viralprod is a powerful R package designed for automating viral
production data analyses. It is a valuable tool for researchers and
marine scientists investigating marine viruses, enabling them to
calculate, analyze and visualize viral production efficiently.
You can find detailed information in the vignettes online:
You can install the development version of viralprod from
GitHub using the following code:
# install.packages("devtools")
devtools::install_github("mdhishamshaikh/ViralProduction_R", dependencies = TRUE, build_vignettes = TRUE)To load the package, use the following command:
library(viralprod)The viralprod package simplifies the analysis of viral production
data, making it suitable for researchers studying marine viruses. To
utilize the package effectively, it’s essential to complete prior steps,
including sampling, virus reduction assay encompassing filtration and
incubation stages, and subsequent flow cytometry processing. The package
is structured around three fundamental steps, each contributing to a
comprehensive analysis:
- Calculating Viral Production: It provides two primary methods,
linear regressionandVIPCAL, for calculating viral production rates from viral counts. - Analyze Viral Production: Various parameters for estimating virus-mediated microbial mortality can be determined based on the calculated viral production values.
- Visualize Viral Production: Effective data visualization plays a pivotal role in data analyses. The package provides illustrative examples to visualize your viral production data effectively.
Begin by exploring the available functions within the package. Alongside
the pipe operator from the dplyr package, a variety of functions are
at your disposal:
ls("package:viralprod")
#> [1] "%>%" "vp_analyze" "vp_calculate"
#> [4] "vp_check_populations" "vp_class_count_data" "vp_class_ori_abu"
#> [7] "vp_end_to_end" "vp_list_of_methods" "vp_visualize"Within the package, each of the three primary steps has its own,
dedicated function. Additionally, a comprehensive wrapper function
exists, vp_end_to_end, which integrates all three essential steps of
viral production analyses into a single execution. Executing this
singular function yields calculated viral production results, analyzed
data, and visualizations. Below, we will demonstrate the functionality
of the wrapper function using example data available in the
inst/extdata folder, use of the separate main functions is integrated
in the Introduction to viralprod vignette.
Running the code, provided below, also serves as a convenient method to
verify the correct installation of the package. Note that the assessment
of input data frames conforms to the package’s requirements is
seamlessly integrated into the wrapper function through the
vp_class_count_data function.
For detailed information regarding the specific requirements for input
data, please consult the vignette: Input data for viralprod.
Furthermore, comprehensive insights into the methodology, including
individual function usage, will be expounded upon in
Introduction to viralprod.
Two input data files are required: count data from flow cytometer, the original abundances of seawater sample. Load in example data:
data_NJ2020_all <- read.csv(system.file('extdata', 'NJ2020_Station_2_and_6_all_populations.csv', package = "viralprod"))
head(data_NJ2020_all)
#> Sample_Name Staining_Protocol Expt_Date Date_Measurement Location
#> 1 vi201103.011 Viruses 06-08-20 03-11-20 NJ2020
#> 2 vi201103.027 Viruses 06-08-20 03-11-20 NJ2020
#> 3 vi201104.009 Viruses 06-08-20 03-11-20 NJ2020
#> 4 vi201103.013 Viruses 06-08-20 03-11-20 NJ2020
#> 5 vi201103.028 Viruses 06-08-20 03-11-20 NJ2020
#> 6 vi201104.010 Viruses 06-08-20 03-11-20 NJ2020
#> Station_Number Depth Sample_Type Timepoint Replicate c_Bacteria c_HNA
#> 1 2 1 0.22 0 1 2302.772 657.9348
#> 2 2 1 VP 0 1 902558.635 485109.3090
#> 3 2 1 VPC 0 1 825159.915 467213.8330
#> 4 2 1 0.22 0 2 2302.772 328.9674
#> 5 2 1 VP 0 2 857782.516 513137.7551
#> 6 2 1 VPC 0 2 770149.254 451553.3084
#> c_LNA c_Viruses c_V1 c_V2 c_V3 VBR HNAperLNA ...20
#> 1 1644.837 12126652 10474610 1558655 93387.35 5266.11111 0.4000000 NA
#> 2 417449.326 33939019 28626527 4623201 689291.12 37.60312 1.1620795 NA
#> 3 357946.082 36735608 31173468 4822708 739431.39 44.51938 1.3052632 NA
#> 4 1973.804 12777569 10998408 1675073 104089.08 5548.77778 0.1666667 NA
#> 5 344644.761 34344563 28800531 4845791 698240.85 40.03878 1.4888889 NA
#> 6 318595.945 34180810 28994948 4534163 651699.32 44.38206 1.4173228 NA
#> ...21 ...22 ...23 ...24 ...25 ...26 ...27 ...28 ...29 ...30 ...31
#> 1 NA NA NA NA NA NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA NA NA NA NA NA
#> 4 NA NA NA NA NA NA NA NA NA NA NA
#> 5 NA NA NA NA NA NA NA NA NA NA NA
#> 6 NA NA NA NA NA NA NA NA NA NA NAstr(data_NJ2020_all)
#> 'data.frame': 84 obs. of 31 variables:
#> $ Sample_Name : chr "vi201103.011" "vi201103.027" "vi201104.009" "vi201103.013" ...
