---
title: "Qploidy"
date: "Last update: 2026-07-09"
vignette: >
  %\VignetteIndexEntry{Qploidy}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output:
  rmdformats::html_clean:
    highlight: kate
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

> [!IMPORTANT]
> **We moved and improved!** Check out **Qploidy2** at: 
[https://github.com/Breeding-Insight/Qploidy2](https://github.com/Breeding-Insight/Qploidy2)


This vignette describes how to use `Qploidy`, an R package designed for ploidy
and aneuploidy estimation using genotyping platform data. For a detailed
explanation of the `Qploidy` methodology, please refer to its publication.

### When does the `Qploidy` methodology work?

The `Qploidy` approach is effective under the following conditions:  
- Your marker data originates from **Axiom** or **Illumina genotyping arrays**.  
- Your marker data is derived from **targeted sequencing platforms** (e.g.,
  DArTag, GTseq, AgriSeq).  
- All DNA samples were prepared following the **same library preparation
  protocol**.  
- You know the **ploidy** of at least a subset of 60 samples *or* you know the
  **most common ploidy** in the dataset.  
- Your dataset includes **heterozygous samples**.  

### When does the `Qploidy` methodology NOT work?

The methodology will not be effective under the following circumstances:  
- Your marker data comes from **RADseq** or **GBS** (Genotyping-by-Sequencing)
  platforms.  
- You intend to **combine datasets from different sequencing batches**.  
   - For example: If you extracted DNA and sequenced two plates (192 samples)
     as one batch, and later sequenced an additional three plates (288 samples)
     as a second batch, you would need to analyze the two batches **separately**
     in `Qploidy`. Combining all 480 samples into a single analysis will lead
     to incorrect results.  
- You **do not have a subset of samples with known ploidy** or **lack a
  predominant ploidy** in your dataset.  
- Your samples consist of **inbred lines** (homozygous individuals).  

# Installation

To install the `Qploidy` package, you can use the following commands:

```{r, eval=FALSE}
# Install the latest stable version from CRAN
install.packages("Qploidy")

# Install the development version from GitHub
#install.packages("devtools")
devtools::install_github("cristianetaniguti/Qploidy")
```

# Input files

In this vignette, we demonstrate the workflow using a simulated VCF file containing 
50 samples and 500 genetic markers, allowing for a concise and computationally 
efficient example. We also present results derived from a real-world rose Axiom array 
dataset, which includes 524 garden rose cultivars and a total of 137,786 genetic markers.

## Target sequencing VCF file

Let's generate our simulated VCF file. This file contains 500 markers and 60
samples, with a mix of tetraploid, diploid, and triploid samples. The tetraploid
samples are used as the reference for standardization because they are the most
common ploidy in the dataset. 

```{r}
library(Qploidy)

# Simulate a VCF file with 500 markers and 60 samples
simulate_vcf(
  seed = 1234, file_path = "vcf_example_simulated.vcf",
  n_tetraploid = 35, n_diploid = 5, n_triploid = 10,
  n_markers = 500
)
```

```{r}
data <- qploidy_read_vcf(vcf_file = "vcf_example_simulated.vcf")
head(data)
```

## From Axiom array summary file

In case your samples were genotyped using the Axiom array platform, you should
have a `summary` file or `.CEL` files. If you have `.CEL` files, you can convert
them into a `summary` file using the `apt-probeset-genotype` plugin available in
the Analysis Power Tools (APT) platform from Thermo Fisher Scientific (Waltham,
MA). A `summary` is composed by a header that can have many lines and a body that 
looks like this:

```{r, echo=FALSE}
temp1 <- tempfile(fileext = ".txt")
simulate_axiom_summary(seed = 1234, file_path = temp1)
temp1 <- read.table(temp1, header = TRUE)
head(temp1)
```

