TSAR Workflow by Command
Pipeline analysis from raw data reading to graphic visualization
1. Introduction
TSAR Package provides simple solution to qPCR data processing, computing thermal shift analysis given either raw fluorescent data or smoothed curves. The functions provide users with the protocol to conduct preliminary data checks and also expansive analysis on large scale of data. Furthermore, it showcases simple graphic presentation of analysis, generating clear box plot and line graphs given input of desired designs. Overall, TSAR Package offers a workflow easy to manage and visualize.
2. Installation
Load Package and other relevant packages. Usage of dplyr and ggplot2 along with TSAR package is recommended for enhanced analysis.
Use commands below to install TSAR package: library(BiocManager) BiocManager::install(“CGAO123/TSAR”, build_vignettes = TRUE)
library(TSAR)
library(dplyr)
library(ggplot2)
3. Load Data
Read data in .txt or .csv format. Use read.delim function to input tab delimited file; use read.csv to input comma separated files. Other formats of input are welcomed as long as data is stored in data frame structure as numeric type (calculation-required data) and characters (non-calculation-required data). Ensure excessive lines are removed (e.g. skip = , nrows = ). Means to check these are View(), pre-opening data file in excel, or manually removing all excessive data before input reading. Package defaults variable names as “Well.Position”, “Temperature”, “Fluorescence”, “Normalized”. Consider renaming data frame before proceding to following step.
data("qPCR_data1")
raw_data <- qPCR_data1
4. Data Pre-Processing
Select data of individual cell for pre-analysis screening. e.g. select well A1
test <- raw_data %>% filter(Well.Position == "A01")
Run example analysis on one well to screen potential errors and enhancement of model.
# normalize fluorescence reading into scale between 0 and 1
test <- normalize(test, fluo = 5, selected = c(
"Well.Position", "Temperature",
"Fluorescence", "Normalized"
))
head(test)
#> Well.Position Temperature Fluorescence Normalized
#> 1 A01 21.97290 87464.91 0.4339496
#> 2 A01 22.03227 87437.66 0.4337473
#> 3 A01 22.09164 87410.72 0.4335473
#> 4 A01 22.15101 87384.08 0.4333495
#> 5 A01 22.21038 87357.69 0.4331535
#> 6 A01 22.26976 87331.48 0.4329590
gammodel <- model_gam(test, x = test$Temperature, y = test$Normalized)
test <- model_fit(test, model = gammodel)
Output analysis result using view_model() to view normalized data and fitted model. Determine if any noise need to be removed (i.e. subsetting by temperature range). Determine which model is the best (i.e. is currrent data already smoothed, does fitted model suit well.) Determine if Tm-estimation is proper. *current model assumes derivative estimation of Tm value.
#> [1] 53.73642
view <- view_model(test)
view[[1]] + theme(aspect.ratio = 0.7, legend.position = "bottom")
view[[2]] + theme(aspect.ratio = 0.7, legend.position = "bottom")
Screen all wells for curve shape on raw_data set and sift out corrupted data. This step is not required but may help remove data modeling errors.
myApp <- weed_raw(raw_data, checkrange = c("A", "C", "1", "12"))
raw_data <- remove_raw(raw_data, removerange = c("B", "H", "1", "12"))
screen(raw_data) + theme(
aspect.ratio = 0.7,
legend.position = "bottom",
legend.text = element_text(size = 6),
legend.key.size = unit(0.4, "cm"),
legend.title = element_text(size = 8)
) +
guides(color = guide_legend(nrow = 2, byrow = TRUE))
5. 96-well Analysis Application
TSAR package excels in mass analysis by propagating identical protocols to all 96 wells. smoothed = T infers current data is smoothed and no separate modeling is needed. If modeling is needed, input argument as smoothed = F.
TSAR package performs derivative analysis using a generalized additive model through package mgcv or boltzmann analysis using nlsLM from package minpack.lm.
