Potential biomarkers Validation on Public Patient data_Borcherding_1_Malignant_and Nonmalignant_Patient_Sample

1. load libraries

2. Load Data into Seurat

ss_Borcherding$sample <- recode(ss_Borcherding$sample, 
                                "Control" = "Normal CD4", 
                                "SS_Patient1" = "Malignant CD4")

table(ss_Borcherding$sample)

Malignant CD4    Normal CD4 
         3443          4437 

3. QC

VlnPlot(ss_Borcherding, features = c("nFeature_RNA", 
                                    "nCount_RNA", 
                                    "percent.mt"), 
                                      ncol = 3)

Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.

VlnPlot(ss_Borcherding, features = c("nFeature_RNA", 
                                         "nCount_RNA", 
                                         "percent.mt",
                                         "percent.rb"), 
                            ncol = 4, pt.size = 0.1) & 
              theme(plot.title = element_text(size=10))

Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Warning: The following requested variables were not found: percent.rb

FeatureScatter(ss_Borcherding, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA") +
  geom_smooth(method = 'lm')

##FeatureScatter is typically used to visualize feature-feature relationships ##for anything calculated by the object, ##i.e. columns in object metadata, PC scores etc.

FeatureScatter(ss_Borcherding, 
               feature1 = "nCount_RNA", 
               feature2 = "percent.mt")+
  geom_smooth(method = 'lm')

FeatureScatter(ss_Borcherding, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA")+
  geom_smooth(method = 'lm')

4. Log-normalization (scale factor = 10,000)

ss_Borcherding <- NormalizeData(ss_Borcherding, normalization.method = "LogNormalize", scale.factor = 10000)

Normalizing layer: counts.SS_Patient1
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Normalizing layer: counts.Control
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

5. Variable features & scaling

ss_Borcherding <- FindVariableFeatures(ss_Borcherding, selection.method = "vst", nfeatures = 2000)

Finding variable features for layer counts.SS_Patient1
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.Control
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

ss_Borcherding <- ScaleData(ss_Borcherding, features = VariableFeatures(ss_Borcherding))