In `Qploidy`, use the `read_axiom` function to read the `summary` file. It skips
the header and reads the body of the file. The function will return a data frame.

```{r, eval=FALSE}
data <- read_axiom("AxiomGT1_summary.txt")
head(data)
```

```
   MarkerName     SampleName         X         Y        R     ratio
1 AX-86752740      Lemon_Fiz  977.7548  793.4656 1771.220 0.4479768
2 AX-86752740   High_Voltage 1292.2205  582.7886 1875.009 0.3108191
3 AX-86752740    Lemon_Fiz.1  919.8055  870.9619 1790.767 0.4863624
4 AX-86752740 High_Voltage.1 1368.1266  645.9481 2014.075 0.3207170
5 AX-86752740     Brite_Eyes  238.5416 1242.7593 1481.301 0.8389648
6 AX-86752740 Morden_Blush.1  302.1575 1542.4037 1844.561 0.8361900
```

## From Illumina array 

In case you have a Illumina array summary file, `Qploidy` expects the
following format:

```
[Header]
GSGT Version    2.0.5
Processing Date 4/17/2023 9:31 AM
Content         specieX.bpm
Num SNPs        26251
Total SNPs      26251
Num Samples     100
Total Samples   100

[Data]
SNP Name   Sample ID   GC Score   Theta   X      Y      X Raw   Y Raw   Log R Ratio
mk-1       SAMP-2      0.6814     0.247   0.023  0.009  504     100     0.5211668
mk-10      SAMP-2      0.9204     0.000   0.006  0.000  346     27      -1.021583
mk-100     SAMP-2      0.1695     0.367   0.030  0.020  576     158     1.177404
mk-101     SAMP-2      0.8655     0.222   0.026  0.010  538     102     0.3459635
mk-103     SAMP-2      0.8932     0.000   0.019  0.000  465     18      0.7578704
mk-104     SAMP-2      0.5146     0.175   0.041  0.012  675     114     1.016254
mk-105     SAMP-2      0.8060     0.450   0.018  0.015  458     132     0.4305636
mk-106     SAMP-2      0.0000     0.386   0.002  0.002  315     57      NaN
mk-107     SAMP-2      0.4804     0.495   0.026  0.025  534     189     1.0151
```

Verify if you file look like this one. If confirmed, you can use the `read_illumina_array`
function to read the data. If you include more than one file, the function will
merge them into a single data frame. 

```{r, eval=FALSE}
data <- read_illumina_array(
  "data/illumina/Set1.txt", # This is a example code, data not available
  "data/illumina/Set2.txt",
  "data/illumina/Set3.txt"
)
head(input_data)
```

```
MarkerName     SampleName     X     Y     R     ratio
1        CT1         S1-2_1 0.023 0.009 0.032 0.2812500
2       CT10         S1-2_1 0.006 0.000 0.006 0.0000000
3      CT100         S1-2_1 0.030 0.020 0.050 0.4000000
4      CT101         S1-2_1 0.026 0.010 0.036 0.2777778
5      CT103         S1-2_1 0.019 0.000 0.019 0.0000000
6      CT104         S1-2_1 0.041 0.012 0.053 0.2264151
```

# Define Reference Samples - Choose Path (1) or (2)

`Qploidy` applies a standardization method to allele intensities or read counts to enable 
the comparison of copy numbers across the genome of a sample. To achieve this, it requires 
a set of reference samples. There are two ways to select these reference samples:

1. **If you have a subset of samples with known ploidy**, `Qploidy` will use this subset as 
the reference.  
2. **If you know the most common ploidy among your samples**, you can initially run `Qploidy` 
using all samples as the reference. From this first run, you can identify samples with the 
common ploidy, then select this subset to use as the reference in a second run of `Qploidy`.