8. Merge Data across Biological Replicates
Repeat step 2 through 6 on replicate data set. A five step function call will complete all analysis. If additional screening is desired, a two step call will run the interactive window to allow selection of
data("qPCR_data2")
raw_data_rep <- qPCR_data2
raw_data_rep <- remove_raw(raw_data_rep, removerange = c("B", "H", "1", "12"))
myApp <- weed_raw(raw_data_rep)
shiny::runApp(myApp)
raw_data_rep <- remove_raw(raw_data_rep, removelist = "A12")
screen(raw_data_rep) + theme(
aspect.ratio = 0.7,
legend.position = "bottom",
legend.text = element_text(size = 6),
legend.key.size = unit(0.4, "cm"),
legend.title = element_text(size = 8)
) +
guides(color = guide_legend(nrow = 2, byrow = TRUE))
analysis_rep <- gam_analysis(raw_data_rep, smoothed = TRUE)
output_rep <- read_tsar(analysis_rep, output_content = 2)
norm_data_rep <- join_well_info(
file_path = NULL,
file = well_information,
output_rep, type = "by_template"
)
norm_data_rep <- na.omit(norm_data_rep)
Merge data by content. All data are marked its source file name and experiment date.
norm_data <- na.omit(norm_data)
norm_data_rep <- na.omit(norm_data_rep)
tsar_data <- merge_norm(
data = list(norm_data, norm_data_rep),
name = c(
"Vitamin_RawData_Thermal Shift_02_162.eds.csv",
"Vitamin_RawData_Thermal Shift_02_168.eds.csv"
),
date = c("20230203", "20230209")
)
9. Tm Estimation Shift Visualization
Use condition_IDs() and well_IDs() to select or remove condition to visualize. Visualize Tm estimation by compound or ligand type in the format of box graph.
#> [1] "CA FL_DMSO" "CA FL_CAI" "CA FL_BIOTIN" "CA FL_4-ABA"
#> [5] "CA FL_=+-LA" "CA FL_PyxINE HCl"
#> [1] "A01_CA FL_DMSO_20230203" "A02_CA FL_DMSO_20230203"
#> [3] "A03_CA FL_CAI_20230203" "A04_CA FL_CAI_20230203"
#> [5] "A05_CA FL_BIOTIN_20230203" "A06_CA FL_BIOTIN_20230203"
#> [7] "A07_CA FL_4-ABA_20230203" "A08_CA FL_4-ABA_20230203"
#> [9] "A09_CA FL_=+-LA_20230203" "A10_CA FL_=+-LA_20230203"
#> [11] "A11_CA FL_PyxINE HCl_20230203" "A12_CA FL_PyxINE HCl_20230203"
#> [13] "A01_CA FL_DMSO_20230209" "A02_CA FL_DMSO_20230209"
#> [15] "A03_CA FL_CAI_20230209" "A04_CA FL_CAI_20230209"
#> [17] "A05_CA FL_BIOTIN_20230209" "A06_CA FL_BIOTIN_20230209"
#> [19] "A07_CA FL_4-ABA_20230209" "A08_CA FL_4-ABA_20230209"
#> [21] "A09_CA FL_=+-LA_20230209" "A10_CA FL_=+-LA_20230209"
#> [23] "A11_CA FL_PyxINE HCl_20230209"
conclusion <- tsar_data %>%
filter(condition_ID != "NA_NA") %>%
filter(condition_ID != "CA FL_Riboflavin")
TSA_boxplot(conclusion,
color_by = "Protein", label_by = "Ligand",
separate_legend = FALSE
)
10. TSA Curve Visualization
Specify Control condition by assigning condition_ID to control. TSA_compare_plot generated multiple line graphs for comparison.
control_ID <- "CA FL_DMSO"
TSA_compare_plot(conclusion,
y = "RFU",
control_condition = control_ID
)
#> $`CA FL_CAI`
#>
#> $`CA FL_BIOTIN`
#>
#> $`CA FL_4-ABA`
#>
#> $`CA FL_=+-LA`
#>
#> $`CA FL_PyxINE HCl`
#>
#> $`Control: CA FL_DMSO`
Select by condition or well IDs to view curves and estimated Tm values.
error <- conclusion %>% filter(condition_ID == "CA FL_PyxINE HCl")
TSA_wells_plot(error, separate_legend = FALSE)
To further visualize the comparison, graph first derivatives grouped by needs (i.e. well_ID, condition_ID, or other separately appended conditions). Below is an example command. Due to size limit of vignette, graph will not be displayed. view_deriv(conclusion, frame_by = "condition_ID")
11. Session Info
11.1 Citation
#> To cite package 'TSAR' in publications use:
#>
#> Gao X, McFadden WM, Sarafianos SG (2023). _TSAR: Thermal Shift
#> Analysis in R_. doi:10.18129/B9.bioc.TSAR
#> <https://doi.org/10.18129/B9.bioc.TSAR>, R package version 1.0.0,
#> <https://bioconductor.org/packages/TSAR>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {TSAR: Thermal Shift Analysis in R},
#> author = {Xinlin Gao and William M. McFadden and Stefan G. Sarafianos},
#> year = {2023},
#> note = {R package version 1.0.0},
#> url = {https://bioconductor.org/packages/TSAR},
#> doi = {10.18129/B9.bioc.TSAR},
#> }
#> To cite R in publications use:
#>
#> R Core Team (2023). _R: A Language and Environment for Statistical
#> Computing_. R Foundation for Statistical Computing, Vienna, Austria.