#> $ Staining_Protocol: chr "Viruses" "Viruses" "Viruses" "Viruses" ...
#> $ Expt_Date : chr "06-08-20" "06-08-20" "06-08-20" "06-08-20" ...
#> $ Date_Measurement : chr "03-11-20" "03-11-20" "03-11-20" "03-11-20" ...
#> $ Location : chr "NJ2020" "NJ2020" "NJ2020" "NJ2020" ...
#> $ Station_Number : int 2 2 2 2 2 2 2 2 2 2 ...
#> $ Depth : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ Sample_Type : chr "0.22" "VP" "VPC" "0.22" ...
#> $ Timepoint : num 0 0 0 0 0 0 0 0 0 3 ...
#> $ Replicate : int 1 1 1 2 2 2 3 3 3 1 ...
#> $ c_Bacteria : num 2303 902559 825160 2303 857783 ...
#> $ c_HNA : num 658 485109 467214 329 513138 ...
#> $ c_LNA : num 1645 417449 357946 1974 344645 ...
#> $ c_Viruses : num 12126652 33939019 36735608 12777569 34344563 ...
#> $ c_V1 : num 10474610 28626527 31173468 10998408 28800531 ...
#> $ c_V2 : num 1558655 4623201 4822708 1675073 4845791 ...
#> $ c_V3 : num 93387 689291 739431 104089 698241 ...
#> $ VBR : num 5266.1 37.6 44.5 5548.8 40 ...
#> $ HNAperLNA : num 0.4 1.162 1.305 0.167 1.489 ...
#> $ ...20 : logi NA NA NA NA NA NA ...
#> $ ...21 : logi NA NA NA NA NA NA ...
#> $ ...22 : logi NA NA NA NA NA NA ...
#> $ ...23 : logi NA NA NA NA NA NA ...
#> $ ...24 : logi NA NA NA NA NA NA ...
#> $ ...25 : logi NA NA NA NA NA NA ...
#> $ ...26 : logi NA NA NA NA NA NA ...
#> $ ...27 : logi NA NA NA NA NA NA ...
#> $ ...28 : logi NA NA NA NA NA NA ...
#> $ ...29 : logi NA NA NA NA NA NA ...
#> $ ...30 : logi NA NA NA NA NA NA ...
#> $ ...31 : logi NA NA NA NA NA NA ...NJ2020_original_abundances <- read.csv(system.file('extdata','NJ2020_original_abundances.csv', package = "viralprod"))
NJ2020_original_abundances
#> ...1 Location Bacterial_Sample_Name Viral_Sample_Name Station_Number
#> 1 1 NJ2020 BA210507.066 VI210507.067 1
#> 2 2 NJ2020 BA210507.067 VI210507.068 2
#> 3 3 NJ2020 BA210507.068 VI210507.069 3
#> 4 4 NJ2020 ba201028.015 vi201028.008 4
#> 5 5 NJ2020 BA210507.069 VI210507.070 5
#> 6 6 NJ2020 BA210507.070 VI210507.071 6
#> 7 7 NJ2020 BA210507.071 VI210507.072 7
#> Expt_Date Total_Bacteria HNA LNA Total_Viruses V1 V2
#> 1 04-08-20 3454942 1993730 1461212 182285832 140810685 33328040
#> 2 06-08-20 3231054 2038118 1192936 204686985 161552701 34950478
#> 3 18-08-20 6665980 3740874 2925106 226495058 191800795 26766127
#> 4 20-08-20 8163509 3911664 4251845 183418451 153581030 22152025
#> 5 01-09-20 3297776 1775346 1522430 137359967 111715891 22360925
#> 6 03-09-20 4702883 2332421 2370463 153088962 122845569 25154253
#> 7 05-09-20 3599259 1837173 1762086 133369028 107261386 22336907
#> V3 VBR Latitude Longitude
#> 1 8147107 52.76089 53.00177 4.789015
#> 2 8183806 63.34990 53.00177 4.789015
#> 3 7928136 33.97776 53.00177 4.789015
#> 4 7685396 22.46809 53.00177 4.789015
#> 5 3283152 41.65230 53.00177 4.789015
#> 6 5089140 32.55215 53.00177 4.789015
#> 7 3770735 37.05458 53.00177 4.789015str(NJ2020_original_abundances)
#> 'data.frame': 7 obs. of 16 variables:
#> $ ...1 : int 1 2 3 4 5 6 7
#> $ Location : chr "NJ2020" "NJ2020" "NJ2020" "NJ2020" ...