Below are the steps to proceed for each case, tagged as (1) and (2):

## (1) Using a Subset with Known Ploidy

For this method, you will need to separate your subset of samples with known ploidy:

In our simulated example, the sample names tell us which ploidy they are. Let's just
create a vector with the tetraploid samples for us to use later:

```{r}
# All samples
all_samples <- unique(data$SampleName)
all_samples
tetraploid_samples <- all_samples[grep("Tetraploid", all_samples)]
tetraploid_samples
```

## (2) Using the Most Common Ploidy

For this method, you will use the entire dataset as the reference during the first 
round of the `Qploidy` run:

```{r, eval=FALSE}
data_reference <- data
```

# (1) and (2) Run Dosage Caller

To proceed, you need to determine the dosage for all reference samples. If you haven’t done 
this yet, you can use any suitable dosage caller.  

- For **array data**, we recommend using the 
[`fitPoly`]( https://CRAN.R-project.org/package=fitPoly) package.  
- For **sequencing data**, we suggest using the 
[`updog`](https://github.com/dcgerard/updog) or 
[`polyRAD`](https://github.com/lvclark/polyRAD) packages.  

These packages determine dosages by analyzing the distribution of allele intensities or read 
counts across all samples for each marker, using an informed ploidy model. You will provide 
either the **most common ploidy in your population** or the **known ploidy of your selected subset** 
as the informed ploidy.  

- **Path (2):** If you provide the most common ploidy, samples with different ploidy levels may 
receive incorrect dosages. However, these samples can be filtered out in the next step.  

**Warning:**  
Depending on the number of samples and markers, this step may take a significant amount of time to 
complete. It is highly recommended to run it on a high-performance computing system where you can 
utilize multiple cores and avoid issues with excessive RAM usage.

### Target sequencing VCF data

* If the VCF file already contains dosage/genotype information called for the subset/most 
common ploidy:

```{r}
genos <- qploidy_read_vcf("vcf_example_simulated.vcf", geno = TRUE)
dim(genos)

genos.pos <- qploidy_read_vcf("vcf_example_simulated.vcf", geno.pos = TRUE)
head(genos.pos)
```

* If the VCF file lacks marker names in the `ID` column of the fixed portion, 
  the `MarkerName` column will contain `NAs`. To resolve this, you can use the 
  following code to concatenate the chromosome and position as marker names:

```{r, eval=FALSE}
genos.pos$MarkerName <- paste0(genos.pos$Chromosome, "_", genos.pos$Position)
head(genos.pos)
```

#### (1) Filter the genos object to keep only the known ploidy samples subset

```{r}
genos <- genos[which(genos$SampleName %in% tetraploid_samples), ]
```

* If the VCF file doesn't contain dosage/genotype information (GT format field) called 
for the subset/most common ploidy:

```{r}
library(tidyr)

# Prepare inputs for updog
## Approach (1)
# tobe_genotyped <- data[which(data$SampleName %in% tetraploid_samples),]

## Approach (2)
tobe_genotyped <- data

ref <- pivot_wider(tobe_genotyped[, 1:3], names_from = SampleName, values_from = X)
ref <- as.matrix(ref)
rownames_ref <- ref[, 1]
ref <- ref[, -1]
ref <- apply(ref, 2, as.numeric)
rownames(ref) <- rownames_ref

size <- pivot_wider(tobe_genotyped[, c(1, 2, 5)], names_from = SampleName, values_from = R)
size <- as.matrix(size)
rownames_size <- size[, 1]
size <- size[, -1]
size <- apply(size, 2, as.numeric)
rownames(size) <- rownames_size

size[1:10, 1:10]
ref[1:10, 1:10]
```

```{r}
library(updog)
multidog_obj <- multidog(
  refmat = ref,
  sizemat = size,
  model = "norm", # It depends of your population structure
  ploidy = 4, # Most common ploidy in your population
  nc = 6
) # Change the parameters accordingly!!

genos <- data.frame(
  MarkerName = multidog_obj$inddf$snp,
  SampleName = multidog_obj$inddf$ind,
  geno = multidog_obj$inddf$geno,
  prob = multidog_obj$inddf$maxpostprob
)

head(genos)
```

Notice that, if you are following approach (2) diploids and triploids samples genotypes will be wrongly called as tetraploids. But, because 
they are not the most common samples in the dataset, they should not interfere significantly in the dosage call of the tetraploids. You can
go back to this step after a first round of ploidy evaluation and call the dosage of the tetraploids only.

