GigaMUGA_Reference_QC.Rmd

---
title: "Reference Data QC: GigaMUGA"
site: workflowr::wflow_site
output:
  workflowr::wflow_html:
    toc: true
    toc_float: true
    toc_collapsed: true
    toc_depth: 3
    code_folding: hide
editor_options:
  chunk_output_type: console
---

# GigaMUGA Annotations

```{r setup, echo=FALSE, include=FALSE}
knitr::opts_chunk$set(message = FALSE)
# require(tidyverse)
# ^ this if running locally
require(fst)
require(dplyr)
require(tidyr)
require(stringr)
require(parallel)
# ^ these if running on sumner
require(data.table)
require(purrr)
require(furrr)
require(qtl2)
require(magrittr)
require(DT)
require(plotly)
require(progress)
require(ape)
require(RColorBrewer)
require(tictoc)
require(furrr)
require(vroom)
today <- format(Sys.Date(), format="%Y%m%d")
QCtheme <- theme_bw() + 
  theme(panel.grid = element_blank(),
        axis.text = element_text(colour = "black"),
        axis.title = element_text(colour = "black"))
writeReferenceGenotypes <- function(founder_file, nonfounder_file){
  if(file.exists(paste0("data/GigaMUGA/",founder_file))){
    founder_sample_genos <- fst::read.fst(paste0("data/GigaMUGA/GigaMUGA_founder_sample_genotypes/",founder_file)) %>%
      dplyr::filter(!marker %in% above.cutoff$marker) # should be clean, but just in case
    non_founder_sample_genos <- fst::read.fst(paste0("data/GigaMUGA/GigaMUGA_reference_genotypes/",nonfounder_file)) %>%
      dplyr::filter(!sample_id %in% colnames(founder_sample_genos)[-1],
                    !sample_id %in% bad_samples$sample_id,
                    !marker %in% above.cutoff$marker) %>%
      dplyr::select(sample_id, marker, genotype) %>%
      dplyr::mutate(genotype = dplyr::if_else(genotype == "-", true = "N", false = genotype)) %>%
      tidyr::pivot_wider(names_from = sample_id, values_from = genotype)
    
    reference_sample_genos <- founder_sample_genos %>%
      dplyr::left_join(., non_founder_sample_genos)
    
    reference_sample_genos[is.na(reference_sample_genos)] <- "N"
    return(reference_sample_genos)
  } else {
    non_founder_sample_genos_int <- fst::read.fst(paste0("data/GigaMUGA/GigaMUGA_reference_genotypes/",nonfounder_file)) %>%
      dplyr::filter(!sample_id %in% bad_samples$sample_id,
                    !marker %in% above.cutoff$marker) %>%
      dplyr::select(sample_id, marker, genotype)
    if(nrow(non_founder_sample_genos_int) > 0){
      non_founder_sample_genos <- non_founder_sample_genos_int %>%
        dplyr::mutate(genotype = dplyr::if_else(genotype == "-", true = "N", false = genotype)) %>%
        tidyr::pivot_wider(names_from = sample_id, values_from = genotype)
      
      reference_sample_genos <- non_founder_sample_genos
      reference_sample_genos[is.na(reference_sample_genos)] <- "N"
      return(reference_sample_genos)
    } else {
      print("NO REFERENCE GENOTYPES")
    }
  }
}
writeFounderConsensusGenotypes <- function(founder_file){
  
  founder_consensus_genos <- fst::read.fst(paste0("data/GigaMUGA/GigaMUGA_founder_consensus_genotypes/",founder_file)) %>%
    dplyr::filter(!marker %in% above.cutoff$marker) # should be clean, but just in case
  
  return(founder_consensus_genos)
}
writeReferenceIntensities <- function(nonfounder_file){
  
    reference_sample_intensities <- fst::read.fst(paste0("data/GigaMUGA/GigaMUGA_reference_genotypes/",nonfounder_file)) %>%
      dplyr::filter(!sample_id %in% bad_samples$sample_id,
                    !marker %in% above.cutoff$marker) %>%
      dplyr::select(sample_id, marker, X, Y)
    
    reference_x_intensities <- reference_sample_intensities %>%
      dplyr::select(-Y) %>%
      tidyr::pivot_wider(names_from = sample_id, values_from = X)
    
    reference_y_intensities <- reference_sample_intensities %>%
      dplyr::select(-X) %>%
      tidyr::pivot_wider(names_from = sample_id, values_from = Y)
    if(nrow(reference_x_intensities) == 0){
      print("NO REFERENCE INTENSITIES")
    } else {
      return(list(reference_x_intensities, reference_y_intensities)) 
    }
  
}

###########################################
# Function to quickly pull strain names from non-founder samples
# Inputs:
# Sample IDs

# Outputs:
# Character vector of strains
###########################################
findStrain <- function(x){
  
  # Decompose strain from mouse ID for males
  m_strains <- strsplit(x, split = "m")[[1]]
  
  # If the string was actually split using "m" the sample was male
  if(TRUE %in% str_detect(m_strains, pattern = "X12")){
    
    # If there was a 129 or similar mouse with an m in the strain name, some collapsing needs to occur
    X129_strains <- paste(strsplit(x, split = "m")[[1]][[1]], sep = "m", collapse = "m")
    return(X129_strains)
    
  } else if(length(m_strains) == 1){
    
    f_strains <- strsplit(x, split = "f")[[1]][[1]] 
    return(f_strains)
  } else {
    
    return(m_strains[[1]][[1]])
  }
  
}