#> $ Bacterial_Sample_Name: chr "BA210507.066" "BA210507.067" "BA210507.068" "ba201028.015" ...
#> $ Viral_Sample_Name : chr "VI210507.067" "VI210507.068" "VI210507.069" "vi201028.008" ...
#> $ Station_Number : int 1 2 3 4 5 6 7
#> $ Expt_Date : chr "04-08-20" "06-08-20" "18-08-20" "20-08-20" ...
#> $ Total_Bacteria : num 3454942 3231054 6665980 8163509 3297776 ...
#> $ HNA : num 1993730 2038118 3740874 3911664 1775346 ...
#> $ LNA : num 1461212 1192936 2925106 4251845 1522430 ...
#> $ Total_Viruses : num 1.82e+08 2.05e+08 2.26e+08 1.83e+08 1.37e+08 ...
#> $ V1 : num 1.41e+08 1.62e+08 1.92e+08 1.54e+08 1.12e+08 ...
#> $ V2 : num 33328040 34950478 26766127 22152025 22360925 ...
#> $ V3 : num 8147107 8183806 7928136 7685396 3283152 ...
#> $ VBR : num 52.8 63.3 34 22.5 41.7 ...
#> $ Latitude : num 53 53 53 53 53 ...
#> $ Longitude : num 4.79 4.79 4.79 4.79 4.79 ...Before executing the comprehensive wrapper function, let’s take a look at all the different arguments:
args(vp_end_to_end)
#> function (data = data.frame(), original_abundances = data.frame(),
#> methods = c(1:12), SR_calc = TRUE, BP_endpoint = TRUE, burst_sizes = c(),
#> bacterial_secondary_production = NULL, nutrient_content_bacteria = list(),
#> nutrient_content_viruses = list(), write_output = TRUE, output_dir = "")
#> NULLIn the initial stages of the wrapper function, both input data frames
undergo a thorough examination to ensure they conform to the required
format. Subsequently, we encounter the methods parameter, which
governs the selection of algorithmic variants of linear regression and
VIPCAL for the calculation of viral production rates. As a user, you
have the flexibility to execute all available variants or opt for a
subset, among the prominent methods are:
- Method 4: vp_linear_average_replicates_diff.
- Method 9: vp_VIPCAL_average_replicates_diff.
- Method 12: vp_VIPCAL_average_replicates_diff_LMER_SE.
Furthermore, the wrapper function offers a range of supplementary parameters. In the absence of user-defined values, default settings defined within the function, are automatically applied. Notably, the package incorporates a dedicated function designed to generate a comprehensive list of available variants for viral production calculation in the global environment.