### Array data

Here we show an option of genotype calling for array data using fitpoly software:

```{r, eval=FALSE}
library(fitPoly)
saveMarkerModels(
  ploidy = 4, # Most common ploidy among Texas Garden Roses collection
  data = data_reference, # output of the previous function
  filePrefix = "fitpoly_output/texas_roses", # Define the path and prefix for the result files
  ncores = 2
) # Change it accordingly to your system!!!!

library(vroom) # Package to speed up reading files
fitpoly_scores <- vroom("fitpoly_output/texas_roses_scores.dat")

genos <- data.frame(
  MarkerName = fitpoly_scores$MarkerName,
  SampleName = fitpoly_scores$SampleName,
  geno = fitpoly_scores$maxgeno,
  prob = fitpoly_scores$maxP
)
head(genos)
```

```
   MarkerName  SampleName geno      prob
1 AX-86752740 1000 Wishes    2 0.7831438
2 AX-86752740  10004-N008    2 0.9892338
3 AX-86752740  10037_N046    2 0.9844171
4 AX-86752740  10038-N001    2 0.9916813
5 AX-86752740  10043_N019    2 0.9871057
6 AX-86752740  10043_N049    2 0.9955792
```

Differently than a VCF file, a `summary` file does not contain the genomic positions of the markers.
You will need to provide these information in a separate file as the following example.

```{r, eval=FALSE}
# File containing markers genomic positions
genos.pos_file <- read.table("geno.pos_roses_texas.txt", header = T)
head(genos.pos)
```

```
            SNP      probes chr      pos
1 Affx-86843634 AX-86752747   6   255852
2 Affx-86842821 AX-86752763   6 62032267
3 Affx-86839613 AX-86752769   5  9169310
4 Affx-86838724 AX-86752790   1 44259488
5 Affx-86840823 AX-86752809   7 12991931
6 Affx-86842443 AX-86752817   2 53861719
```

```{r, eval=FALSE}
# Edit for Qploidy input format
genos.pos <- data.frame(
  MarkerName = genos.pos_file$probes,
  Chromosome = genos.pos_file$chr,
  Position = genos.pos_file$pos
)
head(genos.pos)
```

```
   MarkerName Chromosome Position
1 AX-86752747          6   255852
2 AX-86752763          6 62032267
3 AX-86752769          5  9169310
4 AX-86752790          1 44259488
5 AX-86752809          7 12991931
6 AX-86752817          2 53861719
```

# (1) and (2) Standardization

```{r, echo=FALSE}
temp_file <- tempfile(fileext = ".tsv.gz") # saving in a temporary file
simu_data_standardized <- standardize(
  data = data,
  genos = genos,
  geno.pos = genos.pos,
  ploidy.standardization = 4,
  threshold.n.clusters = 5,
  n.cores = 1,
  out_filename = temp_file,
  verbose = TRUE
)

simu_data_standardized
```


```{r, eval=FALSE}
simu_data_standardized <- standardize(
  data = data,
  genos = genos,
  geno.pos = genos.pos,
  ploidy.standardization = 4,
  threshold.n.clusters = 5,
  n.cores = 1,
  out_filename = "my_standardized_data.tsv.gz",
  verbose = TRUE
)

simu_data_standardized
```

How it look like for our real roses dataset:

```
This is on object of class 'ploidy_standardization'
--------------------------------------------------------------------
Parameters
                                                                        
1 standardization type:                                      intensities
2 Ploidy:                                                    4          
3 Minimum number of heterozygous classes (clusters) present: 5          
4 Maximum number of missing genotype by marker:              0.1        
5 Minimum genotype probability:                              0.8        
--------------------------------------------------------------------
Filters
                                                                                            
1 Number of markers at raw data:                                            137786 (100%)   
2 Percentage of filtered genotypes by probability threshold:                -      (16.79 %)
3 Number of markers filtered by missing data:                               5659   (4.11 %) 
4 Number of markers filtered for not having the minimum number of clusters: 101853 (73.92 %)
5 Number of markers filtered for not having genomic information:            252    (0.18 %) 
6 Number of markers with estimated BAF:                                     28100  (20.39 %)
```

See the details of the parameters with `?standardize`.