```

## Loading in data from Sumner analyses
```{r}
load("data/GigaMUGA/GigaMUGA_QC_Results.RData")
load("data/GigaMUGA/GigaMUGA_SexCheck_Results.RData")
load("data/GigaMUGA/GigaMUGA_BadSamples_BadMarkers.RData")
gm_metadata <- vroom::vroom("data/GigaMUGA/gm_uwisc_v2.csv")
```

## Marker QC: Searching for missing genotype calls

```{r Markers with high "N" counts among reference samples}
no.calls <- control_allele_freqs_df %>%
  dplyr::ungroup() %>%
  dplyr::filter(genotype == "N") %>%
  tidyr::pivot_wider(names_from = genotype, 
                     values_from = n) %>%
  dplyr::left_join(., gm_metadata) %>%
  dplyr::select(marker, chr, bp_grcm39, freq) %>%
  dplyr::mutate(chr = as.factor(chr))

## Identifying markers with missing genotypes at a frequency higher than the 95th percentile of "N" frequencies across all markers
cutoff <- quantile(no.calls$freq, probs = seq(0,1,0.05))[[19]]
```

Of `r length(unique(as.factor(control_allele_freqs_df$marker)))` markers, `r nrow(no.calls)` failed to genotype at least one sample, and `r nrow(above.cutoff)` markers failed to genotype at least `r cutoff*100`% of samples.

```{r Plotting no calls, echo=FALSE}
# Distribution of N frequencies across all markers. Dotted line indicates the 95th percentile of N frequencies (cutoff).
ggplot(no.calls, mapping = aes(x = freq)) + 
  geom_histogram(bins = 100) +
  scale_x_continuous(breaks = seq(0,1,0.1)) + 
  geom_vline(xintercept = cutoff, linetype = 2) + 
  QCtheme + 
  labs(x = "Fraction of mice with missing genotypes",
       y = "Number of markers")
```

## Sample QC

### Searching for samples with poor marker representation

In a similar fashion, we calculated the number of reference samples with missing genotypes. Repeated observations of samples/strains with identical names meant that genotype counts for each marker among them couldn't be grouped and tallied, so determining no-call frequency occurred column-wise. Mouse over individual samples to see the number of markers with missing genotypes for each sample.

```{r Reference samples with high missingness}

## Interactive plot of the number of missing genotypes for each sample.
bad_sample_cutoff <- quantile(n.calls.strains.df$n.no.calls, probs = seq(0,1,0.05))[20]
sampleQC <- ggplot(n.calls.strains.df, 
                   mapping = aes(x = reorder(sample_id,n.no.calls), 
                                 y = n.no.calls,
                                 text = paste("Sample:", sample_id))) + 
  geom_point() +
  QCtheme + 
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) +
  geom_hline(yintercept = bad_sample_cutoff, linetype = 2) + 
  labs(x = "Number of mice with missing genotypes",
       y = "Number of markers")
ggplotly(sampleQC, tooltip = c("text","y"))

