Pipeline analysis
from raw data reading to graphic visualization Pipeline analysis
from raw data reading to graphic visualization
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.
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(“TSAR”)
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.
Select data of individual cell for pre-analysis screening. e.g. select well A1
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 0.4329590gammodel <- 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
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.
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))
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.
Read analysis using read_tsar() function and view head and tail to ensure appropriate output was achieved. Data output can also be saved locally into .csv or .txt format using function wrtie_tsar. However, pipeline to downstream analysis does not require output to be locally saved.
#> Well.Position tm
#> 1 A01 53.49893
#> 2 A02 54.33012
#> 3 A03 53.79578
#> 4 A04 54.68635
#> 5 A05 54.27076
#> 6 A06 55.04258#> Well.Position tm
#> 7 A07 53.14270
#> 8 A08 55.16132
#> 9 A09 53.97390
#> 10 A10 55.04258
#> 11 A11 54.27076
#> 12 A12 54.68635write output data file
write_tsar(read_tsar(x, output_content = 2),
name = "0923_tm_val", file = "csv")
For downstream analysis, data need to be mapped towards specific ligand and compound. Use may input by default excel template included in the package or input as .txt or .csv table, specifying Ligand and Compound by Well ID. Data with coumpound and ligand labels can also be stored locally using the same mean as previous step. All data are kept including wells with blank condition information (specified as NA). In case removal is needed, call function na.omit().
#> Well.Position Temperature Fluorescence Normalized norm_deriv tm
#> 1 A01 21.97290 87464.91 0.4339496 -0.003408165 53.49893
#> 2 A01 22.03227 87437.66 0.4337473 -0.003369012 53.49893
#> 3 A01 22.09164 87410.72 0.4335473 -0.003331383 53.49893
#> 4 A01 22.15101 87384.08 0.4333495 -0.003300231 53.49893
#> 5 A01 22.21038 87357.69 0.4331535 -0.003277111 53.49893
#> 6 A01 22.26976 87331.48 0.4329590 -0.003259463 53.49893
#> Protein Ligand
#> 1 CA FL DMSO
#> 2 CA FL DMSO
#> 3 CA FL DMSO
#> 4 CA FL DMSO
#> 5 CA FL DMSO
#> 6 CA FL DMSO#> Well.Position Temperature Fluorescence Normalized norm_deriv tm
#> 14766 A12 94.64307 15464.46 0.011995930 -0.03386972 54.68635
#> 14767 A12 94.70245 15343.25 0.009985017 -0.03379408 54.68635
#> 14768 A12 94.76181 15222.33 0.007978866 -0.03371186 54.68635
#> 14769 A12 94.82118 15101.69 0.005977393 -0.03363219 54.68635
#> 14770 A12 94.88055 14981.33 0.003980515 -0.03355871 54.68635
#> 14771 A12 94.93993 14861.24 0.001988068 -0.03348381 54.68635
#> Protein Ligand
#> 14766 CA FL PyxINE HCl
#> 14767 CA FL PyxINE HCl
#> 14768 CA FL PyxINE HCl
#> 14769 CA FL PyxINE HCl
#> 14770 CA FL PyxINE HCl
#> 14771 CA FL PyxINE HClWrite output into the working directory with write_tsar
write_tsar(norm_data, name = "vitamin_tm_val_norm", file = "csv")
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"))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.
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
)
Specify Control condition by assigning condition_ID to control. TSA_compare_plot generated multiple line graphs for comparison.
#> $`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")
#> 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_. doi:10.32614/CRAN.package.dplyr
#> <https://doi.org/10.32614/CRAN.package.dplyr>, R package version
#> 1.1.4, <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 1.1.4},
#> url = {https://CRAN.R-project.org/package=dplyr},
#> doi = {10.32614/CRAN.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},
#> }#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> 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_1.9.1 ggplot2_3.5.1 dplyr_1.1.4 TSAR_1.5.0
#>
#> 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_4.5.0
#> [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_4.5.0
#> [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] sass_0.4.9 yaml_2.3.10 lazyeval_0.2.2
#> [31] Formula_1.2-5 plotly_4.10.4 later_1.3.2
#> [34] pillar_1.9.0 car_3.1-3 ggpubr_0.6.0
#> [37] jquerylib_0.1.4 tidyr_1.3.1 cachem_1.1.0
#> [40] abind_1.4-8 nlme_3.1-166 mime_0.12
#> [43] tidyselect_1.2.1 zip_2.3.1 digest_0.6.37
#> [46] stringi_1.8.4 purrr_1.0.2 labeling_0.4.3
#> [49] splines_4.5.0 cowplot_1.1.3 fastmap_1.2.0
#> [52] grid_4.5.0 colorspace_2.1-1 cli_3.6.3
#> [55] magrittr_2.0.3 utf8_1.2.4 broom_1.0.7
#> [58] withr_3.0.2 scales_1.3.0 promises_1.3.0
#> [61] backports_1.5.0 rmarkdown_2.28 httr_1.4.7
#> [64] cellranger_1.1.0 ggsignif_0.6.4 openxlsx_4.2.7.1
#> [67] evaluate_1.0.1 knitr_1.48 viridisLite_0.4.2
#> [70] mgcv_1.9-1 rlang_1.1.4 Rcpp_1.0.13
#> [73] xtable_1.8-4 glue_1.8.0 rhandsontable_0.3.8
#> [76] jsonlite_1.8.9 R6_2.5.1