vp_list_of_methods()
names(list_of_methods)
#> [1] "vp_linear_allpoints"
#> [2] "vp_linear_separate_replicates"
#> [3] "vp_linear_average_replicates"
#> [4] "vp_linear_average_replicates_diff"
#> [5] "vp_linear_average_replicates_diff_LMER"
#> [6] "vp_VIPCAL_separate_replicates"
#> [7] "vp_VIPCAL_average_replicates"
#> [8] "vp_VIPCAL_average_replicates_SE"
#> [9] "vp_VIPCAL_average_replicates_diff"
#> [10] "vp_VIPCAL_average_replicates_diff_SE"
#> [11] "vp_VIPCAL_average_replicates_diff_LMER"
#> [12] "vp_VIPCAL_average_replicates_diff_LMER_SE"For example, consider a closer look at method 12:
list_of_methods[12]
#> $vp_VIPCAL_average_replicates_diff_LMER_SE
#> function (data)
#> {
#> separate_replicate_dataframe_with_timepoints <- vp_separate_replicate_dataframe(data)
#> determine_viral_production_dataframe <- determine_vp_VIPCAL_LMER_model_SE(separate_replicate_dataframe_with_timepoints)
#> viral_production_VIPCAL <- determine_viral_production_dataframe %>%
#> dplyr::group_by(.data$tag, .data$Time_Range, .data$Population,
#> .data$Sample_Type) %>% dplyr::arrange("tag", factor(.data$Sample_Type,
#> levels = c("VP", "VPC", "Diff")), factor(.data$Population,
#> levels = .GlobalEnv$populations_to_analyze[grep("c_V",
#> .GlobalEnv$populations_to_analyze)])) %>% dplyr::mutate(VP_Method = "VPCL_AR_DIFF_LMER_SE") %>%
#> dplyr::select("tag", "Location", "Station_Number", "Depth",
#> dplyr::everything())
#> return(viral_production_VIPCAL)
#> }
#> <bytecode: 0x00000172c1595af8>
#> <environment: namespace:viralprod>Now, it’s time to execute the wrapper function. Although the following
demonstration uses example data, it’s essential to note that executing
this function can serve as a convenient method to verify the correct
installation of the package. Since this is for illustration purposes, no
output files will be written. If write_output is set to TRUE, ensure
to specify the output directory where the results should be stored. All
output data frames and a list containing various visualizations will
become available in the global environment.
# All methods will be executed with default parameters
vp_end_to_end(data = data_NJ2020_all,
original_abundances = NJ2020_original_abundances,
methods = c(1:12),
SR_calc = TRUE,
BP_endpoint = TRUE,
burst_sizes = c(),
bacterial_secondary_production = NULL,
nutrient_content_bacteria = list(),
nutrient_content_viruses = list(),
write_output = FALSE,
output_dir = "")
#> [1] "Following populations will be analyzed: c_Bacteria, c_HNA, c_LNA, c_Viruses, c_V1, c_V2, c_V3"
#> [1] "Following populations will be analyzed: c_Bacteria, c_HNA, c_LNA, c_Viruses, c_V1, c_V2, c_V3"
#> [1] "Processing using method: vp_linear_allpoints"
#> [1] "Analysis done for method vp_linear_allpoints. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_linear_separate_replicates"
#> [1] "Analysis done for method vp_linear_separate_replicates. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_linear_average_replicates"
#> [1] "Analysis done for method vp_linear_average_replicates. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_linear_average_replicates_diff"
#> [1] "Analysis done for method vp_linear_average_replicates_diff. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_linear_average_replicates_diff_LMER"
#> [1] "Analysis done for method vp_linear_average_replicates_diff_LMER. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_separate_replicates"
#> [1] "Analysis done for method vp_VIPCAL_separate_replicates. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_average_replicates"
#> [1] "Analysis done for method vp_VIPCAL_average_replicates. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_average_replicates_SE"
#> [1] "Analysis done for method vp_VIPCAL_average_replicates_SE. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_average_replicates_diff"
#> [1] "Analysis done for method vp_VIPCAL_average_replicates_diff. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_average_replicates_diff_SE"
#> [1] "Analysis done for method vp_VIPCAL_average_replicates_diff_SE. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_average_replicates_diff_LMER"
#> [1] "Analysis done for method vp_VIPCAL_average_replicates_diff_LMER. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_average_replicates_diff_LMER_SE"
#> [1] "Analysis done for method vp_VIPCAL_average_replicates_diff_LMER_SE. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_linear_separate_replicates, only separate replicate results"
#> [1] "Analysis done for method vp_linear_separate_replicates. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Processing using method: vp_VIPCAL_separate_replicates, only separate replicate results"
#> [1] "Analysis done for method vp_VIPCAL_separate_replicates. Please check calc_vp_error_list and calc_vp_warn_list for any error or warnings."
#> [1] "Default values used for burst size!"
#> [1] "Default value used for bacterial secondary production!"
#> [1] "Default values used for nutrient content of bacteria!"
#> [1] "Default values used for nutrient content of viruses!"
#> [1] "Default values used for burst size!"
#> [1] "Default value used for bacterial secondary production!"