```

### Validating sex of reference samples

We next validated the sexes of each sample using sex chromosome probe intensities. We paired up probe intensities, joined available metadata, and filtered down to only markers covering the X and Y chromosomes. Then we flagged markers with high missingness across all samples, as well as samples with high missingness among all markers.The first round of inferring predicted sexes used a rough search of the sample name for expected nomenclature convention, which includes a sex denotation.

```{r Preliminary sex prediction}

## Tabulating the number of samples for each predicted sex
predicted.sex.table <- predicted.sexes %>%
  dplyr::group_by(predicted.sex) %>%
  dplyr::count() 

```

This captured `r predicted.sex.table[which(predicted.sex.table$predicted.sex == "f"),2]$n` female samples, `r predicted.sex.table[which(predicted.sex.table$predicted.sex == "m"),2]$n` male samples, leaving `r predicted.sex.table[which(predicted.sex.table$predicted.sex == "unknown"),2]$n` samples of unknown predicted sex from nomenclature alone.

```{r Predicted sex table, eval=FALSE, include=FALSE}
# Table of samples for which sex could not be predicted from sample name alone. Using one marker is fine as an example as the sample info for each marker is identical.
predicted.sexes %>%
  dplyr::filter(predicted.sex == "unknown")
```

After predicting the sexes of the vast majority of reference samples, we visualized the average probe intensity among X Chromosome markers for each sample, labeling them by predicted sex. Samples colored black were unabled to have their sex inferred by the sample name, but cluster well with mice for which sex could be inferred. Conversely, some samples' predicted sex is discordant with X and Y Chromosome marker intensities (i.e. blue samples that cluster with mostly orange samples, and vice versa). Mouse over individual dots to view the sample, as well as whether it was flagged for having many markers with missing genotype information. In many cases, pulling substrings of sample names as the sex of the sample was too sensitive and misclassified samples.

```{r Predicted sex visualization}
predicted.sex.plot.palettes <- sex.chr.intensities %>%
  dplyr::ungroup() %>%
  dplyr::distinct(sample_id, predicted.sex, high_missing_sample) %>%
  dplyr::mutate(predicted.sex.palette = dplyr::case_when(predicted.sex == "f" ~ "#5856b7",
                                                         predicted.sex == "m" ~ "#eeb868",
                                                         predicted.sex == "unknown" ~ "black"))
predicted.sex.palette <- predicted.sex.plot.palettes$predicted.sex.palette
names(predicted.sex.palette) <- predicted.sex.plot.palettes$predicted.sex

mean.x.intensities.by.sex.plot <- sex.chr.intensities %>%
  dplyr::mutate(high_missing_sample = dplyr::if_else(high_missing_sample == "FLAG", 
                                                     true = "Bad Samples", 
                                                     false = "Good Samples")) %>% 
  ggplot(., mapping = aes(x = mean.x.chr.int, 
                         y = mean.y.int, 
                         colour = predicted.sex,
                         text = sample_id)) + 
  geom_point() + 
  scale_colour_manual(values = predicted.sex.palette) + 
  facet_grid(.~high_missing_sample) + 
  QCtheme
ggplotly(mean.x.intensities.by.sex.plot, 
         tooltip = c("text"))

