Guided tutorial
8 November 2022
Package
COTAN 1.2.0
Contents
library(COTAN)
library(data.table)
library(Matrix)
library(ggrepel)
#> Loading required package: ggplot2
library(factoextra)
#> Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(Rtsne)
library(utils)
library(plotly)
#>
#> Attaching package: 'plotly'
#> The following object is masked from 'package:ggplot2':
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#> last_plot
#> The following object is masked from 'package:stats':
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#> filter
#> The following object is masked from 'package:graphics':
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#> layout
library(tidyverse)
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library(htmlwidgets)
library(MASS)
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#> select
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library(dendextend)
#>
#> ---------------------
#> Welcome to dendextend version 1.16.0
#> Type citation('dendextend') for how to cite the package.
#>
#> Type browseVignettes(package = 'dendextend') for the package vignette.
#> The github page is: https://github.com/talgalili/dendextend/
#>
#> Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
#> You may ask questions at stackoverflow, use the r and dendextend tags:
#> https://stackoverflow.com/questions/tagged/dendextend
#>
#> To suppress this message use: suppressPackageStartupMessages(library(dendextend))
#> ---------------------
#>
#> Attaching package: 'dendextend'
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#> set
#> The following object is masked from 'package:stats':
#>
#> cutreemycolours <- c("A" = "#8491B4B2","B"="#E64B35FF")
my_theme = theme(axis.text.x = element_text(size = 14, angle = 0, hjust = .5, vjust = .5, face = "plain", colour ="#3C5488FF" ),
axis.text.y = element_text( size = 14, angle = 0, hjust = 0, vjust = .5, face = "plain", colour ="#3C5488FF"),
axis.title.x = element_text( size = 14, angle = 0, hjust = .5, vjust = 0, face = "plain", colour ="#3C5488FF"),
axis.title.y = element_text( size = 14, angle = 90, hjust = .5, vjust = .5, face = "plain", colour ="#3C5488FF"))
data_dir = tempdir()This is just an example on a toy dataset. If the user want to try on a real dataset it can be used the previous command to download it and the next commented line to use it as input data. There are also available online many other examples at link.
data("ERCCraw", package = "COTAN")
ERCCraw = as.data.frame(ERCCraw)
rownames(ERCCraw) = ERCCraw$V1
ERCCraw = ERCCraw[,2:ncol(ERCCraw)]
ERCCraw[1:5,1:5]
#> AAACCGTGAAGCCT.1 AAACGCACTGCTGA.1 AAACTTGACCGAAT.1 AAAGACGAGGAAGC.1
#> ERCC-00002 1662 4036 3919 4124
#> ERCC-00003 95 283 298 290
#> ERCC-00004 25 75 70 75
#> ERCC-00009 41 57 78 98
#> ERCC-00012 0 0 0 0
#> AAAGACGATCCGAA.1
#> ERCC-00002 4220
#> ERCC-00003 312
#> ERCC-00004 87
#> ERCC-00009 87
#> ERCC-00012 0Define a directory where the output will be stored.
out_dir <- paste0(tempdir(),"/")1 Analytical pipeline
Inizialise the COTAN object with the row count table and the metadata for the experiment.
obj = new("scCOTAN",raw = ERCCraw)
#obj = initRaw(obj,GEO="GSM2861514" ,sc.method="Drop_seq",cond = "mouse cortex E17.5")
obj = initRaw(obj,GEO="ERCC" ,sc.method="10X",cond = "negative ERCC dataset")
#> [1] "Initializing S4 object"Now we can start the cleaning. Analysis requires and starts from a matrix of raw UMI counts after removing possible cell doublets or multiplets and low quality or dying cells (with too high mtRNA percentage, easily done with Seurat or other tools).
If we do not want to consider the mitochondrial genes we can remove them before starting the analysis.
genes_to_rem = get.genes(obj)[grep('^mt', get.genes(obj))]
cells_to_rem = names(get.cell.size(obj)[which(get.cell.size(obj) == 0)])
obj = drop.genes.cells(obj,genes_to_rem,cells_to_rem )We want also to define a prefix to identify the sample.