Centering and scaling data matrix

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6. PCA + UMAP

# PCA
ss_Borcherding <- RunPCA(ss_Borcherding, features = VariableFeatures(ss_Borcherding))

PC_ 1 
Positive:  TRBV14, TRBV21-1, S100A10, TRAV9-2, CRIP1, B2M, ITGB1, S100A6, S100A4, PLS3 
       HLA-C, S100A11, KLF6, CD70, MBP, KIR3DL2, PCSK1N, AHNAK, CD52, HACD1 
       LMNA, VIM, HPGD, GPR15, CCDC167, PTTG1, ANXA2, HLA-A, TSPAN2, KIR3DL1 
Negative:  RPS4Y1, RPS12, RPS6, TPT1, RPL3, MT-CO3, MT-ND3, RPL13, MT-CO1, TXNIP 
       RPSA, MT-ATP8, RPL35, C1orf162, RPS18, MT-CYB, ABLIM1, RPS19, RPLP1, SLC40A1 
       NUCB2, RPLP0, SATB1, HNRNPA1, AIF1, PCED1B, FYB, MBNL1, MT-CO2, VAMP5 
PC_ 2 
Positive:  TYROBP, CST3, LYZ, FCER1G, FCN1, S100A9, HLA-DRA, CYBB, S100A8, SERPINA1 
       CLEC12A, FGL2, VCAN, CSTA, LST1, FGR, MPEG1, SPI1, RAB31, DUSP6 
       MNDA, S100A12, CD68, SLC7A7, NCF2, CLEC7A, LGALS2, TNFAIP2, CD14, LYN 
Negative:  RPSA, RPL3, IFITM1, RPS18, RPLP0, RPL13, TCF7, LTB, LEF1, CD7 
       CCR7, IL7R, PTPRCAP, RPS12, IL32, RPS6, CD27, RPS19, TRBV14, TRBV21-1 
       MAL, ID3, ARHGAP15, TRAV9-2, STMN1, AES, CD3G, RPLP1, LRRN3, RGCC 
PC_ 3 
Positive:  PFN1, MIR4435-2HG, LINC00152, GZMA, CCL5, CST7, ANXA5, GZMK, HLA-DPA1, FAS 
       CTSC, CXCR3, C12orf75, CDCA7, PRDM1, SRGN, CD99, HLA-DRB5, PTPRCAP, KLRB1 
       SH3BGRL3, NKG7, FAM46C, HLA-DPB1, GBP5, ITGA4, CD74, JAKMIP1, ARHGDIB, CD2 
Negative:  TCF7, TRBV14, TRBV21-1, CCR7, LRRN3, FOS, LEF1, JUNB, SPINT2, TRAV9-2 
       RAB25, LINC01480, RHOB, KIR3DL2, RPL13, ESPN, PLS3, KIR3DL1, ID3, DNM3OS 
       COL6A2, TSPAN2, PCSK1N, RHBDL1, JUN, MAL, EPHX2, S100A9, AIF1, LYZ 
PC_ 4 
Positive:  MT-CO2, MT-CO1, MT-ND6, AHNAK, MT-CYB, MIR4435-2HG, FTH1, LINC00152, SH3BGRL3, S100A10 
       TTN, HIST1H1E, FOXP3, C12orf75, S100A4, TTC39C, LGALS3, VIM, NEAT1, MYO1F 
       SAMSN1, S100A6, KLF6, PREX1, TIGIT, PRDM1, CTLA4, HLA-DQA1, CDCA7, MT-ND3 
Negative:  HLA-B, CFL1, HLA-A, IFITM1, ITM2B, CALM2, CCR7, ARHGDIB, MYL12A, CD7 
       PGK1, HSP90AB1, ACTB, MYL6, RPL27A, IFITM2, TXNIP, CD3G, STMN1, RPS18 
       HLA-C, NOSIP, ANXA1, CORO1A, SELL, LEF1, RPL35, PRDX2, CALR, B2M 
PC_ 5 
Positive:  MALAT1, MT-ND3, PTPRC, CCR7, TTN, LEF1, MBNL1, MT-ATP8, MT-ATP6, MT-CYB 
       MT-CO2, SELL, IL6ST, FOXP1, ITK, MT-CO1, LINC00861, MT-CO3, MT-ND2, SPTBN1 
       SF3B1, FAM65B, DDX5, CLEC2D, SLFN5, GPR155, FCMR, IKZF2, SESN3, PDE3B 
Negative:  RPLP1, RPLP0, RPS18, RPL13, RPS19, RPSA, RPS12, SH3BGRL3, RPL35, TPT1 
       RPL36A, NME2, RPL3, TMSB10, S100A4, ATP5E, ACTG1, RPS6, S100A11, CRIP1 
       VIM, FTH1, GAPDH, S100A10, PFN1, ACTB, HNRNPA1, IFITM2, TMSB4X, TXN 

# Optional: Visualize elbow plot
ElbowPlot(ss_Borcherding, ndims = 20)

# Run JackStraw analysis properly
ss_Borcherding <- JackStraw(ss_Borcherding, num.replicate = 100)

Warning: The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.

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ss_Borcherding <- ScoreJackStraw(ss_Borcherding, dims = 1:20)

# Visualize JackStraw scores
JackStrawPlot(ss_Borcherding, dims = 1:20)

7. PCA Visualization

# TEST-1
# given that the output of RunPCA is "pca"
# replace "so" by the name of your seurat object

pct <- ss_Borcherding[["pca"]]@stdev / sum(ss_Borcherding[["pca"]]@stdev) * 100
cumu <- cumsum(pct) # Calculate cumulative percents for each PC
# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[-length(pct)] - pct[-1]) > 0.1), decreasing = T)[1] + 1
# last point where change of % of variation is more than 0.1%. -> co2
co2