```

Because the split between inferred sexes of good samples was so distinct, we used k-means clustering to quickly match the clusters to sexed samples and assign or re-assign sexes to samples with unknown or apparently incorrect sex information, respectively. Samples highlighted above were also re-evaluated using strain-specific marker information.

```{r kmeans clustering}
# Prints a table of all samples with an option to view whether a sample had its sex redesignated.
reSexed_samples_table <- reSexed_samples %>%
  dplyr::mutate(resexed = predicted.sex != inferred.sex)
reSexed_samples_table %>% 
  dplyr::select(sample_id, resexed, predicted.sex, inferred.sex) %>%
  DT::datatable(., filter = "top", 
              escape = FALSE)
```

The plot below demonstrates that this clustering technique does a pretty good job at capturing the information we want. Moving forward with sample QC we used the reassigned inferred sexes of the samples.

```{r kmeans plotting}
# Interactive scatter plot of intensities similar to above, but recolors and outlines samples based on redesignated sexes.
reSexed.plot <- ggplot(reSexed_samples_table %>%
                         dplyr::arrange(inferred.sex),
                       mapping = aes(x = mean.x.chr.int, 
                                     y = mean.y.int, 
                     fill = inferred.sex,
                     colour = predicted.sex,
                     text = sample_id,
                     label = resexed)) + 
  geom_point(shape = 21,size = 3, alpha = 0.7) + 
  scale_colour_manual(values =  predicted.sex.palette) +
  scale_fill_manual(values = predicted.sex.palette) +
  QCtheme

ggplotly(reSexed.plot, 
         tooltip = c("text","label"))
```

Visualizing the corrected sex assignments, we can see two samples that stand out of the clusters: FAM and JAXMP_cookii_TH_BC19281. Because the X and Y probe intensities were supplied to k-means clustering independently in this datasest and the values for each channel were intermediate in this sample, FAM was predicted as female using the y channel and male using the x channel data. Because FAM is not a CC/DO founder or a sample representative of non-musculus species, we assigned this sample to the  "unknown" sex category. JAXMP_cookii_TH_BC19281 clustered with both sexes in a similar fashion, but because of genetic divergence between its genome and the mouse reference genome from which probes were designed, the irregular probe intensity data is not unexpected. For this reason, and the Y channel data being much more consistent with female sample, we categorized this sample as female.

### Validating reference sample genetic backgrounds

A key component of sample QC for our purposes is knowing that markers that we expect to deliver the consensus genotype (*i.e.* in a cross) actually provide us the correct strain information and allow us to correctly infer haplotypes.

```{r founder sample breakdown}
# Count up samples for each founder and resulting cross and display a table
# Dam names = row names; Sire name = column names
founder_sample_table <- founderSamples %>%
  dplyr::group_by(dam,sire) %>%
  dplyr::count() %>%
  tidyr::pivot_wider(names_from = sire, values_from = n)
DT::datatable(founder_sample_table, escape = FALSE, 
              options = list(columnDefs = list(list(width = '20%', targets = c(8)))))
```

From the table, we can see that all possible pairwise combinations of CC/DO founder strains are represented, with the exception of (NZO/HILtJxCAST/EiJ)F1 and (NZO/HILtJxPWK/PhJ)F1. These missing samples could be interesting; these two crosses have been previously noted as "reproductively incompatible" in the [literature](https://link.springer.com/article/10.1007/s00335-008-9135-8). We constructed a rough dendrogram from good marker genotypes to determine whether samples cluster according to known relationships among founder strains. Edge colors represent rough clustering into six groups - three of which contain samples derived from wild-derived founder strains and their F1 hybrids with other founder strains.


```{r founder sample dendrogram, fig.width=10, fig.height=10}
recoded_wide_geno_files <- list.files("data/GigaMUGA/GigaMUGA_founder_sample_dendrogram_genos//", pattern = "gm_widegenos_chr")
recoded_wide_sample_genos_list <- list()
for(i in 1:length(recoded_wide_geno_files)){
  recoded_wide_sample_genos_list[[i]] <- fst::read.fst(path = paste0("data/GigaMUGA/GigaMUGA_founder_sample_dendrogram_genos/",recoded_wide_geno_files[[i]]))
}
recoded_wide_sample_genos <- Reduce(dplyr::bind_cols,recoded_wide_sample_genos_list)
sample_id_cols <- recoded_wide_sample_genos %>%
  dplyr::select(contains("sample_id"))
recoded_wide_sample_genos_nosamps <- recoded_wide_sample_genos %>%
  dplyr::select(-contains("sample_id"))
rownames(recoded_wide_sample_genos_nosamps) <- sample_id_cols[,1]

# Scaling the genotype matrix, then calculating euclidean distance between all samples
dd <- dist(scale(recoded_wide_sample_genos_nosamps), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")

# Plotting sample distances as a dendrogram
dend_colors = c("slateblue", # classical strains
                "blue", 
                qtl2::CCcolors[6:8]) # official colors for wild-derived CC/DO founder strains
clus8 = cutree(hc, 5)
plot(as.phylo(hc), type = "f", cex = 0.5, tip.color = dend_colors[clus8],
     no.margin = T, label.offset = 1, edge.width = 0.5)
```

