What are required dependencies?
Load Shiny separately if running shiny applications. Load ggplot2 and plotly if intending to modify automated graphs.
How are Tm values estimated?
TSAR package estimates uses derivative method by locating maximum first derivative. Errors may occur during the process, edit estimation with caution if errors were perceived.
Are smoothing applied to data, what kind?
TSAR package utilizes gam function from package mgcv. Model assumes method = "GACV.Cp" and sets to formula = y ~ s(x, bs = "ad"). Smoothing may be toggled off by specifying parameters. Refer to function documentations for instructions.
Got new suggestions or unmentioned-problems?
Start a new issue in our github repository: "CGAO123/TSAR"
TSAR package processes large amounts of data of similar properties, it is easy to confuse one with another and fail analysis. Verify your data input and out here by check for variable names. Consider renaming your data frame to theese following if issues are encountered.
raw_data
required:
Temperature (double)
Fluorescence (double)
Well.Position (character)
norm_data
required:
Temperature (double)
Fluorescence (double)
Well.Position (character)
generated by tsar:
norm_deriv (double)
tm (double)
tsar_data
required:
Temperature (double)
Fluorescence (double)
Well.Position/Well (character)
norm_deriv (double)
Tm (double)
Protein (character)
Ligand (character)
generated by tsar:
ExperimentalFileName (character)
well_ID (character)
condition_ID (character)
For more detailed examples, refers to vignettes, TSAR_Package_Structure and TSAR_Workflow_by_Command.
Given the two available workflows, here is a list of corresponding shiny functions and relative command line functions.
View Selected; TSAR::screen()
Remove Selected; remove_raw()
View Model Fit; view_model()
Analyze all Wells; gam_analysis()
normalize(), model_gam(), model_fit(), Tm_est()Upload Well Information & Set Conditions; join_well_info()
Manual Input; load template using data("well_information")
write.csv()Save File; write_tsar(), read_tsar()
Merge Replicate Trials; merge_norm()
Generate Boxplot; TSA_boxplot()
Generate Compare Plots; TSA_compare_plot()
Graph Selected Curves; TSA_wells_plot()
Compare Derivatives; view_deriv()
List Condition IDs; condition_IDs()
List Well IDs; well_IDs()
List Tms; TSA_Tms()
List Delta Tms; Tm_difference()
#> R version 4.5.0 RC (2025-04-04 r88126 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] TSAR_1.6.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 xfun_0.52 bslib_0.9.0
#> [4] ggplot2_3.5.2 shinyjs_2.1.0 htmlwidgets_1.6.4
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#> [10] tools_4.5.0 generics_0.1.3 tibble_3.2.1
#> [13] pkgconfig_2.0.3 Matrix_1.7-3 data.table_1.17.0
#> [16] readxl_1.4.5 lifecycle_1.0.4 stringr_1.5.1
#> [19] compiler_4.5.0 munsell_0.5.1 minpack.lm_1.2-4
#> [22] carData_3.0-5 httpuv_1.6.15 shinyWidgets_0.9.0
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#> [28] lazyeval_0.2.2 Formula_1.2-5 plotly_4.10.4
#> [31] later_1.4.2 pillar_1.10.2 car_3.1-3
#> [34] ggpubr_0.6.0 jquerylib_0.1.4 tidyr_1.3.1
#> [37] cachem_1.1.0 abind_1.4-8 nlme_3.1-168
#> [40] mime_0.13 tidyselect_1.2.1 zip_2.3.2
#> [43] digest_0.6.37 stringi_1.8.7 dplyr_1.1.4
#> [46] purrr_1.0.4 splines_4.5.0 fastmap_1.2.0
#> [49] grid_4.5.0 colorspace_2.1-1 cli_3.6.4
#> [52] magrittr_2.0.3 broom_1.0.8 scales_1.3.0
#> [55] promises_1.3.2 backports_1.5.0 rmarkdown_2.29
#> [58] httr_1.4.7 ggsignif_0.6.4 cellranger_1.1.0
#> [61] openxlsx_4.2.8 shiny_1.10.0 evaluate_1.0.3
#> [64] knitr_1.50 viridisLite_0.4.2 mgcv_1.9-3
#> [67] rlang_1.1.6 Rcpp_1.0.14 xtable_1.8-4
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#> [73] R6_2.6.1