        "HDGF"          "SCO2"         
[1041] "COMMD3"        "MITF"         
foldChange <- bulk.cond1|> filter(p_val_adj<0.01)  |>  pull(avg_log2FC)
names(foldChange) <- genes

Les gènes sont identifiés selon leur nom ou “gene symbol”. Pour obtenir la meilleure correspondance possible avec les bases de données, on va les convertir en entrez id, qui sont les identifiants de la base de données gene du ncbi :

eg = clusterProfiler::bitr(genes, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
'select()' returned 1:1 mapping between keys and columns
Warning in clusterProfiler::bitr(genes, fromType = "SYMBOL", toType =
"ENTREZID", : 8.06% of input gene IDs are fail to map...
eg  |> as_tibble()  |>  rmarkdown::paged_table()

Il existe d’autres identifiants et d’autres méthodes pour convertir les identifiants, comme la librairie R biomaRt et les identifiants ensembl :

require(biomaRt)
ensembl <- useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl",version =114)


gene_conversion <- getBM(attributes = c("ensembl_gene_id","entrezgene_id","hgnc_symbol"),
      filters = "hgnc_symbol",
      values = genes, 
      mart = ensembl)
gene_conversion %>% as_tibble() %>% rmarkdown::paged_table()

7.2 Les outils d’analyse d’enrichissement

7.2.1 Outils en ligne

7.2.2 Packages R

  • ClusterProfiler
  • hypeR
  • gprofiler2
  • fgsea (utilisé en interne par ClusterProfiler)

Dans ce TP nous utiliserons uniquement ClusterProfiler car il est très complet et bien documenté (voir ici et ici)

7.3 Différentes sources de voies biologiques

  1. Gene Ontology (GO)
  2. KEGG (Kyoto Encyclopedia of Genes and Genomes)
  3. WikiPathways
  4. Disease Ontology
  5. Reactome
  6. MSigDB (Molecular Signatures Database)
  7. MeSH (Medical Subject Headings)

Dans ce TP nous utiliserons uniquement la Gene Ontology, mais c’est possible d’utiliser les autres avec ClusterProfiler

7.3.1 Gene Ontology (GO)

7.3.2 Site web : http://geneontology.org/

7.3.3 Description :

GO est une base de données très utilisée qui classe les gènes selon trois grandes ontologies :

  1. Molecular Function
    • Activités élémentaires d’un produit génique au niveau moléculaire.
  2. Biological Process
    • Suite coordonnée d’événements impliquant plusieurs fonctions moléculaires.
  3. Cellular Component
    • Structures ou localisations cellulaires où les produits géniques sont actifs.

7.4 Approches d’enrichissements

  1. Analyse de Surreprésentation (ORA - Over Representation Analysis) : Cette méthode détermine si les gènes différentiellement exprimés sont enrichis dans des voies spécifiques ou des groupes ontologiques.

  2. Analyse d’Enrichissement de Groupes de Gènes (GSEA - Gene Set Enrichment Analysis) : GSEA évalue si un ensemble pré-défini de gènes présente des différences statistiquement significatives entre deux ou plusieurs conditions biologiques.

Dans ClusterProfiler, les fonctions gseGO() et enrichGO permettent d’utiliser ces deux méthodes avec la Gene Ontology

7.5 Enrichissement par analyse de sur-représentation (ORA - Over Representation Analysis)

L’Analyse de Surreprésentation (ORA) est une méthode courante pour identifier si certaines fonctions ou processus biologiques connus sont surreprésentés (= enrichis) dans une liste de gènes obtenue expérimentalement

From bioinformatics-core-shared-training.github.io/Bulk_RNAseq_Course_Apr22

From bioinformatics-core-shared-training.github.io/Bulk_RNAseq_Course_Apr22

7.6 Analyse GO de sur-représentation avec R et clusterProfiler

On utilise enrichGO() sur nos gènes différentiellement exprimés (objet eg).

org.db <- org.Hs.eg.db::org.Hs.eg.db
ego <- enrichGO(gene          = eg$ENTREZID,
                universe      = NULL,
                OrgDb         = org.db,
                keyType = "ENTREZID",
                ont           = "BP",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.05,
                qvalueCutoff  = 0.05,
                readable      = TRUE)

ego |> as_tibble() |>  rmarkdown::paged_table()

7.6.1 Méthodes de visualisation des résultats d’enrichissement

Voir ici pour la liste complète

7.6.1.1 Dotplot

7.6.1.2 Gene-Concept Network

Récupérer la valeur du fold-change :

foldChange <- foldChange  |> enframe()  |> left_join(eg,by = c("name"="SYMBOL"))  |> dplyr::select(ENTREZID,value)  |> drop_na()  |> deframe()

7.6.1.3 UpSet plots

7.6.1.4 Gene-Concept Network