From here we curated a set of genotypes for each CC/DO founder strain that were fixed across replicate samples from that strain. The genotypes for intersecting markers between two strains were combined ("crossed") to form predicted genotypes for each F1 hybrid of CC/DO founders. Then, the genotypes of each CC/DO founder F1 hybrid were compared directly to what was predicted, and the concordance shown below is the proportion of markers of each individual that match this prediction. In order to generate a consensus call for each CC/DO founder, we had to determine whether individual samples for a given founder were correctly typed and, if the marker failed, eliminate it so that consensus could be formed. Below is an example of what that might look like for an individual marker. Note how when at least one sample for a given strain is not assigned one of the two expected alleles, the consensus is an "NA".

```{r fig.width=10, fig.height=5}
chr_concordance_files <- list.files("data/GigaMUGA/GigaMUGA_founder_sample_concordance/")
chr_concordance_list <- list()
for(i in 1:length(chr_concordance_files)){
  chr_concordance_list[[i]] <- fst::read.fst(path = paste0("data/GigaMUGA/GigaMUGA_founder_sample_concordance/",chr_concordance_files[[i]]))
}
founder_concordance_df <- Reduce(dplyr::full_join, chr_concordance_list)

founder_concordance_df_3 <- founder_concordance_df %>%
  dplyr::group_by(sample, alt_chr, dam, sire, inferred.sex) %>%
  dplyr::summarise(total.MATCH = sum(MATCH),
                   total.NO_MATCH = sum(`NO MATCH`),
                   concordance = total.MATCH/(total.MATCH+total.NO_MATCH))
  
auto_concordance <- founder_concordance_df_3 %>% 
  dplyr::filter(alt_chr == "Autosome")
X_concordance <- founder_concordance_df_3 %>% 
  dplyr::filter(alt_chr == "X")
founder_palette <- qtl2::CCcolors
names(founder_palette) <- unique(founderSamples$dam)

auto_concordance_plot <- ggplot(auto_concordance, mapping = aes(x = dam, 
                                                          y = concordance, 
                                                          color = dam, 
                                                          fill = sire,
                                                          label = sample,
                                                          text = concordance)) + 
  geom_jitter(shape = 21, width = 0.25) +
  scale_fill_manual(values = founder_palette) +
  scale_colour_manual(values = founder_palette) +
  # ylim(c(0.5,1.05)) + 
  labs(x = "Dam Founder Strain",
       y = "Autosomal Genotype Concordance") +   
  facet_grid(.~inferred.sex) +
  QCtheme + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
ggplotly(auto_concordance_plot, tooltip = c("label", "text"))

X_concordance_plot <- ggplot(X_concordance, mapping = aes(x = dam, 
                                                          y = concordance, 
                                                          color = dam, 
                                                          fill = sire,
                                                          label = sample,
                                                          text = concordance)) + 
  geom_jitter(shape = 21, width = 0.25) +
  scale_fill_manual(values = founder_palette) +
  scale_colour_manual(values = founder_palette) +
  # ylim(c(0.5,1.05)) + 
  labs(x = "Dam Founder Strain",
       y = "X Chromosome Genotype Concordance") +
  facet_grid(.~inferred.sex) +
  QCtheme + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) 
  
ggplotly(X_concordance_plot, tooltip = c("label","text"))
```



