unCTC Study 2 (TPM)

Include libraries

library(Linnorm)
library(SingleCellExperiment)
library(GSVA)
library(ggplot2)
library(factoextra)
library(NbClust)
library(reticulate)
library(PCAtools)
library(umap)
library(dplyr)
library(infercnv)
library(readtext)
library(D3GB)
library(GenomicRanges)
library(IRanges)
library(S4Vectors)
library(ggpubr)

Source all the scripts

source("~/Supplemental_code/unCTC/CreateSingleCellObject.R")
source("~/Supplemental_code/unCTC/DDLK_Clust.R")
source("~/Supplemental_code/unCTC/Gene_Violin_plots.R")
source("~/Supplemental_code/unCTC/PathwayEnrichmentScore.R")
source("~/Supplemental_code/unCTC/Stouffer_score.R")
source("~/Supplemental_code/unCTC/unCTC_pathway_plots.R")
source("~/Supplemental_code/unCTC/unCTC_libraries.R")
source("~/Supplemental_code/unCTC/GroupsDiffGenes.R")
source("~/Supplemental_code/unCTC/GroupsDiffPathways.R")
source("~/Supplemental_code/unCTC/CNV_alterations.R")

Load datasets for Study 2

#Create Expression data list
load("~/Supplemental_code/unCTC_datasets/Poonia_et_al._TPMData.RData")
load("~/Supplemental_code/unCTC_datasets/Poonia_et_al._metaData.RData")
load("~/Supplemental_code/unCTC_datasets/Ding_et_al._WBC1_metaData.RData")
load("~/Supplemental_code/unCTC_datasets/Ding_et_al._WBC1_TPMData.RData")
load("~/Supplemental_code/unCTC_datasets/Ding_et_al._WBC2_TPMData.RData")
load("~/Supplemental_code/unCTC_datasets/Ding_et_al._WBC2_metaData.RData")
load("~/Supplemental_code/unCTC_datasets/Ebright_et_al._TPMData.RData")
load("~/Supplemental_code/unCTC_datasets/Ebright_et_al._metaData.RData")

Load geneset

This package includes one geneset, which is taken from molecular signature database.

load("~/Supplemental_code/unCTC_datasets/c2.all.v7.2.symbols.RData")

#Create Expression data list
dataList = list(Poonia_et_al._TPMData,Ebright_et_al._TPMData,
                Ding_et_al._WBC1_TPMData,Ding_et_al._WBC2_TPMData)

#Create Data Id's list
dataId = list("Poonia_et_al._TPMData","Ebright_et_al._TPMData",
              "Ding_et_al._WBC1_TPMData","Ding_et_al._WBC2_TPMData")

#Create Meta data list
MetaData = list(Poonia_et_al._metaData, Ebright_et_al._metaData, 
                Ding_et_al._WBC1_metaData, Ding_et_al._WBC2_metaData )

#Genesets given with this package
genesets = c2.all.v7.2.symbols

Calculate pathway enrichment score

PathwayEnrichmentScore uses the following steps:

Integrate data passed in the list based on common genes.

• Filter out low expressed genes and cells.
  
• Use Linnorm.Norm() for normalization and batch effect correction.
  
• Create singleCellObject instance.
  
• Calculate pathway enrichment score.
  
• Store metadata.
  
• Return pathway enrichment score and pathway metadata.

PathwayEnrichmentScore requires following inputs:
* data_list: List of expression data matrices
* data_id: List of expression data matrices’ name in the same order. * Genesets: List of pathways * min.size: Minimum size of genes in pathways/Genesets, Default is 10
* max.size: Maximum size of genes in pathways/Genesets, Default is 500
* min_Sample: filter out genes which are not expressedin at least min_Sample cells, Default is 5.
* min_Gene: Filter out those cells which do not express at least min_Gene genes, Default is 1500. * Parallel_threads : Number of threads in parallel to execute process

invisible({capture.output({

Pathway_score = unCTC::PathwayEnrichmentScore(data_list =dataList,
                                        data_id = dataId,
                                        Genesets = genesets,
                                        min.size=10,
                                        max.size = 500,
                                        min_Sample = 5,
                                        min_Gene = 1500,
                                       Parallel_threads=8L)
})})

Calculate the optimal number of clusters for pathway enrichment score matrix

For the above pathway enrichment score matrix, we calculate the number of clusters using the Elbow method.

library(factoextra)
library(NbClust)
fviz_nbclust(Pathway_score$Pathway_score, kmeans, method = "wss") +
    geom_vline(xintercept = 4, linetype = 2)+
    labs(subtitle = "Elbow method")

DDLK Clusteing

DDLk_Clust need the following inputs

• PathwayScore: Pathway enrichment score matrix. We get this from PathwayEnrichmentScore_output$Pathway_score
  
• PathwayMetaData: Pathways metadata. we get this from PathwayEnrichmentScore_output$Pathway_metadata
  
• n: Number of clusters for K-means clustering. We can determine the optimal number of clusters for pathway enrichment score matrix using the Elbow method, Average silhouette method, Gap statistic method.
  