#> [1] "Default values used for nutrient content of bacteria!"
#> [1] "Default values used for nutrient content of viruses!"
#> [1] "Default values used for burst size!"
#> [1] "Default value used for bacterial secondary production!"
#> [1] "Default values used for nutrient content of bacteria!"
#> [1] "Default values used for nutrient content of viruses!"After running the wrapper function, check the global environment to access its output:
ls()
#> [1] "analyzed_vp_results_df" "analyzed_vp_results_dictionary"
#> [3] "calc_vp_error_list" "calc_vp_warn_list"
#> [5] "data_NJ2020_all" "list_of_methods"
#> [7] "NJ2020_original_abundances" "plot_list"
#> [9] "populations_to_analyze" "vp_results_output_BP_df"
#> [11] "vp_results_output_df" "vp_results_output_SR_df"
#> [13] "vp_results_output_T24_df"The calculation step provides by default four data frames:
- vp_results_output_df contains the viral production results for all samples.
- vp_results_output_T24_df contains the viral production results for all samples at the end of the assay.
- vp_results_output_SR_df contains the viral production results of the separate replicate treatment, no averaging over replicates.
- vp_results_output_BP_df contains the viral production results for all samples with the bacterial endpoint taken into account.
head(vp_results_output_df)
#> Location Station_Number Depth Time_Range Population Sample_Type VP
#> 1 NJ2020 2 1 T0_T3 c_Viruses VP -864818.8
#> 2 NJ2020 2 1 T0_T6 c_Viruses VP -800817.3
#> 3 NJ2020 2 1 T0_T17.5 c_Viruses VP 1551505.8
#> 4 NJ2020 2 1 T0_T20.5 c_Viruses VP 2223299.7
#> 5 NJ2020 2 1 T0_T24 c_Viruses VP 2899833.4
#> 6 NJ2020 6 1 T0_T3 c_Viruses VP -170717.8
#> abs_VP VP_SE VP_R_Squared VP_Method
#> 1 -2594456.3 50303.36 0.98664740 LM_ALLPOINTS
#> 2 -4804904.1 70874.29 0.94802116 LM_ALLPOINTS
#> 3 27151351.0 313474.82 0.71011430 LM_ALLPOINTS
#> 4 45577643.0 395260.81 0.70877756 LM_ALLPOINTS
#> 5 69596000.7 458625.48 0.71417750 LM_ALLPOINTS
#> 6 -512153.5 396013.09 0.04439726 LM_ALLPOINTSThe analyze step enhances the viral production results by adding
various parameters to estimate virus-mediated microbial mortality. An
additional data frame provides descriptions and units for each variable.
str(analyzed_vp_results_df)
#> 'data.frame': 288 obs. of 51 variables:
#> $ Location : chr "NJ2020" "NJ2020" "NJ2020" "NJ2020" ...
#> $ Station_Number : int 2 6 2 6 2 6 2 6 2 6 ...
#> $ Depth : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ Time_Range : chr "T0_T24" "T0_T24" "T0_T24" "T0_T24" ...
#> $ Population : chr "c_Viruses" "c_Viruses" "c_Viruses" "c_Viruses" ...
#> $ Sample_Type : chr "VP" "VP" "VP" "VP" ...
#> $ VP : num 2899833 -134616 2899833 -134616 2899833 ...
#> $ abs_VP : num 69596001 -3230789 69596001 -3230789 69596001 ...
#> $ VP_SE : num 458625 127765 693869 44049 592214 ...
#> $ VP_R_Squared : num 0.7142 0.0649 0.8514 0.3284 0.857 ...
#> $ VP_Method : chr "LM_ALLPOINTS" "LM_ALLPOINTS" "LM_SR_AVG" "LM_SR_AVG" ...
#> $ B_0 : num 856930 1258635 856930 1258635 856930 ...
#> $ B_OS : num 3231054 4702883 3231054 4702883 3231054 ...
#> $ V_OS : num 2.05e+08 1.53e+08 2.05e+08 1.53e+08 2.05e+08 ...
#> $ c_VP : num 10933826 -502993 10933826 -502993 10933826 ...
#> $ c_abs_VP : num 262411815 -12071822 262411815 -12071822 262411815 ...
#> $ c_VP_SE : num 1729248 477393 2616233 164588 2232943 ...
#> $ P_Cells_BS_10 : num 3062.2 -95.9 3062.2 -95.9 3062.2 ...