#t = "E17.5_cortex"
t = "ERCC"
print(paste("Condition ",t,sep = ""))
#> [1] "Condition ERCC"
#--------------------------------------
n_cells = length(get.cell.size(object = obj))
print(paste("n cells", n_cells, sep = " "))
#> [1] "n cells 1015"
n_it = 11.1 Data cleaning
First we create the directory to store all information regarding the data cleaning.
if(!file.exists(out_dir)){
dir.create(file.path(out_dir))
}
if(!file.exists(paste(out_dir,"cleaning", sep = ""))){
dir.create(file.path(out_dir, "cleaning"))
}ttm = clean(obj)
#> [1] "Start estimation mu with linear method"
#> [1] 88 1015
#> [1] "Start PCA"
#> [1] "starting hclust"
obj = ttm$object
ttm$pca.cell.2
Run this when B cells need to be removed.
pdf(file.path(out_dir,"cleaning",paste(t,"_",n_it,"_plots_before_cells_exlusion.pdf", sep = "")))
ttm$pca.cell.2
ggplot(ttm$D, aes(x=n,y=means)) + geom_point() +
geom_text_repel(data=subset(ttm$D, n > (max(ttm$D$n)- 15) ), aes(n,means,label=rownames(ttm$D[ttm$D$n > (max(ttm$D$n)- 15),])),
nudge_y = 0.05,
nudge_x = 0.05,
direction = "x",
angle = 90,
vjust = 0,
segment.size = 0.2)+
ggtitle("B cell group genes mean expression")+my_theme +
theme(plot.title = element_text(color = "#3C5488FF", size = 20, face = "italic",vjust = - 5,hjust = 0.02 ))
dev.off()
if (length(ttm$cl1) < length(ttm$cl2)) {
to_rem = ttm$cl1
}else{
to_rem = ttm$cl2
}
n_it = n_it+1
obj = drop.genes.cells(object = obj,genes = c(),cells = to_rem)
gc()
ttm = clean(obj)
#ttm = clean.sqrt(obj, cells)
obj = ttm$object
ttm$pca.cell.2
Run this only in the last iteration, instead the previous code, when B cells group has not to be removed
pdf(file.path(out_dir,"cleaning",paste(t,"_",n_it,"_plots_before_cells_exlusion.pdf", sep = "")))
ttm$pca.cell.2
ggplot(ttm$D, aes(x=n,y=means)) + geom_point() +
geom_text_repel(data=subset(ttm$D, n > (max(ttm$D$n)- 15) ), aes(n,means,label=rownames(ttm$D[ttm$D$n > (max(ttm$D$n)- 15),])),
nudge_y = 0.05,
nudge_x = 0.05,
direction = "x",
angle = 90,
vjust = 0,
segment.size = 0.2)+
ggtitle(label = "B cell group genes mean expression", subtitle = " - B group NOT removed -")+my_theme +
theme(plot.title = element_text(color = "#3C5488FF", size = 20, face = "italic",vjust = - 10,hjust = 0.02 ),
plot.subtitle = element_text(color = "darkred",vjust = - 15,hjust = 0.01 ))
dev.off()
#> png
#> 2To color the pca based on nu_j (so the cells’ efficiency)
nu_est = round(get.nu(object = obj), digits = 7)
plot.nu <-ggplot(ttm$pca_cells,aes(x=PC1,y=PC2, colour = log(nu_est)))
plot.nu = plot.nu + geom_point(size = 1,alpha= 0.8)+
scale_color_gradient2(low = "#E64B35B2",mid = "#4DBBD5B2", high = "#3C5488B2" ,
midpoint = log(mean(nu_est)),name = "ln(nu)")+
ggtitle("Cells PCA coloured by cells efficiency") +
my_theme + theme(plot.title = element_text(color = "#3C5488FF", size = 20),
legend.title=element_text(color = "#3C5488FF", size = 14,face = "italic"),
legend.text = element_text(color = "#3C5488FF", size = 11),
legend.key.width = unit(2, "mm"),
legend.position="right")
pdf(file.path(out_dir,"cleaning",paste(t,"_plots_PCA_efficiency_colored.pdf", sep = "")))
plot.nu
dev.off()
#> png
#> 2
plot.nu
The next part is use to remove the cells with efficiency too low.
nu_df = data.frame("nu"= sort(get.nu(obj)), "n"=c(1:length(get.nu(obj))))
ggplot(nu_df, aes(x = n, y=nu)) +
geom_point(colour = "#8491B4B2", size=1)+
my_theme #+ ylim(0,1) + xlim(0,70)
We can zoom on the smallest values and, if we detect a clear elbow, we can decide to remove the cells.
yset = 0.4#threshold to remove low UDE cells
plot.ude <- ggplot(nu_df, aes(x = n, y=nu)) +
geom_point(colour = "#8491B4B2", size=1) +
my_theme + ylim(0,1) + xlim(0,400) +
geom_hline(yintercept=yset, linetype="dashed", color = "darkred") +
annotate(geom="text", x=200, y=0.25,
label=paste("to remove cells with nu < ",yset,sep = " "),
color="darkred", size=4.5)
pdf(file.path(out_dir,"cleaning",paste(t,"_plots_efficiency.pdf", sep = "")))
plot.ude
#> Warning: Removed 668 rows containing missing values (geom_point).
dev.off()
#> png
#> 2
plot.ude
#> Warning: Removed 668 rows containing missing values (geom_point).