• out.dir: Directory to save enrichment score. This input is mandatory. Default is your current directory.
  
• MetaData: list of metadata for all expression matrices. This input is optional. Only give if you have the same columns in all metadata matrices.

#Retrive information about the version of python being used by reticulate
#reticulate::py_config()
#If version is different from the the given path then restart session and
#give path again can change path

DDLK_Clusters = DDLK_Clust(PathwayScore = Pathway_score$Pathway_score,
                           PathwayMetaData = Pathway_score$Pathway_metadata,
                           n = 4,
                           out.dir = getwd(),
                           MetaData = MetaData
                           )

unCTC plots:

unCTC_plots Plots principal components of pathway enrichment score.

• Return list of three plots:group_by_Class, group_by_Cluster,p1_p5_Pairssplot

Required input for unCTC_plots method is:

• PathwayScore: Pathway enrichment score matrix. We get this from PathwayEnrichmentScore_output$Pathway_score.
  
• PathwayMetaData: Pathways metadata. we get this from PathwayEnrichmentScore_output$Pathway_metadata.
  
• colorby: Any column name from PathwayMetaData, default is “Data_id”.
  
• Color_cluster: Any column name from PathwayMetaData, default is “Clusters”.
  
• pairsplotLegend: Legend Position in pairsplot. Choose one from “left”,“right” and “none”

plots = unCTC_pathway_plots(Pathway_score = DDLK_Clusters$Pathway_score,
                    Pathway_metadata = DDLK_Clusters$PathwayDDLK_clust,
                    colorby = "Data_id",
                    Color_cluster = "Clusters",
                    pairsplotLegend = "none")

PCA plots

plots$group_by_Class_PCA

plots$group_by_Cluster_PCA

UMAP plots

plots$group_by_Class_umap

plots$group_by_Cluster_umap

• stacked bar plot shows the count of CTCs (red) and WBCs (blue), while the x-axis shows clusters.

library(ggplot2)
ggplot(DDLK_Clusters$PathwayDDLK_clust, aes(x=Clusters, fill = Cell_type))+
       theme_classic()+
       geom_bar(stat="count")+
       scale_color_manual()+
       scale_fill_manual(
       values = c("dodgerblue4","firebrick3","darkgreen","dark turquoise"))

Differential genes

Provide differential genes between given groups.

Differential_genes require following inputs:

• data_list: List of expression data matrices
  
• min_Sample: filter out genes which are not expressedin at least min_Sample cells, Default is 5
  
• min_Gene: Filter out those cells which do not express at least min_Gene genes, Default is 1500.
  
• DDLK_Clusters: output of DDLK_Clust()
  
• data_id: List of expression data matrices name in same order.
  
• data_type: choose from given vector,c(“Normalised”,“Raw”).
• DifferentiateBy: Default is Clusters. We can choose any column name from DDLK_Clusters$PathwayDDLK_clust
  
• p_val = Threshold p-value, Default is 0.05
• lfc = Threshold log fold change value, Default is 0
• up_gene_number = Select number of upregulated genes you want to obtain. Default is 10. If you get an error when computing the number of upregulated genes, relax the p val parameter.

Diff_matrix = unCTC::Differential_genes(data_list=dataList,
                                 min_Sample = 5,
                                 min_Gene = 1500,
                                 DDLK_Clusters,
                                 Genesets = genesets,
                                 data_id=dataId,
                                 data_type = "Normalised",
                                 DifferentiateBy = "Clusters",
                                 up_gene_number = 200)

Heatmap showing the top 200 upregulated genes in the 4 clusters.

library(pheatmap)
library(viridis)
annotation = Diff_matrix$annotations
annotation$Data_id <- NULL
annotation$GroupID <- NULL
annotation$Cell_type <- NULL
ann = annotation[,c("HormoneStatus","Class","Clusters")] 

pheatmap(t(scale(t(Diff_matrix$DiffMat))),cluster_cols = FALSE,
         show_colnames = FALSE,cluster_rows = FALSE, show_rownames = FALSE,
         color = viridis(1000),annotation = ann)

Differential Pathways

Provide differential pathways between given groups.