The `print` function of the resulting object provides information about marker filtering during the 
process. One critical filtering step involves the number of dosage clusters represented for each 
marker. If your samples are highly inbred at certain loci, you might not have individuals 
representing all possible dosages for those marker locations.  

For example, when using tetraploids as the reference, some markers may have individuals with 
dosages of only 0, 1, 3, and 4 (missing dosage 2). Without representatives for the missing 
dosage, `Qploidy` standardization cannot be performed.  

- If `threshold.n.clusters` is set to `ploidy + 1`, markers with missing dosages will be discarded.  
- If `threshold.n.clusters` is smaller than `ploidy + 1`, the missing dosage cluster centers will 
be imputed for standardization purposes.  

## Recover the `ploidy_standardization` Object from a Saved File

To revisit this step later without re-running the upstream functions, you can load the previously 
generated file using the `read_qploidy_standardization` function:

```{r, echo=FALSE}
simu_data_standardized <- read_qploidy_standardization(temp_file)
```

```{r, eval=FALSE}
simu_data_standardized <- read_qploidy_standardization("my_standardized_data.tsv.gz")
```

# (1) and (2) Plot results

`Qploidy` provides several options for visualization of the results. Check the complete description 
with `?plot_qploidy_standaridization`. Bellow you can see examples:

```{r}
# Select a sample to be evaluated
samples <- unique(simu_data_standardized$data$SampleName)
head(samples)
```

```{r}
sample <- "Tetraploid1"

# Proportion of heterozygous loci, BAF (Qploidy standardized ratio), and zscore by genomic positions
p <- plot_qploidy_standardization(
  x = simu_data_standardized,
  sample = sample,
  type = c("het", "BAF", "zscore"),
  dot.size = 0.05,
  chr = 1:2
)
p
```

See how it look like for our real roses dataset:

![](fig1.jpg)

```{r}
# Heterozygous frequency, BAF (Qploidy standardized ratio), and zscore by genomic positions
# centromere positions added
p <- plot_qploidy_standardization(
  x = simu_data_standardized,
  sample = sample,
  type = c("het", "BAF", "zscore"),
  dot.size = 0.05,
  chr = 1:2,
  add_centromeres = TRUE,
  centromeres = c("chr1" = 15000000, "chr2" = 19000000)
)

p
```

See how it look like for our real roses dataset:

![](fig2.jpg)

```{r}
# Raw allele intensity or read count ratio and BAF (Qploidy standardized ratio) histograms
# combining all markers in the sample (sample level resolution)
p <- plot_qploidy_standardization(
  x = simu_data_standardized,
  sample = sample,
  type = c("Ratio_hist_overall", "BAF_hist_overall"),
  chr = 1:2,
  ploidy = 4,
  add_expected_peaks = TRUE
)
p
```

Notice that, because it is a simulated data, the ratio and the BAF don't differ much. See how it look like for our real roses dataset:

![](fig3.jpg)

```{r}
# BAF histograms (chromosome level resolution) and Z score
p <- plot_qploidy_standardization(
  x = simu_data_standardized,
  sample = sample,
  type = c("BAF_hist", "zscore"),
  chr = 1:2,
  add_expected_peaks = TRUE,
  ploidy = c(4, 4)
)
p
```

See how it look like for our real roses dataset:

