1 PvSeroApp in R Tutorial

1.1 Data Analysis: `runPvSeroPipeline()`

Run this global function runPvSeroPipeline() embedded within the {SeroTrackR} R package! This function contains all of the steps in order of how to perform the Plasmodium vivax serology test and treat protocol as found in our application!

1.1.1 Visualisation of the PvSeroApp Pipeline

1.1.2 Using Tutorial Dataset: Load the Data

We will be using the build-in files in the R package for this tutorial, as shown below.

library(SeroTrackR)
library(tidyverse)

your_raw_data <- c(
  system.file("extdata", "example_MAGPIX_plate1.csv", package = "SeroTrackR"),
  system.file("extdata", "example_MAGPIX_plate2.csv", package = "SeroTrackR"), 
  system.file("extdata", "example_MAGPIX_plate3.csv", package = "SeroTrackR")
)
your_plate_layout <- system.file("extdata", "example_platelayout_1.xlsx", package = "SeroTrackR")

To run your OWN data, follow the code below and uncomment (i.e., remove the hashtags):

your_raw_data <- c(
  "PATH/TO/YOUR/FILE/plate1.csv",
  "PATH/TO/YOUR/FILE/plate2.csv",
  "PATH/TO/YOUR/FILE/plate3.csv"
)
your_plate_layout <- "PATH/TO/YOUR/FILE/plate_layout.xlsx"

1.1.3 Run Classification: Yes

final_analysis <- runPvSeroPipeline(
  raw_data = your_raw_data, 
  plate_layout = your_plate_layout, 
  platform = "magpix", 
  location = "ETH", 
  experiment_name = "experiment1", 
  std_point = 10,
  classify = "Yes", 
  algorithm_type = "antibody_model", 
  sens_spec = "balanced"
)
#> PASS: File example_magpix_plate1.csv successfully validated.
#> PASS: File example_magpix_plate2.csv successfully validated.
#> PASS: File example_magpix_plate3.csv successfully validated.
#> Plate layouts correctly identified!
#> QC Processes completed.
#> MFI to RAU conversion completed.
#> Please write a standard point curve.
#> QC Plotting completed.
#> Pv classification completed.

1.1.3.1 Classification

This is a table containing the classification results (seropositive or seronegative) for each SampleID. In this case, the classification results are stored in the pred_class_max column as we chose the sens_spec = "balanced". If you change it to another type of threshold, then the suffix of that column will change accordingly.

You will also see the relative antibody unit (RAU) values (columns with antigen names), whether the sample passed QC check (QC_total) and the plate that they were run on.

final_analysis[[1]] %>%
  head() %>% 
  kable()

SampleID	Plate	QC_total	PvEBP	Pv-fam-a	PvMSP5	PvMSP1-19	PvMSP8	PvPTEX150	PvCSS	PvRBP2b	pred_class_max
ABC013	plate1	pass	0.0003339	0.0015045	0.0002163	0.0014567	0.0000195	0.0001591	0.0000772	0.0003714	seropositive
ABC097	plate2	pass	0.0004324	0.0015615	0.0001944	0.0013373	0.0000195	0.0001549	0.0000705	0.0009189	seropositive
ABC181	plate3	pass	0.0003822	0.0015832	0.0002144	0.0013711	0.0000195	0.0001582	0.0000710	0.0002070	seropositive
ABC022	plate1	pass	0.0200000	0.0200000	0.0007373	0.0194885	0.0006247	0.0003145	0.0006008	0.0004895	seropositive
ABC106	plate2	pass	0.0057123	0.0193731	0.0007458	0.0195240	0.0006263	0.0003077	0.0006480	0.0126203	seropositive
ABC190	plate3	pass	0.0098260	0.0200000	0.0007400	0.0200000	0.0006020	0.0003171	0.0006555	0.0175253	seropositive

1.1.3.2 Standard Curve Plot

The standard curve plots are generated from the antibody data from the standards you indicated in your plate layout (e.g. S1-S10) and Median Fluorescent Intensity (MFI) units are displayed in log10-scale. In the case of the PvSeroTaT multi-antigen panel, the antigens will be displayed and in general your standard curves should look relatively linear (only when the y-axis is on logarithmic scale).

final_analysis[[2]]
#> NULL

1.1.3.3 Bead Counts QC Plot

A summary of the bead counts for each plate well are displayed, with blue indicating there are sufficient beads (≥15) or red when there are not enough. If any of the wells are red, they should be double-checked manually and re-run on a new plate if required.