## Writing reference files

The list of output file types following QC is as follows:

-   Genotype file; rows = markers, columns = samples

-   Probe intensity file; rows = markers, columns = samples

-   Sample metadata; columns = sample, strain, sex

Each file type is generated for:

-   All good samples

-   Eight CC/DO founder strains

```{r output files for all samples}

bad_samples <- high.n.samples %>%
  dplyr::filter(!sample_id %in% c("JAXMP_caroli_UNK_BC21325",
                                  "JAXMP_minu_KE_BC24327",
                                  "JAXMP_minu_KE_BC24339",
                                  "JAXMP_pahari_TH_BC21314"))

#############################################
## Write Reference Genotypes - All Samples ##
#############################################

founder_sample_geno_files <- c(as.list(list.files(path = "data/GigaMUGA/GigaMUGA_founder_sample_genotypes/", pattern = "gm_founder_genos_chr_")[1:19]),
                                  "M",
                                  "NA",
                                  list.files(path = "data/GigaMUGA/GigaMUGA_founder_sample_genotypes/", pattern = "gm_founder_genos_chr_")[20], 
                                  "Y")

nonfounder_sample_geno_files <- as.list(list.files(path = "data/GigaMUGA/GigaMUGA_reference_genotypes/", pattern = "gm_genos_chr_"))
future::plan(multisession, workers = 16)
make_chunks <- furrr:::make_chunks
make_chunks(n_x = length(founder_sample_geno_files), n_workers = 16)


reference_genos_list <- furrr::future_map2(.x = founder_sample_geno_files, 
                                           .y = nonfounder_sample_geno_files,
                                           .f = writeReferenceGenotypes, 
                                      .options = furrr_options(seed = TRUE)) %>%
  purrr::discard(., is.character)

# All reference genotypes
reference_genos_df <- Reduce(dplyr::bind_rows, reference_genos_list)


###############################################
## Write Reference Intensities - All Samples ##
###############################################
reference_intensities_list <- furrr::future_map(.x = nonfounder_sample_geno_files, 
                                                .f = writeReferenceIntensities, 
                                                .options = furrr_options(seed = TRUE))
reference_X_intensities_df <- purrr::transpose(reference_intensities_list)[[1]] %>%
  purrr::keep(., is.data.frame) %>%
  Reduce(dplyr::bind_rows, .)
reference_Y_intensities_df <- purrr::transpose(reference_intensities_list)[[2]] %>%
  purrr::keep(., is.data.frame) %>%
  Reduce(dplyr::bind_rows, .)



#################################################
## Write Consensus Genotypes - Founder Strains ##
#################################################
founder_consensus_files <- list.files(path = "data/GigaMUGA/GigaMUGA_founder_consensus_genotypes/", pattern = "gm_founder_consensus_chr_")
founder_consensus_genos <- furrr::future_map(.x = founder_consensus_files, 
                                             .f = writeFounderConsensusGenotypes, 
                                             .options = furrr_options(seed = TRUE)) %>%
  Reduce(dplyr::bind_rows, .)


####################################
## Write Founder Mean Intensities ##
####################################

founder_samples_strains <- founderSamples %>%
  dplyr::filter(bg == "INBRED") %>%
  dplyr::distinct(sample_id, dam) %>%
  dplyr::rename(strain = dam) # if inbred, dam == strain

founder_mean_x_ints <- reference_X_intensities_df %>%