![](fig4.jpg)

```{r}
# BAF histograms combining all markers in the sample (chromosome-arm level resolution) and Z score
p <- plot_qploidy_standardization(
  x = simu_data_standardized,
  sample = sample,
  type = c("BAF_hist", "zscore"),
  chr = 1:2,
  ploidy = c(4, 4, 4, 4), # Provide ploidy for each arm
  add_expected_peaks = TRUE,
  add_centromeres = TRUE,
  centromeres = c("chr1" = 15000000, "chr2" = 19000000)
)

p
```

See how it look like for our real roses dataset:

![](fig5.jpg)

It is important to note that the quality of results can vary across samples, as observed in different 
plots. Key aspects to assess include:  
- How sharp the peaks are in the histogram plots.  
- How well the dots cluster in the BAF vs. genomic position plots.  
- Whether the patterns match the decay or increase of the Z-score (Z).  

In our publication, we categorize sample quality into different **resolutions** based on the ability 
to estimate copy numbers, ranked from highest to lowest resolution:  
1. **Chromosome-arm resolution**: When the copy number of all chromosome arms can be estimated.  
2. **Chromosome resolution**: When at least one chromosome arm’s copy number cannot be estimated.  
3. **Sample resolution**: When the copy number of at least one entire chromosome cannot be estimated.  
4. **None**: When the histogram peaks, considering all markers, do not fit any of the expected ploidy 
levels tested.  

For more details and examples, please refer to the publication.

# (1) and (2) Ploidy Estimation for All Samples

**Warning**: This method may not be fully accurate in certain situations. While it offers a helpful 
initial guide, **visual inspection** of the plots described in the previous section is essential to 
assess the resolution and confirm the ploidy.

```{r}
# If sample level resolution
estimated_ploidies_sample <- area_estimate_ploidy(
  qploidy_standardization = simu_data_standardized, # standardization result object
  samples = "all", # Samples or "all" to estimate all samples
  level = "sample", # Resolution level
  ploidies = c(2, 3, 4, 5)
) # Ploidies to be tested
estimated_ploidies_sample
```

See how it look like for our real roses dataset:

```
Object of class qploidy_area_ploidy_estimation                                                        
1 Number of samples:                     524            
2 Chromosomes:                           1,3,5,2,6,7,4,0
3 Tested ploidies:                       1,2,3,4,5      
4 Number of euploid samples:             459            
5 Number of potential aneuploid samples: 0              
6 Number of highly inbred samples:       65  
```

Highly inbred samples cannot be evaluated for copy number using this method. As a result, 
`Qploidy` returns a missing value (`NA`) for these cases.

```{r}
head(estimated_ploidies_sample$ploidy)
```


See how it look like for our real roses dataset:

```
               [,1]
Lemon_Fiz         4
High_Voltage      4
Lemon_Fiz.1       4
High_Voltage.1    4
Brite_Eyes        4
Morden_Blush.1    4
```

```{r}
# If chromosome resolution
estimated_ploidies_chromosome <- area_estimate_ploidy(
  qploidy_standardization = simu_data_standardized,
  samples = "all",
  level = "chromosome",
  ploidies = c(2, 3, 4, 5)
)
estimated_ploidies_chromosome
```


See how it look like for our real roses dataset:

```
Object of class qploidy_area_ploidy_estimation                                                        
1 Number of samples:                     524            
2 Chromosomes:                           0,1,2,3,4,5,6,7
3 Tested ploidies:                       1,2,3,4,5      
4 Number of euploid samples:             375            
5 Number of potential aneuploid samples: 92             
6 Number of highly inbred samples:       57          
```
```{r}
head(estimated_ploidies_chromosome$ploidy)
```


