TSAR Workflow by Shiny
## 1. Introduction TSAR, short for Thermal Shift Analysis in R, 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. TSAR Package is
wrapped within a three separate shiny application regarding, data
pre-processing, data analysis, and data visualization. All application
can be opened in both interactive window or browsing engine by copy
pasting server address into web browser.
2. Installation
3. Load TSAR Package
Use commands below to install TSAR package: library(BiocManager) BiocManager::install(“TSAR”)
4. Data Pre-Processing
Load data and remove blank wells by specifying a range of wells with
removerange = c() or individual wells with
removelist = c(). User may also use function
weed_raw() to open Shiny application to screen all raw
curves and remove by curve selection. To propagate change to data to
local change, close window properly by clicking
Close Window. The updated data set will be stored in the
global environment as new_raw_data.
In the plot panel, user can interact with graph and select and
un-select data by clicking on curves. User may also
View Selected wells only and Remove Selected.
To return to default page and current all change to data set, click
Refresh Screening. To propagate the same changes to local
data set, simple clickClose Window. Else, click
Copy Well IDs or
Copy Selected in Full Function Call to copy a
remove_raw() call containing all selected wells into
clipboard. Paste the call back into console or script and run to
propagate changes locally. p.s. It is recommended to remove large areas
of blank wells before calling weed_raw() to boost speed of
application.
Selection by grid is also possible; click on grid to highlight wells
of selection. Click again to un-highlight. Click too fast may lead to
error due to speed of reaction, use Clear All Selected
button at the top of page to reset selection. Note that clearing
selections alters to-be-copied selections, but will not restore curves
already removed in the graph. However, changes are not made permanently.
If erroneously removed, simply close and reopen window to run again.
Use View Selected and Remove Selected to
view and remove selection. To return to viewing rest of the data, click
Refresh Screening. To propagate the same changes to local
data set, click Copy Selected in Full Function Call to copy
a remove_raw() call containing all selected wells into
clipboard. Paste the call back into console or script and run to
propagate changes locally.
5. Data Analysis
Analyze data by calling function analyze_norm() and
follow the workflow from top to bottom. Preview data table for changes
occurring at each step and refer to graph to view fit of model on each
well. Always refer to the message for hints and error references. A
success message will also be prompted after each successful run of
step.
Remember to save analysis output locally by clicking
Save File. Always preview data before saving to ensure data
contains all necessary information.
The top left panel output a preview of current data set. The right panel allows user to view the fit of model and Tm estimation by individual wells of selection. Once confirming correct data input and modeling effects,
Click Analyze all Wells to propagate model and analysis
onto the rest of data. A preview of analyzed data will also be modeling
and analyzing all 96 Wells will take between 30 to 50 seconds. If no
modeling are needed, given data are smooth enough, analysis of all 96
wells should be completed under 5 seconds. p.s. actual time length many
subject to change under different conditions
Upload well information by excel template and preview to confirm if
information is correct. Use Preview to preview uploaded
information and directly edit inside the window. User may also choose
Manual Input to put in all condition information by Well.
Make sure to hit Save Changes after editing and click
Set Conditions to link all data to the conditions. A
success message should be prompted. p.s. manually inputted information
will override file upload. If mistakenly saved, please close window and
re-run command.
Lastly, to save all analysis locally, click Save File
after previewing output. p.s. make to select both under
Choose dataset if intending to use
graph_tsar() or other graphing tools within the
package.
6. Data Visualization
Use function graph_tsar() to start a Shiny application
for graphing options. Run na.omit() on data if error
occurs. Four graphing options are allowed, boxplot of Tm, compare plots,
and conditions plot.
Function takes optional data parameter. If analysis file is already
imported in the environment, call function as
graph_tsar(tsar_data). Refer to vignette “TSAR Workflow by
Command” for instructions on how to merge using function
merge_norm(). Else, user may use the merge data panel to
upload and merge data of all test trials. Simply call
graph_tsar() and click Upload and Merge Data
button to reveal the panel to merge data.
Upload is limited to 30MB in size, but not count. After upload, user
will be prompted corresponding numbers of input boxes to specify date of
each experiment. Click Save Dates, then
Merge and Save Data. A short preview of tsar_data will be
prompted. For full lists and filters of well_ID and conditions_ID, refer
to the helper buttons at the bottom of page.
Top panel outputs all plots, selected desired graphing features below and click generate to output graphs. p.s. Graphing compare plots and selected curves are takes longer than boxplot, please give it few seconds to load.
Boxplot has the option to be loaded interactive. Note interactive graphs have to be produced with legend combined.
Generating compare plots will output all plausible comparisons by
control. To any specific one, a drop list View Only: will
be prompted below the button Generate Compare Plots. Select
by condition_ID to zoom in on graphs.