We also save the defined threshold in the metadata and re run the estimation
obj = add.row.to.meta(obj,c("Threshold low UDE cells:",yset))
to_rem = rownames(nu_df[which(nu_df$nu < yset),])
obj = drop.genes.cells(object = obj, genes = c(),cells = to_rem)Repeat the estimation after the cells are removed
ttm = clean(obj)
#> [1] "Start estimation mu with linear method"
#> [1] 88 1004
#> [1] "Start PCA"
#> [1] "starting hclust"
obj = ttm$object
ttm$pca.cell.2
In case we do not want to remove anything, we can run:
pdf(file.path(out_dir,"cleaning",paste(t,"_plots_efficiency.pdf", sep = "")))
ggplot(nu_df, aes(x = n, y=nu)) + geom_point(colour = "#8491B4B2", size=1) +my_theme + #xlim(0,100)+
annotate(geom="text", x=50, y=0.25, label="nothing to remove ", color="darkred")
dev.off()
#> png
#> 2Just to check again, we plot the final efficiency colored PCA
nu_est = round(get.nu(object = obj), digits = 7)
plot.nu <-ggplot(ttm$pca_cells,aes(x=PC1,y=PC2, colour = log(nu_est)))
plot.nu = plot.nu + geom_point(size = 2,alpha= 0.8)+
scale_color_gradient2(low = "#E64B35B2",mid = "#4DBBD5B2", high = "#3C5488B2" ,
midpoint = log(mean(nu_est)),name = "ln(nu)")+
ggtitle("Cells PCA coloured by cells efficiency: last") +
my_theme + theme(plot.title = element_text(color = "#3C5488FF", size = 20),
legend.title=element_text(color = "#3C5488FF", size = 14,face = "italic"),
legend.text = element_text(color = "#3C5488FF", size = 11),
legend.key.width = unit(2, "mm"),
legend.position="right")
pdf(file.path(out_dir,"cleaning",paste(t,"_plots_PCA_efficiency_colored_FINAL.pdf", sep = "")))
plot.nu
dev.off()
#> png
#> 2
plot.nu
1.2 COTAN analysis
In this part all the contingency tables are computed and used to get the statistics (S) To storage efficiency of all the observed tables only the yes_yes is stored. Note that if will be necessary re-computing the yes-yes table, the yes-yes table should be cancelled before running cotan_analysis.
obj = cotan_analysis(obj,cores = 2)
#> [1] "cotan analysis"
#> [1] "mu estimator creation"
#> [1] "save effective constitutive genes"
#> [1] "start a minimization"
#> [1] "Final trance!"
#> [1] "a min: -0.147216796875 | a max 13.5546875 | negative a %: 26.984126984127"COEX evaluation and storing
obj = get.coex(obj)
#> [1] "coex dataframe creation"
#> [1] "creation of observed yes/yes contingency table"
#> [1] "mu estimator creation"
#> [1] "expected contingency tables creation"
#> [1] "The distance between estimated n of zeros and observed number of zero is 0.00413702026069726 over 63"
#> [1] "Done"
#> [1] "coex estimation"
#> [1] "Cleaning RAM"
# saving the structure
saveRDS(obj,file = paste(out_dir,t,".cotan.RDS", sep = ""))2 Analysis of the elaborated data
2.1 GDI
The next function can directly plot the GDI for the dataset with the 1.5 threshold (in red) and the two higher quantiles (in blue).
plot_GDI(obj, cond = "ERCC")
#> [1] "GDI plot "
#> [1] "function to generate GDI dataframe"
#> [1] "Using S"
#> [1] "function to generate S "
If a more complex plot is needed, or if we want to analyze more in detail the GDI data, we can get directly the GDI dataframe.
quant.p = get.GDI(obj)
#> [1] "function to generate GDI dataframe"
#> [1] "Using S"
#> [1] "function to generate S "
head(quant.p)
#> sum.raw.norm GDI exp.cells
#> ERCC-00012 3.175703 1.1614957 2.390438
#> ERCC-00013 5.995188 1.3512649 27.788845
#> ERCC-00014 6.077256 1.3473405 34.860558
#> ERCC-00016 3.569692 1.1518864 3.685259
#> ERCC-00017 1.609829 0.7957451 0.498008
#> ERCC-00019 7.157739 1.4853109 66.135458In the third column of this dataframe is reported the percentage of cells expressing the gene.
