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File	Version	Author	Date	Message
Rmd	8da2e31	tranmanhha135	2024-02-08	workflowr::wflow_publish(here::here("1_analysis/*.Rmd"))
Rmd	d8d23ee	tranmanhha135	2024-01-13	im on holiday
html	d8d23ee	tranmanhha135	2024-01-13	im on holiday
html	36aeb85	Ha Manh Tran	2024-01-13	Build site.
Rmd	a957cff	Ha Manh Tran	2024-01-13	workflowr::wflow_publish(here::here("1_analysis/*Rmd"))
Rmd	221e2fa	tranmanhha135	2024-01-10	fixed error
html	221e2fa	tranmanhha135	2024-01-10	fixed error
html	762020e	tranmanhha135	2024-01-09	Build site.
Rmd	c6d389f	tranmanhha135	2024-01-09	workflowr::wflow_publish(here::here("1_analysis/*.Rmd"))
Rmd	f2e3750	tranmanhha135	2024-01-08	completed DE
Rmd	05fa0b3	tranmanhha135	2024-01-06	added description

Data Setup

# working with data
library(readxl)
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
library(ComplexHeatmap)
library(scales)
library(plyr)

# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(pander)
library(cowplot)
library(pheatmap)
library(VennDiagram)
library(DT)
library(patchwork)
library(kableExtra)
library(extrafont)
loadfonts(device = "all")

# Custom ggplot
library(ggplotify)
library(ggpubr)
library(ggrepel)
library(viridis)

# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)

library(pandoc)
library(knitr)
opts_knit$set(progress = FALSE, verbose = FALSE)
opts_chunk$set(warning=FALSE, message=FALSE, echo=FALSE)

Import DGElist Data

DGElist object containing the raw feature count, sample metadata, and gene metadata, created in the Set Up stage.

Initial Parameterisation

The varying methods used to identify differential expression all rely on similar initial parameters. These include:

The Design Matrix,
Estimation of Dispersion, and
Contrast Matrix

Design Matrix

The experimental design can be parameterised in a one-way layout where one coefficient is assigned to each group. The design matrix formulated below contains the predictors of each sample

Contrast Matrix

The contrast matrix is required to provide a coefficient to each comparison and later used to test for significant differential expression with each comparison group

Limma-Voom

Apply voom transformation

Voom is used to estimate the mean-variance relationship of the data, which is then used to calculate and assign a precision weight for each of the observation (gene). This observational level weights are then used in a linear modelling process to adjust for heteroscedasticity. Log count (logCPM) data typically show a decreasing mean-variance trend with increasing count size (expression).

However, for some dataset with potential sample outliers, voomWithQualityWeights can be used to calculate sample-specific quality weights. The application of observational and sample-specific weights can objectively and systematically correct for outliers and better than manually removing samples in cases where there are no clear-cut reasons for replicate variations. Thus, linear model will be applied to the voom transformation with observational and sample-specific weights.

Observational level weights

Voom transformation with observational weights

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Observational & group level weights

Voom transformation with observational and group-specific weights

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Observational & sample level weights

Voom transformation with observational and sample-specific weights

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

PCA

PCA plots after voom quality weight transformations. Group-specific weights(left) and sample-specific weights (right)

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Apply linear model

When the list of DE genes is large, we can apply a fold change cut-off through application of TREAT to prioritise the genes with greater fold changes and potentially more biologically relevant. Ideally, we are aiming for ~300 genes \(\pm\) 100 genes. Functional enrichment analysis with this number of genes should generate meaningful results.

Importantly, the FC threshold used in TREAT should be chosen as a small value below which results should be ignored, instead of a target fold-change. In general, a modest fold-change of 1.1 - 1.5 is recommended. However, it is more important to select a fold-change cut-off that generates a sufficiently small list of DE genes.

A quick aside on the definition and interpretation of fold change and log2FC. A fold-change (FC) refers to the ratio of two values.

If there is a two fold increase (FC = 2, log2FC = 1) between A vs B, then A is twice as big as B (or A is 200% of B)
If there is a two fold decrease (FC = 0.5, log2FC = -1) between A vs B, then A is half as big as B (or A is 50% of B)

FC=none

TABLE 1: Number of significant DE genes with ‘none’ adjusment method, and at a p-value/adj.p-value of 0.01
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	454	648	285
NotSig	13935	13758	14148
Up	359	342	315

TABLE 2: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.1
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	254	503	60
NotSig	14302	14000	14624
Up	192	245	64

TABLE 3: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.05
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	137	352	16
NotSig	14507	14274	14703
Up	104	122	29

FC=1.1

TABLE 4: Number of significant DE genes with ‘none’ adjusment method, and at a p-value/adj.p-value of 0.01
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	365	531	163
NotSig	14159	14049	14435
Up	224	168	150