#> $ Rate_BS_10 : num 1093383 -50299 1093383 -50299 1093383 ...
#> $ P_BP_Lysed_BS_10: num 405 -18.6 405 -18.6 405 ...
#> $ P_B_Loss_BS_10 : num 812.2 -25.7 812.2 -25.7 812.2 ...
#> $ P_Cells_BS_25 : num 1224.9 -38.4 1224.9 -38.4 1224.9 ...
#> $ Rate_BS_25 : num 437353 -20120 437353 -20120 437353 ...
#> $ P_BP_Lysed_BS_25: num 161.98 -7.45 161.98 -7.45 161.98 ...
#> $ P_B_Loss_BS_25 : num 324.9 -10.3 324.9 -10.3 324.9 ...
#> $ P_Cells_BS_40 : num 766 -24 766 -24 766 ...
#> $ Rate_BS_40 : num 273346 -12575 273346 -12575 273346 ...
#> $ P_BP_Lysed_BS_40: num 101.24 -4.66 101.24 -4.66 101.24 ...
#> $ P_B_Loss_BS_40 : num 203.04 -6.42 203.04 -6.42 203.04 ...
#> $ V_TT : num 0.05342 -0.00329 0.05342 -0.00329 0.05342 ...
#> $ DOC_V : num 3.63e-10 -1.67e-11 3.63e-10 -1.67e-11 3.63e-10 ...
#> $ DON_V : num 1.43e-10 -6.59e-12 1.43e-10 -6.59e-12 1.43e-10 ...
#> $ DOP_V : num 5.20e-11 -2.39e-12 5.20e-11 -2.39e-12 5.20e-11 ...
#> $ DOC_B_BS_10 : num 2.08e-08 -9.56e-10 2.08e-08 -9.56e-10 2.08e-08 ...
#> $ Total_DOC_BS_10 : num 2.11e-08 -9.72e-10 2.11e-08 -9.72e-10 2.11e-08 ...
#> $ DON_B_BS_10 : num 5.47e-09 -2.51e-10 5.47e-09 -2.51e-10 5.47e-09 ...
#> $ Total_DON_BS_10 : num 5.61e-09 -2.58e-10 5.61e-09 -2.58e-10 5.61e-09 ...
#> $ DOP_B_BS_10 : num 8.75e-10 -4.02e-11 8.75e-10 -4.02e-11 8.75e-10 ...
#> $ Total_DOP_BS_10 : num 9.27e-10 -4.26e-11 9.27e-10 -4.26e-11 9.27e-10 ...
#> $ DOC_B_BS_25 : num 8.31e-09 -3.82e-10 8.31e-09 -3.82e-10 8.31e-09 ...
#> $ Total_DOC_BS_25 : num 8.67e-09 -3.99e-10 8.67e-09 -3.99e-10 8.67e-09 ...
#> $ DON_B_BS_25 : num 2.19e-09 -1.01e-10 2.19e-09 -1.01e-10 2.19e-09 ...
#> $ Total_DON_BS_25 : num 2.33e-09 -1.07e-10 2.33e-09 -1.07e-10 2.33e-09 ...
#> $ DOP_B_BS_25 : num 3.50e-10 -1.61e-11 3.50e-10 -1.61e-11 3.50e-10 ...
#> $ Total_DOP_BS_25 : num 4.02e-10 -1.85e-11 4.02e-10 -1.85e-11 4.02e-10 ...
#> $ DOC_B_BS_40 : num 5.19e-09 -2.39e-10 5.19e-09 -2.39e-10 5.19e-09 ...
#> $ Total_DOC_BS_40 : num 5.56e-09 -2.56e-10 5.56e-09 -2.56e-10 5.56e-09 ...
#> $ DON_B_BS_40 : num 1.37e-09 -6.29e-11 1.37e-09 -6.29e-11 1.37e-09 ...
#> $ Total_DON_BS_40 : num 1.51e-09 -6.95e-11 1.51e-09 -6.95e-11 1.51e-09 ...
#> $ DOP_B_BS_40 : num 2.19e-10 -1.01e-11 2.19e-10 -1.01e-11 2.19e-10 ...