Next we can define some gene sets (in this case three) that we want to specifically label in the GDI plot.
AA=c("ERCC-00012","ERCC-00013","ERCC-00014")
BB=c("ERCC-00016","ERCC-00017","ERCC-00019")
CC=c("ERCC-00022","ERCC-00024","ERCC-00028")
text.size = 12
quant.p$highlight = with(quant.p, ifelse(rownames(quant.p) %in% AA, "AA",
ifelse(rownames(quant.p) %in% CC,"Constitutive" ,
ifelse(rownames(quant.p) %in% BB,"BB" , "normal"))))
textdf <- quant.p[rownames(quant.p) %in% c(AA,CC,BB), ]
mycolours <- c("Con" = "#00A087FF","AA"="#E64B35FF","BB"="#F39B7FFF","normal" = "#8491B4B2")
f1 = ggplot(subset(quant.p,highlight == "normal" ), aes(x=sum.raw.norm, y=GDI)) + geom_point(alpha = 0.1, color = "#8491B4B2", size=2.5)
GDI_plot = f1 + geom_point(data = subset(quant.p,highlight != "normal" ), aes(x=sum.raw.norm, y=GDI, colour=highlight),size=2.5,alpha = 0.8) +
geom_hline(yintercept=quantile(quant.p$GDI)[4], linetype="dashed", color = "darkblue") +
geom_hline(yintercept=quantile(quant.p$GDI)[3], linetype="dashed", color = "darkblue") +
geom_hline(yintercept=1.5, linetype="dotted", color = "red", size= 0.5) +
scale_color_manual("Status", values = mycolours) +
scale_fill_manual("Status", values = mycolours) +
xlab("log normalized counts")+ylab("GDI")+
geom_label_repel(data = textdf , aes(x=sum.raw.norm, y=GDI, label = rownames(textdf),fill=highlight),
label.size = NA,
alpha = 0.5,
direction ="both",
na.rm=TRUE,
seed = 1234) +
geom_label_repel(data =textdf , aes(x=sum.raw.norm, y=GDI, label = rownames(textdf)),
label.size = NA,
segment.color = 'black',
segment.size = 0.5,
direction = "both",
alpha = 0.8,
na.rm=TRUE,
fill = NA,
seed = 1234) +
theme(axis.text.x = element_text(size = text.size, angle = 0, hjust = .5, vjust = .5, face = "plain", colour ="#3C5488FF" ),
axis.text.y = element_text( size = text.size, angle = 0, hjust = 0, vjust = .5, face = "plain", colour ="#3C5488FF"),
axis.title.x = element_text( size = text.size, angle = 0, hjust = .5, vjust = 0, face = "plain", colour ="#3C5488FF"),
axis.title.y = element_text( size = text.size, angle = 90, hjust = .5, vjust = .5, face = "plain", colour ="#3C5488FF"),
legend.title = element_blank(),
legend.text = element_text(color = "#3C5488FF",face ="italic" ),
legend.position = "bottom")
legend <- cowplot::get_legend(GDI_plot)
GDI_plot =GDI_plot + theme(
legend.position = "none")
GDI_plot
2.2 Heatmaps
For the Gene Pair Analysis, we can plot an heatmap with the coex values between two genes sets. To do so we need to define, in a list, the different gene sets (list.genes). Then in the function parameter sets we can decide which sets need to be considered (in the example from 1 to 3). In the condition parameter we should insert an array with each file name prefix to be considered (in the example, the file names is “E17.5_cortex”).
list.genes = list("Ref.col"= BB, "AA"=AA, "Const."=CC )
plot_heatmap(df_genes = list.genes,sets = c(1:3),conditions = "ERCC",dir = out_dir)
#> [1] "plot heatmap"
#> [1] "Loading condition ERCC"
#> [1] "ERCC-00016" "ERCC-00017" "ERCC-00019"
#> [1] "Get p-values on a set of genes on columns on a set of genes on rows"
#> [1] "Using function S"
#> [1] "function to generate S "
#> [1] "Ref.col"
#> [1] "AA"
#> [1] "Const."