  dplyr::select(marker,founder_samples_strains$sample_id) %>%
  tidyr::pivot_longer(-marker, names_to = "sample_id", values_to = "X") %>%
  dplyr::left_join(., founder_samples_strains) %>% 
  dplyr::filter(!is.na(X)) %>%
  dplyr::group_by(marker, strain) %>%
  dplyr::summarise(mean_x = mean(X)) %>%
  tidyr::pivot_wider(names_from = strain, values_from = mean_x) %>%
  dplyr::filter(marker %in% founder_consensus_genos$marker)

founder_mean_y_ints <- reference_Y_intensities_df %>%
  dplyr::select(marker,founder_samples_strains$sample_id) %>%
  tidyr::pivot_longer(-marker, names_to = "sample_id", values_to = "Y") %>%
  dplyr::left_join(., founder_samples_strains) %>% 
  dplyr::filter(!is.na(Y)) %>%
  dplyr::group_by(marker, strain) %>%
  dplyr::summarise(mean_y = mean(Y)) %>%
  tidyr::pivot_wider(names_from = strain, values_from = mean_y) %>%
  dplyr::filter(marker %in% founder_consensus_genos$marker)


###################################
## Write Founder Sample Metadata ##
###################################

founder_metadata <- founderSamples %>%
  dplyr::select(-bg) %>%
  dplyr::rename(original_sex = predicted.sex, 
                correct_sex = inferred.sex,
                sample = sample_id) %>%
  dplyr::mutate(strain = dplyr::if_else(dam != sire, true = paste0("(",dam,"x",sire,")F1"), false = dam)) %>%
  dplyr::mutate(dam = case_when(dam == "A/J" ~ "A",
                                dam == "C57BL/6J" ~ "B",
                                dam == "129S1/SvImJ" ~ "C",
                                dam == "NOD/ShiLtJ" ~ "D",
                                dam == "NZO/HILtJ" ~ "E",
                                dam == "CAST/EiJ" ~ "F",
                                dam == "PWK/PhJ" ~ "G",
                                dam == "WSB/EiJ" ~ "H"),
                sire = case_when(sire == "A/J" ~ "A",
                                 sire == "C57BL/6J" ~ "B",
                                 sire == "129S1/SvImJ" ~ "C",
                                 sire == "NOD/ShiLtJ" ~ "D",
                                 sire == "NZO/HILtJ" ~ "E",
                                 sire == "CAST/EiJ" ~ "F",
                                 sire == "PWK/PhJ" ~ "G",
                                 sire == "WSB/EiJ" ~ "H")) %>%
  tidyr::unite("letter", dam:sire, sep = "") %>%
  dplyr::ungroup() %>%
  dplyr::select(sample, strain, correct_sex, original_sex, letter)

reference_meta_nostrain <- predicted.sexes %>%
  dplyr::filter(sample_id %in% colnames(reference_genos_df)[-1]) %>%
  dplyr::left_join(., reSexed_samples %>%
                    dplyr::select(sample_id, inferred.sex)) %>%
  dplyr::rename(sample = sample_id,
                original_sex = predicted.sex,
                correct_sex = inferred.sex) %>%
  dplyr::left_join(., founder_metadata %>%
                     dplyr::select(sample, strain))

unassigned_strains <- reference_meta_nostrain %>%
  dplyr::filter(is.na(strain))
new_strains <- purrr::map(unassigned_strains$sample, findStrain) %>%
  unlist()
unassigned_strains$strain <- new_strains

reference_sample_metadata <- unassigned_strains %>%
  dplyr::mutate(correct_sex = dplyr::if_else(is.na(correct_sex), true = "unknown", false = correct_sex),
                mouse.id.1 = stringr::str_sub(strain, -1),
                strain = dplyr::if_else(mouse.id.1 %in% c("m","f"), 
                                        true = str_sub(string = strain, 1, nchar(strain)-1), 
                                        false = strain)) %>%
  dplyr::select(-mouse.id.1) %>%