Differential_pathways require following inputs:

• Pathway_score: Output of PathwayEnrichmentScore.R method
  
• DDLK_Clusters: output of DDLK_Clust.R method
  
• DifferentiateBy: Default is Clusters. We can choose any column name from DDLK_Clusters$PathwayDDLK_clust
  
• p_val = Threshold p-value, Default is 0.05
• lfc = Threshold log fold change value, Default is 0
• up_pathways_number = Select number of upregulated pathways you want to obtain. Default is 10. If you get an error when computing the number of upregulated pathways, relax the p val parameter.

Diff_path = unCTC::Differential_pathways(Pathway_score,
                      DDLK_Clusters = DDLK_Clusters,
                      DifferentiateBy = "Clusters",
                      up_pathways_number = 100
                      )

Out of top 100 upregulated pathways in each cluster we select relevent pathways from each cluster

annotation = Diff_path$annotations
annotation$Data_id <- NULL
annotation$GroupID <- NULL
annotation$Cell_type <- NULL
ann = annotation[,c("HormoneStatus","Class","Clusters")] 
Specific_pathways = c("BIOCARTA_THELPER_PATHWAY","BIOCARTA_TCYTOTOXIC_PATHWAY","BIOCARTA_CTL_PATHWAY","BIOCARTA_IL17_PATHWAY","BIOCARTA_CTLA4_PATHWAY","LEE_DIFFERENTIATING_T_LYMPHOCYTE","BIOCARTA_MONOCYTE_PATHWAY",
"ZHENG_FOXP3_TARGETS_IN_T_LYMPHOCYTE_DN","SPIELMAN_LYMPHOBLAST_EUROPEAN_VS_ASIAN_2FC_DN","GUTIERREZ_CHRONIC_LYMPHOCYTIC_LEUKEMIA_UP","GOERING_BLOOD_HDL_CHOLESTEROL_QTL_CIS",
"FARMER_BREAST_CANCER_CLUSTER_6","TURASHVILI_BREAST_NORMAL_DUCTAL_VS_LOBULAR_UP","YANG_BREAST_CANCER_ESR1_BULK_UP","NIKOLSKY_BREAST_CANCER_19Q13.1_AMPLICON",
"GINESTIER_BREAST_CANCER_ZNF217_AMPLIFIED_UP","HOLLERN_SOLID_NODULAR_BREAST_TUMOR_UP","FINETTI_BREAST_CANCER_KINOME_RED","NIKOLSKY_BREAST_CANCER_7Q21_Q22_AMPLICON",
"YANG_BREAST_CANCER_ESR1_UP","GINESTIER_BREAST_CANCER_ZNF217_AMPLIFIED_UP","WP_MAMMARY_GLAND_DEVELOPMENT_PATHWAY_INVOLUTION_STAGE_4_OF_4","MACLACHLAN_BRCA1_TARGETS_UP",
"REACTOME_ERBB2_ACTIVATES_PTK6_SIGNALING","REACTOME_PI3K_EVENTS_IN_ERBB2_SIGNALING","REACTOME_PI3K_EVENTS_IN_ERBB4_SIGNALING","REACTOME_SHC1_EVENTS_IN_ERBB2_SIGNALING","REACTOME_GRB2_EVENTS_IN_ERBB2_SIGNALING")
mat = Diff_path$DiffMatpathway[Specific_pathways,]
pheatmap(t(scale(t(mat))),cluster_cols = FALSE,
         show_colnames = FALSE,cluster_rows = FALSE, show_rownames = TRUE, fontsize_row =7,fontsize = 7,
         color = viridis(1000),annotation = ann)

Calcuate Stouffers score

Stouffer_score method uses the following steps:

• Calculate Z score of log normalized data
  
• Calculate Stouffer score for the specific gene list
  
• Assign Stouffer score to different groups of data and results are shown in boxplot

The followings input are required to calculate Stouffer’s score:

• data_list: List of expression data matrices.
  
• min_Sample: filter out genes which are not expressedin at least min_Sample cells, Default is 5.
  
• min_Gene: Filter out those cells which do not express at least min_Gene genes, Default is 1500.
• data_id: List of expression data matrices name in the same order.
  
• gene_list: Specific genes in vector.
  
• MetaData: Optional, List of metadata of expression matrices. If given, then columns of all metadata in the list must be the same.
  
• metaColPos: default = 1,Only require if metadata is given. Position of the column in metadata for which we want to calculate and compare Stouffer score for given gene list.
  