See how it look like for our real roses dataset:

```
               0 1 2 3 4 5 6 7
Lemon_Fiz      4 4 4 4 4 4 4 4
High_Voltage   4 4 4 4 4 4 4 4
Lemon_Fiz.1    4 4 4 4 4 4 4 4
High_Voltage.1 4 4 4 4 4 4 4 4
Brite_Eyes     4 4 4 4 4 4 4 4
Morden_Blush.1 4 4 4 4 4 4 4 4
```

```{r}
# If chromosome-arm resolution
estimated_ploidies_chromosome_arm <- area_estimate_ploidy(
  qploidy_standardization = simu_data_standardized,
  samples = "all",
  level = "chromosome-arm",
  ploidies = c(2, 3, 4, 5),
  centromeres = c("chr1" = 15000000, "chr2" = 19000000)
)

estimated_ploidies_chromosome_arm
```

See how it look like for our real roses dataset:

```
Object of class qploidy_area_ploidy_estimation                                                                                                  
1 Number of samples:                     524                                                      
2 Chromosomes:                           0,1.1,1.2,2.1,2.2,3.1,3.2,4.1,4.2,5.1,5.2,6.1,6.2,7.1,7.2
3 Tested ploidies:                       1,2,3,4,5                                                
4 Number of euploid samples:             304                                                      
5 Number of potential aneuploid samples: 171                                                      
6 Number of highly inbred samples:       49    
```

```{r}
head(estimated_ploidies_chromosome_arm$ploidy)
```


See how it look like for our real roses dataset:

```
               0 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 5.1 5.2 6.1 6.2 7.1 7.2
Lemon_Fiz      4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
High_Voltage   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
Lemon_Fiz.1    4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
High_Voltage.1 4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
Brite_Eyes     4   4   4   4   4   4   4   4   4   4   4   4   4   4   4
Morden_Blush.1 4   4   4   4   4  NA   4   4   4   4   4   4   4   4   4
```

Note that one of the chromosome arms (Morden_Blush.1 - 3.2) returned a value of `NA`. This occurs
 because the arm contains a high number of inbred loci.

```{r}
estimated_ploidies_format <- merge_arms_format(estimated_ploidies_chromosome_arm)
estimated_ploidies_format
```


See how it look like for our real roses dataset:

```
Object of class qploidy_area_ploidy_estimation                                                        
1 Number of samples:                     524            
2 Chromosomes:                           0,1,2,3,4,5,6,7
3 Tested ploidies:                       1,2,3,4,5      
4 Number of euploid samples:             304            
5 Number of potential aneuploid samples: 171            
6 Number of highly inbred samples:       49  
```
```{r}
head(estimated_ploidies_format$ploidy)
```

See how it look like for our real roses dataset:

```
               0   1   2   3      4   5   6   7  
Lemon_Fiz      "4" "4" "4" "4"    "4" "4" "4" "4"
High_Voltage   "4" "4" "4" "4"    "4" "4" "4" "4"
Lemon_Fiz.1    "4" "4" "4" "4"    "4" "4" "4" "4"
High_Voltage.1 "4" "4" "4" "4"    "4" "4" "4" "4"
Brite_Eyes     "4" "4" "4" "4"    "4" "4" "4" "4"
Morden_Blush.1 "4" "4" "4" "NA/4" "4" "4" "4" "4"
```

### Export ploidy result table

```{r, eval=FALSE}
write.csv(estimated_ploidies_format$ploidy, file = "ploidies_before_visual_evaluation.csv")
```

## (1) and (2) Save plots for all samples

Facilitate individual visual inspection by generating figures for all samples in a loop:

```{r, eval=FALSE}
dir.create("figures")

for (i in seq_along(samples)) {
  print(paste0("Part:", i, "/", length(samples)))
  print(paste("Generating figures for", samples[i], "..."))
  # This function will generate figures for sample, chromosome and chromosome-arm resolution level evaluation
  all_resolutions_plots(
    data_standardized = simu_data_standardized,
    sample = samples[i],
    ploidy = estimated_ploidies_chromosome$ploidy[i, 1:2],
    chr = 1:2,
    centromeres = c("chr1" = 15000000, "chr2" = 19000000),
    file_name = paste0("figures/", samples[i])
  )
  print(paste("Done!"))
}
```