TABLE 5: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.1
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	151	364	6
NotSig	14518	14313	14728
Up	79	71	14

TABLE 6: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.05
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	76	264	2
NotSig	14634	14445	14735
Up	38	39	11

FC=1.2

TABLE 7: Number of significant DE genes with ‘none’ adjusment method, and at a p-value/adj.p-value of 0.01
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	263	427	95
NotSig	14376	14262	14590
Up	109	59	63

TABLE 8: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.1
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	65	267	0
NotSig	14666	14469	14748
Up	17	12	0

TABLE 9: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.05
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	14	177	0
NotSig	14730	14566	14748
Up	4	5	0

FC=1.3

TABLE 10: Number of significant DE genes with ‘none’ adjusment method, and at a p-value/adj.p-value of 0.01
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	188	357	50
NotSig	14495	14372	14670
Up	65	19	28

TABLE 11: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.1
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	17	195	0
NotSig	14728	14552	14748
Up	3	1	0

TABLE 12: Number of significant DE genes with ‘fdr’ adjusment method, and at a p-value/adj.p-value of 0.05
	DT vs veh	DT+Treg vs veh	DT+Treg vs DT
Down	0	124	0
NotSig	14748	14623	14748
Up	0	1	0

Differential Gene Expression analysis

Due to the large variance in the veh group, the transformation with observational and group-level weights were used. Without FC cut-off (using TREAT) and an FDR < 0.05, the DT vs DT+Treg comparison had 45 significant DE genes (TABLE 3). This may not be enough DE genes to perform meaningful functional enrichment analysis downstream. Therefore, The FDR threshold is increased to 0.1, in another word, we allow for 10% type I error, i.e. 1 in 10 genes may be a false positive for differential expression. Using a fold-change cut-off through application of TREAT gives additional stringency at the costs of reducing the list of DE genes.

P-value histogram: illustrates the distribution of p-values. As the stringency increases (increasing FC threshold), the distribution shifts towards 1, thus insignificant.
MA plot: helps visualise and identify genes with significant changes in expression. Points deviating from the central axis often indicate differentially expressed genes, allowing assessment of the magnitude and consistency of expression changes across conditions.
- \(x-axis =\) average expression, in log counts per million (CPM)
- \(y-axis =\) log fold change between conditions
Volcano plot: shows significantly differentially expressed genes appearing as points that are both statistically significant (located at the top) and have substantial fold changes (located on the left or right sides). This visualization enables identification of genes that are statistically and biologically significant.
- \(x-axis =\) log fold change between conditions
- \(y-axis =\) negative logarithm of the FDR-adjusted p-values
Heatmap: visualize gene expression patterns across different experimental conditions. Rows are genes, columns represent samples, and the colour intensity indicates the expression level of a gene in a specific sample. The genes are also clustered based on similar expression patterns, which provides insights into the overall structure and relationships within large datasets.
- These heatmaps illustrates the top 30 most significant DE genes
Venn diagram: visualises the significant DE gene overlap between the previous RNA-seq experiment and the current.

DT vs veh

P-val histogram

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10

MA plot

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Volcano plot

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Heatmap

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
762020e	tranmanhha135	2024-01-09

DT+Treg vs veh

P-val histogram

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

MA plot

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Volcano plot

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Heatmap

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
762020e	tranmanhha135	2024-01-09

DT+Treg vs DT

P-val histogram

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

MA plot

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Volcano plot

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10
762020e	tranmanhha135	2024-01-09

Heatmap

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
762020e	tranmanhha135	2024-01-09

Combined

Venn diagram

Heatmap

Version	Author	Date
36aeb85	Ha Manh Tran	2024-01-13
221e2fa	tranmanhha135	2024-01-10

png 
  2

Export Data

The following are exported:

de_genes_all.xlsx - This spreadsheet contains all DE genes.
de_genes_sig.xlsx - This spreadsheet contains only significant DE genes.

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Adelaide
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] knitr_1.45            pandoc_0.2.0          Glimma_2.10.0        
 [4] edgeR_3.42.4          limma_3.56.2          viridis_0.6.4        
 [7] viridisLite_0.4.2     ggrepel_0.9.4         ggpubr_0.6.0         
[10] ggplotify_0.1.2       extrafont_0.19        patchwork_1.1.3      
[13] DT_0.31               VennDiagram_1.7.3     futile.logger_1.4.3  
[16] pheatmap_1.0.12       cowplot_1.1.2         pander_0.6.5         
[19] kableExtra_1.3.4      plyr_1.8.9            scales_1.3.0         
[22] ComplexHeatmap_2.16.0 lubridate_1.9.3       forcats_1.0.0        
[25] stringr_1.5.1         purrr_1.0.2           tidyr_1.3.0          
[28] ggplot2_3.4.4         tidyverse_2.0.0       reshape2_1.4.4       
[31] tibble_3.2.1          readr_2.1.4           magrittr_2.0.3       
[34] dplyr_1.1.4           readxl_1.4.3         