#> <https://www.R-project.org/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {R: A Language and Environment for Statistical Computing},
#> author = {{R Core Team}},
#> organization = {R Foundation for Statistical Computing},
#> address = {Vienna, Austria},
#> year = {2023},
#> url = {https://www.R-project.org/},
#> }
#>
#> We have invested a lot of time and effort in creating R, please cite it
#> when using it for data analysis. See also 'citation("pkgname")' for
#> citing R packages.
#> To cite package 'dplyr' in publications use:
#>
#> Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A
#> Grammar of Data Manipulation_. R package version 1.1.3,
#> <https://CRAN.R-project.org/package=dplyr>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {dplyr: A Grammar of Data Manipulation},
#> author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller and Davis Vaughan},
#> year = {2023},
#> note = {R package version 1.1.3},
#> url = {https://CRAN.R-project.org/package=dplyr},
#> }
#> To cite ggplot2 in publications, please use
#>
#> H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
#> Springer-Verlag New York, 2016.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Book{,
#> author = {Hadley Wickham},
#> title = {ggplot2: Elegant Graphics for Data Analysis},
#> publisher = {Springer-Verlag New York},
#> year = {2016},
#> isbn = {978-3-319-24277-4},
#> url = {https://ggplot2.tidyverse.org},
#> }
11.2 Session Info
#> R version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] shiny_1.7.5.1 ggplot2_3.4.4 dplyr_1.1.3 TSAR_1.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.4 xfun_0.40 bslib_0.5.1
#> [4] shinyjs_2.1.0 htmlwidgets_1.6.2 rstatix_0.7.2
#> [7] lattice_0.22-5 vctrs_0.6.4 tools_4.3.1
#> [10] generics_0.1.3 tibble_3.2.1 fansi_1.0.5
#> [13] pkgconfig_2.0.3 Matrix_1.6-1.1 data.table_1.14.8
#> [16] readxl_1.4.3 lifecycle_1.0.3 farver_2.1.1
#> [19] stringr_1.5.0 compiler_4.3.1 munsell_0.5.0
#> [22] minpack.lm_1.2-4 carData_3.0-5 httpuv_1.6.12
#> [25] shinyWidgets_0.8.0 htmltools_0.5.6.1 sass_0.4.7
#> [28] yaml_2.3.7 lazyeval_0.2.2 plotly_4.10.3
#> [31] later_1.3.1 pillar_1.9.0 car_3.1-2
#> [34] ggpubr_0.6.0 jquerylib_0.1.4 tidyr_1.3.0
#> [37] ellipsis_0.3.2 cachem_1.0.8 abind_1.4-5
#> [40] nlme_3.1-163 mime_0.12 tidyselect_1.2.0
#> [43] zip_2.3.0 digest_0.6.33 stringi_1.7.12
#> [46] purrr_1.0.2 labeling_0.4.3 splines_4.3.1
#> [49] cowplot_1.1.1 fastmap_1.1.1 grid_4.3.1
#> [52] colorspace_2.1-0 cli_3.6.1 magrittr_2.0.3
#> [55] utf8_1.2.4 broom_1.0.5 withr_2.5.1
#> [58] scales_1.2.1 promises_1.2.1 backports_1.4.1
#> [61] rmarkdown_2.25 httr_1.4.7 ggsignif_0.6.4
#> [64] cellranger_1.1.0 openxlsx_4.2.5.2 evaluate_0.22
#> [67] knitr_1.44 viridisLite_0.4.2 mgcv_1.9-0
#> [70] rlang_1.1.1 Rcpp_1.0.11 xtable_1.8-4
#> [73] glue_1.6.2 rhandsontable_0.3.8 jsonlite_1.8.7
#> [76] R6_2.5.1