• metaColName: default = “Class”, Only require if metadata is given.
  Name of the column in metadata for which we want to calculate and compare Stouffer score for given gene list.

With this package, we have given two types of gene list:

Load genelists

load("~/Supplemental_code/unCTC_datasets/Breast_elevated_genes.RData")
load("~/Supplemental_code/unCTC_datasets/Blood_specific_gene.RData")

Stouffer’s Score:

#Calculate Stouffer's score for Blood gene
S_WBC = Stouffer_score(data_list = dataList,
                         min_Sample = 5,
                         min_Gene = 1500,
                         gene_list =Blood_specific_gene,
                         data_id = dataId,
                         Groupby = "Clusters",
                         DDLKCluster_data = DDLK_Clusters)


#Calculate Stouffer's score for Breast elevated genes
S_Breast = Stouffer_score(data_list = dataList,
                           min_Sample = 5,
                           min_Gene = 1500,
                           gene_list = Breast_elevated_genes,
                           data_id = dataId,
                           Groupby = "Clusters",
                           DDLKCluster_data = DDLK_Clusters)

Stouffer’s Score Plot

For better colour visualization we are using following color key:

library(ggplot2)
library(ggpubr)
ColorKey = c("darkred","deepskyblue3","darkolivegreen4",
             "dark turquoise","pale violet red",
             "steelblue","forestgreen","gray2",
             "gray50","hotpink","lightslateblue",
             "tan4","yellow3","sienna4","orchid4")

For Immune genes:

ggplot(S_WBC$Stouffer_score,aes(x=Clusters,y= Stouffer_score,fill=Clusters))+
geom_boxplot(outlier.shape = NA) + theme_classic() +
scale_fill_manual(values = ColorKey) +
ggtitle("Immune gene signature")+
stat_compare_means(comparisons = S_WBC$comparisons,
label = "p.signif", method = "t.test",ref.group = ".all.")

For Breast elevated genes:

ggplot(S_Breast$Stouffer_score,aes(x=Clusters,y= Stouffer_score,fill=Clusters))+
geom_boxplot(outlier.shape = NA) + theme_classic() +
scale_fill_manual(values = ColorKey)+
ggtitle("Breast elevated gene signature")+
stat_compare_means(comparisons = S_Breast$comparisons,
label = "p.signif", method = "t.test",ref.group = ".all.")

Copy Number Variation Analysis:

inferCNV R package is used for analysing copy number variation for raw Count/TPM data. Along with all analysis of inferCNV, unCTC::CNV_alterations calculate addition and deletion position in p and q arms in test/cancerous/ diseased data as compared to reference/normal/healthy. To calculate p and q arm location from inferCNV events, we used GRCh37 cytoband information.

CNV_alterations require the following inputs:

• data_list: List of expression data matrices.
  
• data_id: List of expression data matrices name in the same order.
  
• path= path to save output files.
• min_Sample: filter out genes which are not expressedin at least min_Sample cells, Default is 5.
  
• min_Gene: Filter out those cells which do not express at least min_Gene genes, Default is 1500.
• GenePositionFile: Gene/chromosomal order file. This package includes genecode hg19 gene order file. Either you can use this or download it from outer sources.
  
• threads_no: Thread number for parallel processes, default is 8.
  
• MetaData: Optional, List of metadata of expression matrices. If given, then columns of all metadata in the list must be the same.
  
• Groupby: Any column name from MetaData, which we want to use as an annotation file. Only applicable if MetaData is given.
  
• Reference_name: Any cell type from the data_id list or any cell type from the column assign to Groupby.
  
• obs.title: Title of test/observation matrix. Default is “Observations”.
  
• ref.title: Title of reference matrix. Default is “References”.
  
• out.Filename: Output filename-prefix. Default is “inferCNV”.

Load gene order file

gencode_v19_gene_pos =unCTC::gencode_v19_gene_pos

Load data for copy number variation

load("~/Supplemental_code/unCTC_datasets/Poonia_et_al._PBMC_CountData.RData")
load("~/Supplemental_code/unCTC_datasets/Poonia_et_al._PBMC_metaData.RData")
load("~/Supplemental_code/unCTC_datasets/Poonia_et_al._CountData.RData")
load("~/Supplemental_code/unCTC_datasets/Poonia_et_al._metaData.RData")

InferCNV between Poonia et al.’s CTCs and WBCs

Here we are not evaluating copy no variation script and only including results.