#> [1] "min coex: 0 max coex 0.982460923156275"
We can also plot a general heatmap of coex values based on some markers like the following one.
plot_general.heatmap(prim.markers = c("ERCC-00014","ERCC-00019"), condition = "ERCC",dir = out_dir, p_value = 0.05)
#> [1] "ploting a general heatmap"
#> NULL
#> [1] "Get p-values genome wide on columns genome wide on rows"
#> [1] "Using function S"
#> [1] "function to generate S "
plot_general.heatmap(prim.markers = c("ERCC-00014","ERCC-00019"),markers.list =c("ERCC-00084" ,"ERCC-00085" ,"ERCC-00086") ,symmetric = FALSE, condition = "ERCC",dir = out_dir, p_value = 0.05)
#> [1] "ploting a general heatmap"
#> NULL
#> [1] "Get p-values genome wide on columns genome wide on rows"
#> [1] "Using function S"
#> [1] "function to generate S "
2.3 Get data tables
Sometimes we can also be interested in the numbers present directly in the contingency tables for two specific genes. To get them we can use two functions:
- get.observed.ct to produce the observed data
get.observed.ct(object = obj, g1 = "ERCC-00014",g2 = "ERCC-00019")
#> ERCC-00014.yes ERCC-00014.no
#> ERCC-00019.yes 241 423
#> ERCC-00019.no 109 231- get.expected.ct to produce the expected values
get.expected.ct(object = obj, g1 = "ERCC-00014",g2 = "ERCC-00019")
#> ERCC-00014.yes ERCC-00014.no
#> ERCC-00019.yes 235.7119 428.2888
#> ERCC-00019.no 114.2885 225.7108Another useful function is extract.coex. This can be used to extract the whole or a partial coex matrix from a COTAN object.
# For the whole matrix
coex <- extract.coex(object = obj)
coex[1:5,1:5]
#> ERCC-00012 ERCC-00013 ERCC-00014 ERCC-00016 ERCC-00017
#> ERCC-00012 0.769954876 0.03079731 -0.008469118 0.035409255 -0.004367265
#> ERCC-00013 0.030797310 0.98637914 0.020020333 -0.018534811 -0.013478967
#> ERCC-00014 -0.008469118 0.02002033 0.983490318 -0.003222142 0.094474696
#> ERCC-00016 0.035409255 -0.01853481 -0.003222142 0.982460923 0.028389361
#> ERCC-00017 -0.004367265 -0.01347897 0.094474696 0.028389361 0.185954347# For a partial matrix
coex <- extract.coex(object = obj,genes = c("ERCC-00017", "ERCC-00019", "ERCC-00024", "ERCC-00025", "ERCC-00028", "ERCC-00031"))
head(coex)
#> ERCC-00017 ERCC-00019 ERCC-00024 ERCC-00025 ERCC-00028
#> ERCC-00012 -0.004367265 5.272805e-02 -0.011980011 0.017574171 -0.0120737279
#> ERCC-00013 -0.013478967 4.360570e-02 -0.005497781 0.068917037 0.0310017944
#> ERCC-00014 0.094474696 2.345844e-02 -0.016243015 -0.006247842 -0.0226624019
#> ERCC-00016 0.028389361 7.232674e-05 -0.004746929 0.033126881 0.0005889499
#> ERCC-00017 0.185954347 1.880656e-02 -0.010473707 0.012328879 0.0162721054
#> ERCC-00019 0.018806561 9.695314e-01 -0.010600940 -0.032637961 0.0473851891
#> ERCC-00031
#> ERCC-00012 0.013066001
#> ERCC-00013 -0.020289792
#> ERCC-00014 -0.054916592
#> ERCC-00016 -0.012314141
#> ERCC-00017 -0.017522836
#> ERCC-00019 -0.003396497The next few lines are just to clean after compilation of the documentation.
if (file.exists(paste(out_dir,t,".cotan.RDS", sep = ""))) {
#Delete file if it exists
file.remove(paste(out_dir,t,".cotan.RDS", sep = ""))
}
#> [1] TRUE
unlink(paste(out_dir,"cleaning", sep = ""),recursive = TRUE)
print(sessionInfo())
#> R version 4.2.1 (2022-06-23)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.0
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] dendextend_1.16.0 MASS_7.3-58 htmlwidgets_1.5.4 forcats_0.5.1
#> [5] stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
#> [9] tidyr_1.2.0 tibble_3.1.7 tidyverse_1.3.1 plotly_4.10.0
#> [13] Rtsne_0.16 factoextra_1.0.7 ggrepel_0.9.1 ggplot2_3.3.6
#> [17] Matrix_1.4-1 data.table_1.14.2 COTAN_1.2.0 BiocStyle_2.26.0
#>
#> loaded via a namespace (and not attached):
#> [1] fs_1.5.2 matrixStats_0.62.0 lubridate_1.8.0
#> [4] doParallel_1.0.17 RColorBrewer_1.1-3 httr_1.4.3
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