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.15.0          
  [3] jsonlite_1.8.8              shape_1.4.6                
  [5] magick_2.8.2                farver_2.1.1               
  [7] rmarkdown_2.25              ragg_1.2.7                 
  [9] zlibbioc_1.46.0             GlobalOptions_0.1.2        
 [11] fs_1.6.3                    vctrs_0.6.5                
 [13] memoise_2.0.1               RCurl_1.98-1.13            
 [15] rstatix_0.7.2               webshot_0.5.5              
 [17] S4Arrays_1.0.6              htmltools_0.5.7            
 [19] lambda.r_1.2.4              broom_1.0.5                
 [21] cellranger_1.1.0            gridGraphics_0.5-1         
 [23] sass_0.4.8                  bslib_0.6.1                
 [25] htmlwidgets_1.6.4           futile.options_1.0.1       
 [27] cachem_1.0.8                whisker_0.4.1              
 [29] lifecycle_1.0.4             iterators_1.0.14           
 [31] pkgconfig_2.0.3             Matrix_1.6-3               
 [33] R6_2.5.1                    fastmap_1.1.1              
 [35] MatrixGenerics_1.12.3       GenomeInfoDbData_1.2.10    
 [37] clue_0.3-65                 digest_0.6.33              
 [39] colorspace_2.1-0            S4Vectors_0.38.2           
 [41] DESeq2_1.40.2               rprojroot_2.0.4            
 [43] textshaping_0.3.7           crosstalk_1.2.1            
 [45] GenomicRanges_1.52.1        labeling_0.4.3             
 [47] fansi_1.0.6                 timechange_0.2.0           
 [49] httr_1.4.7                  abind_1.4-5                
 [51] compiler_4.3.2              here_1.0.1                 
 [53] withr_2.5.2                 doParallel_1.0.17          
 [55] backports_1.4.1             BiocParallel_1.34.2        
 [57] carData_3.0-5               highr_0.10                 
 [59] Rttf2pt1_1.3.12             ggsignif_0.6.4             
 [61] rappdirs_0.3.3              DelayedArray_0.26.7        
 [63] rjson_0.2.21                tools_4.3.2                
 [65] httpuv_1.6.13               extrafontdb_1.0            
 [67] glue_1.6.2                  promises_1.2.1             
 [69] cluster_2.1.6               generics_0.1.3             
 [71] gtable_0.3.4                tzdb_0.4.0                 
 [73] hms_1.1.3                   XVector_0.40.0             
 [75] xml2_1.3.6                  car_3.1-2                  
 [77] utf8_1.2.4                  BiocGenerics_0.46.0        
 [79] foreach_1.5.2               pillar_1.9.0               
 [81] yulab.utils_0.1.2           later_1.3.2                
 [83] circlize_0.4.15             lattice_0.22-5             
 [85] tidyselect_1.2.0            locfit_1.5-9.8             
 [87] git2r_0.33.0                gridExtra_2.3              
 [89] IRanges_2.34.1              SummarizedExperiment_1.30.2
 [91] svglite_2.1.3               stats4_4.3.2               
 [93] xfun_0.41                   Biobase_2.60.0             
 [95] matrixStats_1.2.0           stringi_1.8.3              
 [97] workflowr_1.7.1             yaml_2.3.8                 
 [99] evaluate_0.23               codetools_0.2-19           
[101] cli_3.6.2                   systemfonts_1.0.5          
[103] munsell_0.5.0               jquerylib_0.1.4            
[105] Rcpp_1.0.11                 GenomeInfoDb_1.36.4        
[107] png_0.1-8                   parallel_4.3.2             
[109] ellipsis_0.3.2              bitops_1.0-7               
[111] writexl_1.4.2               crayon_1.5.2               
[113] GetoptLong_1.0.5            rlang_1.1.2                
[115] rvest_1.0.3                 formatR_1.14

DGE Analysis

Ha Tran

08-01-2024

Data Setup

Import DGElist Data

Initial Parameterisation

Design Matrix

Contrast Matrix

Limma-Voom

Apply voom transformation

Observational level weights

Observational & group level weights

Observational & sample level weights

PCA

Apply linear model

FC=none

FC=1.1

FC=1.2

FC=1.3

Differential Gene Expression analysis

DT vs veh

P-val histogram

MA plot

Volcano plot

Heatmap

DT+Treg vs veh

P-val histogram

MA plot

Volcano plot

Heatmap

DT+Treg vs DT

P-val histogram

MA plot

Volcano plot

Heatmap

Combined

Venn diagram

Heatmap

Export Data