[1] 12

# TEST-2
# get significant PCs
stdv <- ss_Borcherding[["pca"]]@stdev
sum.stdv <- sum(ss_Borcherding[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100
cumulative <- cumsum(percent.stdv)
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co2 <- sort(which((percent.stdv[1:length(percent.stdv) - 1] - 
                       percent.stdv[2:length(percent.stdv)]) > 0.1), 
              decreasing = T)[1] + 1
min.pc <- min(co1, co2)
min.pc

[1] 12

# Create a dataframe with values
plot_df <- data.frame(pct = percent.stdv, 
           cumu = cumulative, 
           rank = 1:length(percent.stdv))

# Elbow plot to visualize 
  ggplot(plot_df, aes(cumulative, percent.stdv, label = rank, color = rank > min.pc)) + 
  geom_text() + 
  geom_vline(xintercept = 90, color = "grey") + 
  geom_hline(yintercept = min(percent.stdv[percent.stdv > 5]), color = "grey") +
  theme_bw()

NA
NA
NA

8. Clustering (resolution = 1.2)

# Then find neighbors & clusters
ss_Borcherding <- FindNeighbors(ss_Borcherding, dims = 1:10)

Computing nearest neighbor graph
Computing SNN

ss_Borcherding <- FindClusters(ss_Borcherding, resolution = 1.2)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7880
Number of edges: 260091

Running Louvain algorithm...

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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.7903
Number of communities: 19
Elapsed time: 0 seconds

# run UMAP
ss_Borcherding <- RunUMAP(ss_Borcherding, dims = 1:10)

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 session16:36:34 UMAP embedding parameters a = 0.9922 b = 1.112
16:36:34 Read 7880 rows and found 10 numeric columns
16:36:34 Using Annoy for neighbor search, n_neighbors = 30
16:36:34 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:36:34 Writing NN index file to temp file /tmp/Rtmp8n5s4h/file14a79594363bc
16:36:34 Searching Annoy index using 1 thread, search_k = 3000
16:36:36 Annoy recall = 100%
16:36:36 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:36:38 Initializing from normalized Laplacian + noise (using RSpectra)
16:36:38 Commencing optimization for 500 epochs, with 322366 positive edges
16:36:38 Using rng type: pcg
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:36:44 Optimization finished

RunTSNE(object, dims.use = 1:10)

Error: object 'object' not found

9. Visualize UMAP with Clusters

DimPlot(ss_Borcherding, reduction = "umap",group.by = "sample", label = TRUE, pt.size = 0.6) +
  ggtitle("UMAP of Sézary Syndrome CD4+ T Cells")

DimPlot(ss_Borcherding, reduction = "umap", label = TRUE, pt.size = 0.6) +
  ggtitle("UMAP of Sézary Syndrome CD4+ T Cells")

DimPlot(ss_Borcherding, reduction = "tsne", label = TRUE, pt.size = 0.6) +
  ggtitle("TSNE of Sézary Syndrome CD4+ T Cells")

10. FeaturePlots for Top50 UP

top_50_up <- read.csv("top_50_upregulated.csv")        # or read.delim("top_50_up.tsv")
top_50_down <- read.csv("top_50_downregulated.csv")

Idents(ss_Borcherding) <- "sample"


FeaturePlot(ss_Borcherding, 
             features = top_50_up$gene[1:10], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

Warning: The following requested variables were not found: PCLAF

FeaturePlot(ss_Borcherding, 
             features = top_50_up$gene[11:20], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

FeaturePlot(ss_Borcherding, 
             features = top_50_up$gene[21:30], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

FeaturePlot(ss_Borcherding, 
             features = top_50_up$gene[31:40], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

FeaturePlot(ss_Borcherding, 
             features = top_50_up$gene[41:50], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

11. FeaturePlots for Top50 DOWN

FeaturePlot(ss_Borcherding, 
             features = top_50_down$gene[1:10], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