By visualizing the plots, you can correct the results from `area_estimate_ploidy` and add resolution information for each sample. In our
perfect simulated data, all samples correctly identified therefore we will just move forward with the analysis.

```{r}
ploidy_corrected <- estimated_ploidies_chromosome$ploidy
```

# (2) Improve standardization selecting a known ploidy subset after first round

If you used the most common ploidy as the reference instead of a subset with known ploidy, the 
standardization may not be as accurate. You can improve the resolution by following these steps:  
i.   Select the highest resolution plots from the first standardization round.  
ii.  Filter these samples from the `data` object.  
iii. Run standardization again using this subset as the reference.  
iv.  Reevaluate the plots and estimate the ploidy once more.


```{r, eval=FALSE}
# i) Select the highest resolution plots from the first standardization round.
tetraploids <- apply(ploidy_corrected, 1, function(x) all(x == 4))
tetraploids

data_reference2 <- rownames(ploidy_corrected)[which(tetraploids)]
length(data_reference2) 

# ii) Filter these samples from the `data` object.
genos_round2 <- genos[which(genos$SampleName %in% data_reference2), ]

# iii) Run standardization again using this subset as the reference.
simu_data_standardized_round2 <- standardize(
  data = data,
  genos = genos_round2,
  geno.pos = genos.pos,
  ploidy.standardization = 4,
  threshold.n.clusters = 5,
  n.cores = 1,
  out_filename = "my_round2_standardization.tsv.gz",
  verbose = TRUE
)
simu_data_standardized_round2
```

For our perfect simulated data, plot resolution didn't change between first and second round 
but see how it look like for our real roses dataset:

```
This is on object of class 'ploidy_standardization'
--------------------------------------------------------------------
Parameters
                                                                        
1 standardization type:                                      intensities
2 Ploidy:                                                    4          
3 Minimum number of heterozygous classes (clusters) present: 5          
4 Maximum number of missing genotype by marker:              0.1        
5 Minimum genotype probability:                              0.8        
--------------------------------------------------------------------
Filters
                                                                                            
1 Number of markers at raw data:                                            137786 (100%)   
2 Percentage of filtered genotypes by probability threshold:                -      (16.34 %)
3 Number of markers filtered by missing data:                               6108   (4.43 %) 
4 Number of markers filtered for not having the minimum number of clusters: 111977 (81.27 %)
5 Number of markers filtered for not having genomic information:            145    (0.11 %) 
6 Number of markers with estimated BAF:                                     17634  (12.8 %) 
```

New plots for roses real data after round 1 and 2 of standardization:


![Round 1](fig1.jpg)

![Round 2](fig7.jpg)

![Round 1](fig4.jpg)

![Round 2](fig6.jpg)

# How to cite

Taniguti, C. H., Lau, J., Hochhaus, T., Arias, D. C. L., Hokanson, S. C., Zlesak, D. C., Byrne, D. H.,
 Klein, P. E., & Riera-Lizarazu, O. (2025). Exploring chromosomal variations in garden roses: Insights 
 from high-density SNP array data and a new tool, Qploidy. The Plant Genome, e70044. 
 https://doi.org/10.1002/tpg2.70044

# Acknowledgments

### Version 1.0.0

Funded in part by the Robert E. Basye Endowment in Rose Genetics, Dept. of Horticultural Sciences, 
Texas A&M University, and USDA’s National Institute of Food and Agriculture (NIFA), Specialty Crop 
Research Initiative (SCRI) projects: ‘‘Tools for Genomics-Assisted Breeding in Polyploids: Development 
of a Community Resource’’ (Award No. 2020-51181-32156); and ‘‘Developing Sustainable Rose Landscapes via 
Rose Rosette Disease Education, Socioeconomic Assessments, and Breeding RRD-Resistant Roses with Stable 
Black Spot Resistance’’ (Award No. 2022-51181-38330).

### Versions > 1.0.0

Supported by [Breeding Insight](https://breedinginsight.org/).