Compare plots for first derivative graphs are interactive graphs
grouped by specified variable.
Helper functions include these following:
7. Session Info
7.1 Citation
#> To cite package 'TSAR' in publications use:
#>
#> Gao X, McFadden WM, Wen X, Emanuelli A, Lorson ZC, Zheng H, Kirby KA,
#> Sarafianos SG (2023). "Use of TSAR, Thermal Shift Analysis in R, to
#> identify Folic Acid as a Molecule that Interacts with HIV-1 Capsid."
#> _bioRxiv_. doi:10.1101/2023.11.29.569293
#> <https://doi.org/10.1101/2023.11.29.569293>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {Use of TSAR, Thermal Shift Analysis in R, to identify Folic Acid
#> as a Molecule that Interacts with HIV-1 Capsid},
#> author = {X. Gao and W. M. McFadden and X. Wen and A. Emanuelli and Z. C. Lorson and H. Zheng and K. A. Kirby and S. G. Sarafianos},
#> journal = {bioRxiv},
#> year = {2023},
#> doi = {10.1101/2023.11.29.569293},
#> }#> To cite R in publications use:
#>
#> R Core Team (2024). _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 = {2024},
#> 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 1.1.4,
#> https://github.com/tidyverse/dplyr, <https://dplyr.tidyverse.org>.
#>
#> 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 1.1.4, https://github.com/tidyverse/dplyr},
#> url = {https://dplyr.tidyverse.org},
#> }#> 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},
#> }#> To cite package 'shiny' in publications use:
#>
#> Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J,
#> McPherson J, Dipert A, Borges B (2024). _shiny: Web Application
#> Framework for R_. R package version 1.9.1,
#> https://github.com/rstudio/shiny, <https://shiny.posit.co/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {shiny: Web Application Framework for R},
#> author = {Winston Chang and Joe Cheng and JJ Allaire and Carson Sievert and Barret Schloerke and Yihui Xie and Jeff Allen and Jonathan McPherson and Alan Dipert and Barbara Borges},
#> year = {2024},
#> note = {R package version 1.9.1, https://github.com/rstudio/shiny},
#> url = {https://shiny.posit.co/},
#> }#> The 'utils' package is part of R. To cite R in publications use:
#>
#> R Core Team (2024). _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 = {2024},
#> 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.7.2 Session Info
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] shiny_1.9.1 ggplot2_3.5.1 dplyr_1.1.4 TSAR_1.5.0 rmarkdown_2.28
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 xfun_0.48 bslib_0.8.0
#> [4] shinyjs_2.1.0 htmlwidgets_1.6.4 rstatix_0.7.2
#> [7] lattice_0.22-6 vctrs_0.6.5 tools_4.4.1
#> [10] generics_0.1.3 tibble_3.2.1 fansi_1.0.6
#> [13] highr_0.11 pkgconfig_2.0.3 Matrix_1.7-1
#> [16] data.table_1.16.2 readxl_1.4.3 lifecycle_1.0.4
#> [19] farver_2.1.2 stringr_1.5.1 compiler_4.4.1
#> [22] munsell_0.5.1 minpack.lm_1.2-4 carData_3.0-5
#> [25] httpuv_1.6.15 shinyWidgets_0.8.7 htmltools_0.5.8.1
#> [28] sys_3.4.3 buildtools_1.0.0 sass_0.4.9
#> [31] yaml_2.3.10 lazyeval_0.2.2 Formula_1.2-5
#> [34] plotly_4.10.4 later_1.3.2 pillar_1.9.0
#> [37] car_3.1-3 ggpubr_0.6.0 jquerylib_0.1.4
#> [40] tidyr_1.3.1 cachem_1.1.0 abind_1.4-8
#> [43] nlme_3.1-166 mime_0.12 tidyselect_1.2.1
#> [46] zip_2.3.1 digest_0.6.37 stringi_1.8.4
#> [49] purrr_1.0.2 labeling_0.4.3 maketools_1.3.1
#> [52] splines_4.4.1 cowplot_1.1.3 fastmap_1.2.0
#> [55] grid_4.4.1 colorspace_2.1-1 cli_3.6.3
#> [58] magrittr_2.0.3 utf8_1.2.4 broom_1.0.7
#> [61] withr_3.0.2 scales_1.3.0 promises_1.3.0
#> [64] backports_1.5.0 httr_1.4.7 cellranger_1.1.0
#> [67] ggsignif_0.6.4 openxlsx_4.2.7.1 evaluate_1.0.1
#> [70] knitr_1.48 viridisLite_0.4.2 mgcv_1.9-1
#> [73] rlang_1.1.4 Rcpp_1.0.13 xtable_1.8-4
#> [76] glue_1.8.0 rhandsontable_0.3.8 jsonlite_1.8.9
#> [79] R6_2.5.1