Warning: The following requested variables were not found: PCED1B-AS1, SNHG5

FeaturePlot(ss_Borcherding, 
             features = top_50_down$gene[11:20], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

Warning: The following requested variables were not found: RIPOR2

FeaturePlot(ss_Borcherding, 
             features = top_50_down$gene[21:30], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

FeaturePlot(ss_Borcherding, 
             features = top_50_down$gene[31:40], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

Warning: The following requested variables were not found: LINC01578

FeaturePlot(ss_Borcherding, 
             features = top_50_down$gene[41:50], 
             reduction = "umap", 
             cols = c("lightblue", "red"),  # Custom color gradient from light blue to red
             label = TRUE)

Warning: The following requested variables were not found: AL138963.4

Visualization

DimPlot(ss_Borcherding, group.by = "seurat_clusters", label = T, label.box = T, repel = T, reduction = "umap")

NA
NA

Visualization of Potential biomarkers-Upregulated

Idents(ss_Borcherding) <- "sample"

# Vector of genes to plot
up_genes <- c("CLIC1", "COX5A","GTSF1", "MAD2L1","MYBL2","MYL6B","NME1","PLK1", "PYCR1", "SLC25A5", "SRI", "TUBA1C", "UBE2T", "YWHAH")

# DotPlot with custom firebrick-red gradient
DotPlot(ss_Borcherding, features = up_genes) +
  RotatedAxis() +
  scale_color_gradient2(low = "lightyellow", mid = "red", high = "firebrick", midpoint = 1) +
  ggtitle("Expression of Upregulated Genes in Sézary Syndrome") +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    axis.text.y = element_text(size = 12),
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14)
  )

Warning: Scaling data with a low number of groups may produce misleading resultsScale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Idents(ss_Borcherding) <- "seurat_clusters"

# Vector of genes to plot
up_genes <- c("CLIC1", "COX5A","GTSF1", "MAD2L1","MYBL2","MYL6B","NME1","PLK1", "PYCR1", "SLC25A5", "SRI", "TUBA1C", "UBE2T", "YWHAH")

# DotPlot with custom firebrick-red gradient
DotPlot(ss_Borcherding, features = up_genes) +
  RotatedAxis() +
  scale_color_gradient2(low = "lightyellow", mid = "red", high = "firebrick", midpoint = 1) +
  ggtitle("Expression of Upregulated Genes in Sézary Syndrome") +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    axis.text.y = element_text(size = 12),
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14)
  )

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Visualization of Potential biomarkers-Downregulated

Idents(ss_Borcherding) <- "sample"
# Downregulated genes
down_genes <- c("TXNIP", "RASA3", "RIPOR2", 
                "ZFP36", "ZFP36L1", "ZFP36L2",
                "PRMT2", "MAX", "PIK3IP1", 
                "BTG1", "CDKN1B")

# DotPlot with firebrick color for high expression
DotPlot(ss_Borcherding, features = down_genes) +
  RotatedAxis() +
  scale_color_gradient2(low = "lightyellow", mid = "red", high = "firebrick", midpoint = 1) +
  ggtitle("Expression of Downregulated Genes in Sézary Syndrome") +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    axis.text.y = element_text(size = 12),
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14)
  )

Warning: The following requested variables were not found: RIPOR2Warning: Scaling data with a low number of groups may produce misleading resultsScale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Idents(ss_Borcherding) <- "seurat_clusters"

# DotPlot with firebrick color for high expression
DotPlot(ss_Borcherding, features = down_genes) +
  RotatedAxis() +
  scale_color_gradient2(low = "lightyellow", mid = "red", high = "firebrick", midpoint = 1) +
  ggtitle("Expression of Downregulated Genes in Sézary Syndrome") +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    axis.text.y = element_text(size = 12),
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14)
  )

Warning: The following requested variables were not found: RIPOR2Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Save the Seurat object as an RDS

saveRDS(ss_Borcherding, file = "data/ss_Borcherding_Malignant_Normal.rds")