The function will inform you whether there are “No repeats necessary” or provide a list of samples to be re-run. In the example data, the beads in plate 2 wells A1 and A2 will need to be repeated

final_analysis[[3]] # Plot

final_analysis[[4]] # Samples to repeat 
#> # A tibble: 2 × 4
#>   Location SampleID Plate  QC   
#>   <chr>    <chr>    <chr>  <chr>
#> 1 A1       Blank1   plate2 fail 
#> 2 A2       Blank2   plate2 fail

1.1.3.4 Blanks QC Plot

The Median Fluorescent Intensity (MFI) units for each antigen is displayed for your blank samples. In general, each blank sample should have ≤50 MFI for each antigen, if they are higher they should be cross-checked manually.

In the example data, blank samples recorded higher MFI values for LF005 on plate 1 and should be checked to confirm this is expected from the assay.

final_analysis[[5]]

1.1.3.5 Model Output Plot

The automated data processing in this app allows you to convert your Median Fluorescent Intensity (MFI) data into Relative Antibody Units (RAU) by fitting a 5-parameter logistic function to the standard curve on a per-antigen level. The results from this log-log conversion should look relatively linear for each antigen.

final_analysis[[6]]
#> $`1`
#> Ignoring unknown labels:
#> • fill : "Antigen"

1.1.4 Run Classification: No

no_classification_final_analysis <- runPvSeroPipeline(
  raw_data = your_raw_data, 
  plate_layout = your_plate_layout, 
  platform = "magpix", 
  location = "ETH", 
  experiment_name = "experiment1", 
  std_point = 10,
  classify = "No", # key if you do NOT want any classification performed i.e., you do not have PvSeroTaT antigens 
  algorithm_type = "antibody_model", 
  sens_spec = "balanced"
)
#> PASS: File example_magpix_plate1.csv successfully validated.
#> PASS: File example_magpix_plate2.csv successfully validated.
#> PASS: File example_magpix_plate3.csv successfully validated.
#> Plate layouts correctly identified!
#> QC Processes completed.
#> MFI to RAU conversion completed.
#> Please write a standard point curve.
#> QC Plotting completed.
#> No Classification Performed.

1.1.4.1 MFI and RAU Data

no_classification_final_analysis[[1]]  %>%
  head() %>% 
  kable()

SampleID	Plate	QC_total	PvEBP_MFI	PvEBP_Dilution	Pv-fam-a_MFI	Pv-fam-a_Dilution	PvMSP5_MFI	PvMSP5_Dilution	PvMSP1-19_MFI	PvMSP1-19_Dilution	PvMSP8_MFI	PvMSP8_Dilution	PvPTEX150_MFI	PvPTEX150_Dilution	PvCSS_MFI	PvCSS_Dilution	PvRBP2b_MFI	PvRBP2b_Dilution
ABC013	plate1	pass	2712	0.0003339	1569.0	0.0015045	673	0.0002163	327	0.0014567	182.0	1.95e-05	936.0	0.0001591	223	0.0000772	1068.0	0.0003714
ABC014	plate1	pass	134	0.0000216	378.0	0.0004204	117	0.0000277	197	0.0010331	58.0	1.95e-05	122.0	0.0000225	93	0.0000298	465.5	0.0001536
ABC015	plate1	pass	182	0.0000232	209.0	0.0002466	208	0.0000616	374	0.0015985	221.5	1.95e-05	293.0	0.0000535	868	0.0002235	463.0	0.0001527
ABC016	plate1	pass	152	0.0000222	229.5	0.0002688	101	0.0000244	89	0.0005994	48.0	1.95e-05	109.0	0.0000220	110	0.0000383	591.0	0.0001989
ABC017	plate1	pass	1135	0.0001478	236.0	0.0002758	299	0.0000950	507	0.0019840	209.5	1.95e-05	1665.5	0.0002668	266	0.0000893	671.0	0.0002277
ABC018	plate1	pass	174	0.0000229	395.0	0.0004370	175	0.0000460	78	0.0005452	70.0	1.95e-05	294.5	0.0000537	92	0.0000296	103.0	0.0000238

1.1.4.2 QC Plots

Repeat the same steps as above to find the QC plots!

#### Standard Curve Plot
no_classification_final_analysis[[2]]
#> NULL

#### Bead Counts QC Plot
no_classification_final_analysis[[3]] # Plot

no_classification_final_analysis[[4]] # Samples to repeat 
#> # A tibble: 2 × 4
#>   Location SampleID Plate  QC   
#>   <chr>    <chr>    <chr>  <chr>
#> 1 A1       Blank1   plate2 fail 
#> 2 A2       Blank2   plate2 fail

#### Blanks QC Plot
no_classification_final_analysis[[5]]


#### Model Output Plot
no_classification_final_analysis[[6]]
#> $`1`
#> Ignoring unknown labels:
#> • fill : "Antigen"

1.1.5 Create a PDF Report

renderQCReport(
  your_raw_data, 
  your_plate_layout, 
  "magpix", 
  location = "ETH",
  path = "results/" # defaults to your current working directory
)

1.1 Data Analysis: runPvSeroPipeline()