ref_data =Poonia_et_al._PBMC_CountData
ref_metadata = Poonia_et_al._PBMC_metaData
obs_data = Poonia_et_al._CountData
obs_metadata = Poonia_et_al._metaData
dataList1 = list(ref_data,obs_data)
dataId1 = list("WBC","CTC")
MetaData1= list(ref_metadata,obs_metadata)

CNV_Alterations1 = CNV_alterations(
                                    data_list= dataList1,
                                    data_id= dataId1,
                                    min_Sample = 5,
                                    min_Gene = 1500,
                                    path= getwd(),
                                    GenePositionFile= gencode_v19_gene_pos,
                                    threads_no=8,
                                    MetaData = MetaData1,
                                    Groupby = "GroupID",
                                    Reference_name = c("WBC"),
                                    obs.title ="Observations",
                                    ref.title = "References",
                                    cluster_by_groups = TRUE,
                                    out.Filename = "inferCNV"
                                    )

InferCNV by taking Ebright et al.’s CTCs as observation

load("/home/saritap/unCTC_datasets/GSE181279_Countdata.RData")
ref_data = GSE181279_Countdata
load("/home/saritap/unCTC_datasets/Ebright_et_al._CountData.RData")
obs_data = Ebright_et_al._CountData

dataList = list(ref_data,obs_data)
dataId = list("WBC","CTC")


CNV_Alterations2 = CNV_alterations(
                     data_list= dataList,
                     data_id= dataId,
                     min_Sample = 5,
                     min_Gene = 1500,
                     path= getwd(),      
                     GenePositionFile= gencode_v19_gene_pos,  
                     threads_no=8, 
                     Groupby = "Data_id",
                     Reference_name = c("WBC"), # WBC data as reference
                     obs.title ="Observations", 
                     ref.title = "References",
                     out.Filename = "inferCNV1" 
                     )

Gene_Violin_plots

Give violin plot for a given Canonical marker expression.

Gene_Violin_plots require input:
* data_list: List of expression data matrices
* data_id: List of expression data matrices name in the same order.
* min_Sample: filter out genes which are not expressedin at least min_Sample cells, Default is 5.
* min_Gene: Filter out those cells which do not express at least min_Gene genes, Default is 1500. * gene_symbol: Specific gene for which we want to see expression.
* MetaData: Optional, list of metadata of expression matrices. If given then columns of all metadata in the list must be identical.
* Groupby: Any column name from MetaData, which we want to use to see differential expression of the gene. Default is “data_id”.

# Gene Violin plot
PTPRC = Gene_Violin_plots(data_list =dataList,
                  data_id = dataId,
                  min_Sample = 5,
                  min_Gene = 1500,
                  gene_symbol = "PTPRC",
                  DDLKCluster_data = DDLK_Clusters$PathwayDDLK_clust,
                  Groupby = "Clusters")
NKG7 = Gene_Violin_plots(data_list =dataList,
                  data_id = dataId,
                  min_Sample = 5,
                  min_Gene = 1500,
                  gene_symbol = "NKG7",
                  DDLKCluster_data = DDLK_Clusters$PathwayDDLK_clust,
                  Groupby = "Clusters")

EPCAM = Gene_Violin_plots(data_list =dataList,
                  data_id = dataId,
                  min_Sample = 5,
                  min_Gene = 1500,
                  gene_symbol = "EPCAM",
                  DDLKCluster_data = DDLK_Clusters$PathwayDDLK_clust,
                  Groupby = "Clusters")
KRT18 = Gene_Violin_plots(data_list =dataList,
                  data_id = dataId,
                  min_Sample = 5,
                  min_Gene = 1500,
                  gene_symbol = "KRT18",
                  DDLKCluster_data = DDLK_Clusters$PathwayDDLK_clust,
                  Groupby = "Clusters")

Canonical marker expression

library(cowplot)
PTPRC$Violin_plot

NKG7$Violin_plot

EPCAM$Violin_plot

KRT18$Violin_plot

ARI, NMI and Cluster purity

aricode::ARI(DDLK_Clusters$PathwayDDLK_clust$Clusters,DDLK_Clusters$PathwayDDLK_clust$Cell_type)

## [1] 0.570144

aricode::NMI(DDLK_Clusters$PathwayDDLK_clust$Clusters,DDLK_Clusters$PathwayDDLK_clust$Cell_type)

## [1] 0.47599

ClusterPurity <- function(clusters, classes) {
  sum(apply(table(classes, clusters), 2, max)) / length(clusters)
}
ClusterPurity(DDLK_Clusters$PathwayDDLK_clust$Clusters,DDLK_Clusters$PathwayDDLK_clust$Cell_type)

## [1] 0.982823