#> $ Total_DOP_BS_40 : num 2.71e-10 -1.25e-11 2.71e-10 -1.25e-11 2.71e-10 ...analyzed_vp_results_dictionary
#> Variable Unit
#> 1 Location /
#> 2 Station_Number /
#> 3 Depth m
#> 4 Time_Range h
#> 5 Population /
#> 6 Sample_Type /
#> 7 VP #VLP (virus-like particles)/mLh
#> 8 abs_VP #VLP/mL
#> 9 VP_SE /
#> 10 VP_R_Squared /
#> 11 VP_Method /
#> 12 B_0 #bacteria/mL
#> 13 B_OS #bacteria/mL
#> 14 V_OS #viruses/mL
#> 15 c_VP #VLP/mLh
#> 16 c_abs_VP #VLP/mL
#> 17 c_VP_SE /
#> 18 P_Cells_BS_10 %
#> 19 Rate_BS_10 #VLP/mLh
#> 20 P_BP_Lysed_BS_10 %
#> 21 P_B_Loss_BS_10 %
#> 22 P_Cells_BS_25 %
#> 23 Rate_BS_25 #VLP/mLh
#> 24 P_BP_Lysed_BS_25 %
#> 25 P_B_Loss_BS_25 %
#> 26 P_Cells_BS_40 %
#> 27 Rate_BS_40 #VLP/mLh
#> 28 P_BP_Lysed_BS_40 %
#> 29 P_B_Loss_BS_40 %
#> 30 V_TT 1/h
#> 31 DOC_V g C/mLh
#> 32 DON_V g N/mLh
#> 33 DOP_V g P/mLh
#> 34 DOC_B_BS_10 g C/mLh
#> 35 Total_DOC_BS_10 g C/mLh
#> 36 DON_B_BS_10 g N/mLh
#> 37 Total_DON_BS_10 g N/mLh
#> 38 DOP_B_BS_10 g P/mLh
#> 39 Total_DOP_BS_10 g P/mLh
#> 40 DOC_B_BS_25 g C/mLh
#> 41 Total_DOC_BS_25 g C/mLh
#> 42 DON_B_BS_25 g N/mLh
#> 43 Total_DON_BS_25 g N/mLh
#> 44 DOP_B_BS_25 g P/mLh
#> 45 Total_DOP_BS_25 g P/mLh
#> 46 DOC_B_BS_40 g C/mLh
#> 47 Total_DOC_BS_40 g C/mLh
#> 48 DON_B_BS_40 g N/mLh
#> 49 Total_DON_BS_40 g N/mLh
#> 50 DOP_B_BS_40 g P/mLh
#> 51 Total_DOP_BS_40 g P/mLh
#> Description
#> 1 Location of the experiment
#> 2 Number of station where experiment is conducted
#> 3 Depth at which experiment is performed
#> 4 Timepoints of sampling data, expressed in a time range: starting at T = 0 until time of measuring X => T0_TX
#> 5 Population Types: c_Viruses covers the entire virus population, while c_V1, c_V2, and c_V3 represent subpopulations
#> 6 Sample Types: VP rfor lytic viral production, Diff for lysogenic viral production, VPC for both
#> 7 Viral production rate: the mean viral production rate during the current Time_Range
#> 8 Absolute viral production at current timepoint
#> 9 The standard error on the viral production rate
#> 10 R-squared value: goodness of fit of the linear regression model
#> 11 Calculation method of viral production
#> 12 Bacterial abundance at the beginning of the experiment (T0)
#> 13 Bacterial abundance in the original sample
#> 14 Viral abundance in the original sample
#> 15 Corrected viral production rate: viral production rate in the original sample
#> 16 Corrected absolute viral production: absolute viral production in the original sample
#> 17 Corrected standard error on the viral production rate
#> 18 Percentage of cells for given burst size: % lytically infected cells for VP samples, % lysogenic cells for Diff samples
#> 19 Rate of bacteria for given burst size: lysis rate of bacteria for VP samples, lysogenic rate of bacteria for Diff samples
#> 20 Percentage of bacterial production lysed: the quantity of bacterial biomass that undergoes lysis
#> 21 Percentage of bacterial loss per day: the rate at which bacteria are removed due to viral lysis
#> 22 Percentage of cells for given burst size: % lytically infected cells for VP samples, % lysogenic cells for Diff samples
#> 23 Rate of bacteria for given burst size: lysis rate of bacteria for VP samples, lysogenic rate of bacteria for Diff samples
#> 24 Percentage of bacterial production lysed: the quantity of bacterial biomass that undergoes lysis
#> 25 Percentage of bacterial loss per day: the rate at which bacteria are removed due to viral lysis
#> 26 Percentage of cells for given burst size: % lytically infected cells for VP samples, % lysogenic cells for Diff samples
#> 27 Rate of bacteria for given burst size: lysis rate of bacteria for VP samples, lysogenic rate of bacteria for Diff samples
#> 28 Percentage of bacterial production lysed: the quantity of bacterial biomass that undergoes lysis
#> 29 Percentage of bacterial loss per day: the rate at which bacteria are removed due to viral lysis