  dplyr::mutate(strain = dplyr::case_when(strain == "JAXMP_do" ~ "JAXMP_dom",
                                        strain == "JAXMP_" ~ "JAXMP_minu",
                                        strain == "PANCEVO/EiJ31581" ~ "PANCEVO/EiJ",
                                        str_detect(string = strain, pattern = "MWN") ~ "MWN",
                                        str_detect(string = strain, pattern = "PAE") ~ "PAE",
                                        str_detect(string = strain, pattern = "JAXMP_pahari") ~ "JAXMP_pahari",
                                        str_detect(string = strain, pattern = "JAXMP_spt") ~ "JAXMP_spt",
                                        str_detect(string = strain, pattern = "JAXMP_cooki") ~ "JAXMP_cooki",
                                        str_detect(string = strain, pattern = "JAXMP_cptak") ~ "JAXMP_cptak",
                                        str_detect(string = strain, pattern = "JAXMP_caroli") ~ "JAXMP_caroli",
                                        str_detect(string = strain, pattern = "JAXMP_hort") ~ "JAXMP_hort",
                                        TRUE ~ strain)) %>%
  dplyr::bind_rows(.,reference_meta_nostrain %>%
                     dplyr::filter(!sample %in% unassigned_strains$sample)) %>%
  dplyr::filter(sample %in% colnames(reference_genos_df)[-1]) %>%
  dplyr::mutate(correct_sex = dplyr::case_when(sample == "FAM" ~ "unknown",
                                               sample == "JAXMP_cookii_TH_BC19281" ~ "female",
                                               TRUE ~ correct_sex)) %>%
  dplyr::distinct()


###################
## WRITING FILES ##
###################

if(file.exists("output/GigaMUGA/GigaMUGA_genotypes.csv.gz") == "FALSE"){
  dir.create("output/GigaMUGA")
  write.csv(reference_genos_df, file = "output/GigaMUGA/GigaMUGA_genotypes.csv")
  system("gzip output/GigaMUGA/GigaMUGA_genotypes.csv")
}
if(file.exists("output/GigaMUGA/GigaMUGA_x_intensities.csv.gz") == "FALSE"){
  write.csv(reference_X_intensities_df, file = "output/GigaMUGA/GigaMUGA_x_intensities.csv")
  system("gzip output/GigaMUGA/GigaMUGA_x_intensities.csv")  
}
if(file.exists("output/GigaMUGA/GigaMUGA_y_intensities.csv.gz") == "FALSE"){
  write.csv(reference_Y_intensities_df, file = "output/GigaMUGA/GigaMUGA_y_intensities.csv")
  system("gzip output/GigaMUGA/GigaMUGA_y_intensities.csv")
}
if(file.exists("output/GigaMUGA/GigaMUGA_sample_metadata.csv") == "FALSE"){
  write.csv(reference_sample_metadata, file = "output/GigaMUGA/GigaMUGA_sample_metadata.csv")
}

if(file.exists("output/GigaMUGA/GigaMUGA_founder_consensus_genotypes.csv.gz") == "FALSE"){
  write.csv(founder_consensus_genos, 
            file = "output/GigaMUGA/GigaMUGA_founder_consensus_genotypes.csv")
  system("gzip output/GigaMUGA/GigaMUGA_founder_consensus_genotypes.csv")
}
if(file.exists("output/GigaMUGA/GigaMUGA_founder_mean_x_intensities.csv.gz") == "FALSE"){
  write.csv(founder_mean_x_ints, file = "output/GigaMUGA/GigaMUGA_founder_mean_x_intensities.csv")
  system("gzip output/GigaMUGA/GigaMUGA_founder_mean_x_intensities.csv")
}
if(file.exists("output/GigaMUGA/GigaMUGA_founder_mean_y_intensities.csv.gz") == "FALSE"){
  write.csv(founder_mean_y_ints, file = "output/GigaMUGA/GigaMUGA_founder_mean_y_intensities.csv")
  system("gzip output/GigaMUGA/GigaMUGA_founder_mean_y_intensities.csv")
}
if(file.exists("output/GigaMUGA/GigaMUGA_founder_metadata.csv") == "FALSE"){
  write.csv(founder_metadata, file = "output/GigaMUGA/GigaMUGA_founder_metadata.csv")
}

```