#> 30 Viral turnover time: time to replacte the current virus population by new viruses
#> 31 Dissolved organic carbon release of viruses
#> 32 Dissolved organic nitrogen release of viruses
#> 33 Dissolved organic phosphorus release of viruses
#> 34 Dissolved organic carbon release of bacteria for given burst size
#> 35 Total dissolved organic carbon release for given burst size
#> 36 Dissolved organic nitrogen release of bacteria for given burst size
#> 37 Total dissolved organic nitrogen release for given burst size
#> 38 Dissolved organic phosphorus release of bacteria for given burst size
#> 39 Total dissolved organic phosphorus release of bacteria for given burst size
#> 40 Dissolved organic carbon release of bacteria for given burst size
#> 41 Total dissolved organic carbon release for given burst size
#> 42 Dissolved organic nitrogen release of bacteria for given burst size
#> 43 Total dissolved organic nitrogen release for given burst size
#> 44 Dissolved organic phosphorus release of bacteria for given burst size
#> 45 Total dissolved organic phosphorus release of bacteria for given burst size
#> 46 Dissolved organic carbon release of bacteria for given burst size
#> 47 Total dissolved organic carbon release for given burst size
#> 48 Dissolved organic nitrogen release of bacteria for given burst size
#> 49 Total dissolved organic nitrogen release for given burst size
#> 50 Dissolved organic phosphorus release of bacteria for given burst size
#> 51 Total dissolved organic phosphorus release of bacteria for given burst sizeThe visualize step generates a list of plot objects. The package
offers eight different ways to visualize the original viral count data,
calculated viral production data, or analyzed viral production data.
names(plot_list)
#> [1] "NJ2020_Station_2_Depth_1_Overview"
#> [2] "NJ2020_Station_6_Depth_1_Overview"
#> [3] "NJ2020_Collision_Rates"
#> [4] "NJ2020_Comparison_Linear_Methods"
#> [5] "NJ2020_Comparison_VIPCAL_Methods"
#> [6] "NJ2020_Comparison_LM_VIPCAL_VIPCAL_SE"
#> [7] "NJ2020_Comparison_ALL"
#> [8] "NJ2020_Comparison_VIPCAL_VIPCAL_SE"
#> [9] "NJ2020_Comparison_VIPCAL_VIPCAL_SE_ROGME"
#> [10] "NJ2020_Percentage_Cells"
#> [11] "NJ2020_Total_Nutrient_Release"For instance, let’s start with an overview of the count data of Station 2 for each sample type. The bacterial endpoint for this assay is highlighted in pink. It’s worth noting that stopping the assay at this point is advisable to prevent an increase in collision rates in VP samples, which may result in higher-than-expected lytic viral production.
grid::grid.draw(plot_list[[1]]$plot_object)
Additionally, the package provides multiple ways for calculating viral
production. While linear regression uses the slope between the count
data, VIPCAL looks at the average of increments to determine viral
production. VIPCAL-SE takes it a step further by considering standard
errors, ensuring that only true increments are used for calculation. As
shown in the figures, VIPCAL may overestimate viral production, while
VIPCAL-SE tends to be more conservative.
plot_list[[6]]$plot_object
plot_list[[9]]$plot_object
One of the analyzed variables considers the nutrient release from bacteria and viruses, mapping the total nutrient release in the sample at the end of the assay.
plot_list[[11]]$plot_object
Now that you have explored the available functions and the wrapper
function, you are ready to harness the full potential of the viralprod
package for your own viral production data analysis. A more detailed and
comprehensive tutorial on the package’s functionality and usage, can be
found in the Introduction to viralprod vignette. This vignette
provides in-depth insights into various underlying methods and features
of the package, enabling you to leverage the package’s capabilities
effectively.
MIT. See LICENSE.md
This software is developed for scientific, educational and research purposes. It is not meant to be used for legal or economical purposes.