read_stats.rmd

---
title: "Reads Identity Statistics"
author:
  - name: Lan Huong Nguyen
    affiliation: Institute for Computational and Mathematical Engineering, Stanford University, CA 94305
keywords: microbiome, gPCA, PCA, clustering, differential abundance.
date: "`r BiocStyle::doc_date()`"
output: 
  html_notebook:
    toc_float: true
    df_print: paged
vignette: >
  %\VignetteIndexEntry{diet microbiome}
  %\VignetteEngine{knitr::rmarkdown}
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(
  echo = TRUE, warning = FALSE, message = FALSE, 
  fig.align = 'center', fig.wide = TRUE, 
  fig.width = 8, fig.height = 6
)

```

# Set up

```{r}
.packages <- c("gridExtra","RColorBrewer", 
               "tidyverse", "dplyr", "phyloseq")
sapply(.packages, require, character.only = TRUE)

options(stringsAsFactors = FALSE)
```


# Load data

Raw reads statistics:

```{r}
main.dir <- "~/"
path.to.dada2 <- file.path(main.dir, "/dada2PipelineRes/")
readsfile <- file.path(path.to.dada2, "track/track_nochim_reads.csv")
track.reads <- read.csv(readsfile, row.names = 1)
tbl_df(track.reads)
```

```{r}
sample.name <- gsub("(.*)\\_", "", track.reads$sample.id)
particip.samples <- !is.na(as.numeric(sample.name))
particip.reads <- colSums(track.reads[particip.samples, 4:ncol(track.reads)])/10^6
particip.reads
```

```{r}
percent.kept <- particip.reads/particip.reads[1]
percent.kept
```


```{r}
(ps0 <- readRDS("../data/phyloseq/diet_participants.rds"))
```


```{r}
(pstips <- readRDS("../data/phyloseq/diet_participants_tipglom.rds"))
```


```{r}
ps <- subset_samples(ps0, sample_sums(ps0) >= 10000)
ps <- subset_samples(ps, !is.na(Cat.12Mo))
ps <- subset_samples(ps, !(SubjectID == "1139" & Timepoint == "10 Week"))
ps2 <- subset_samples(ps, Cohort == 2)
ps3 <- subset_samples(ps, Cohort == 3)

ps2
ps3
```

```{r}
table(sample_data(ps2)$Timepoint)
table(sample_data(ps3)$Timepoint)

```


```{r}
summary(sample_sums(ps2))
sd(sample_sums(ps2))

summary(sample_sums(ps3))
sd(sample_sums(ps3))
```



## Collapsing repeated sequencing samples

These statistics change after collapsing the replicated samples:

Biological samples which were sequenced twice:

```{r}
(sample_data(ps) %>% 
   group_by(Cohort, SampleID) %>% 
   dplyr::summarise(
     count = n(), 
     meas = paste0(MeasID, collapse = "; ")
     ) %>%
   filter(count > 1))
```

```{r}
new_smp_data <- sample_data(ps) %>%
  data.frame() %>%
  select(-Lane, -MeasID, -OrigSampleDepth) %>%
  distinct() 
rownames(new_smp_data) <- new_smp_data$SampleID

dim(new_smp_data)
```

Combine repeated samples by computing the mean 

```{r}
collapse_samples <- function(physeq, sample_id = "SampleID", fun = mean){
  sampledata_collapsed <- sample_data(physeq) %>%
    select(-Lane, -MeasID, -OrigSampleDepth) %>%
    distinct() 
  rownames(sampledata_collapsed) <- sampledata_collapsed[[sample_id]]
  seqtab <- data.frame(as(otu_table(physeq), "matrix"))
  if(!taxa_are_rows(physeq)) seqtab <- t(seqtab)
  
  sample_ids <- physeq@sam_data[[sample_id]]
  seqtab_collapsed <- sapply(unique(sample_ids), function(id){ 
    if(sum(sample_ids == id) == 1) return(seqtab[, sample_ids == id])
    apply(seqtab[, sample_ids == id], 1, fun)
  })
  rownames(seqtab_collapsed) <- rownames(seqtab)
  physeq_collapsed <- phyloseq(
    otu_table(seqtab_collapsed, taxa_are_rows = TRUE),
    sample_data(sampledata_collapsed), 
    tax_table(physeq), phy_tree(physeq))
  return(physeq_collapsed)
}

ps.c <- collapse_samples(ps)
```

```{r}
ps2.c <- subset_samples(ps.c, Cohort == 2)
ps3.c <- subset_samples(ps.c, Cohort == 3)

ps2.c
ps3.c
```

```{r}
summary(sample_sums(ps2.c))
sd(sample_sums(ps2.c))

summary(sample_sums(ps3.c))
sd(sample_sums(ps3.c))
```

```{r}
table(sample_data(ps2.c)$Timepoint)
table(sample_data(ps3.c)$Timepoint)

```


# Seq clusters bp-Identity


```{r}
taxtable <- data.frame(ps@tax_table)  
taxtabTips <- data.frame(pstips@tax_table)
```

```{r}
head(taxtabTips)
```

```{r}
table(sapply(taxtable$Seq, nchar))
```

```{r}
library(stringdist)

read_agreement <- function(taxtable, taxtabTips){
  taxtable <- data.frame(taxtable)  
  taxtabTips <- data.frame(taxtabTips)
  
  tip_seqs_names <- strsplit(taxtabTips$SeqIncluded , "/")
  
  tip_seqs_bp <- lapply(tip_seqs_names, function(seqs) 
    taxtable[seqs, "Seq"])
  
  tip_seq_count <- sapply(tip_seqs_bp, length)
  names(tip_seqs_bp) <- rownames(taxtabTips)
  
  tip_seqs_bp <- tip_seqs_bp[tip_seq_count > 1]
  
  tip_dist_lst <- lapply(tip_seqs_bp, function(i_tip) {
    i_dist_df <- expand.grid(
      seq1_idx = seq_along(i_tip), 
      seq2_idx = seq_along(i_tip)
      ) %>%
      filter(seq1_idx < seq2_idx) 
  
    i_dist_df$bp_diff <- mapply(
      stringdist::stringdist,
      i_tip[i_dist_df$seq1_idx],
      i_tip[i_dist_df$seq2_idx],
      MoreArgs = list(method = "lv") # "lcs"
    )
    i_dist_df$percent_agreement <- (233 -i_dist_df$bp_diff)/233
    return(i_dist_df)
  })
}


```

```{r}
tips_seq_agreement_h1 <- read_agreement(ps@tax_table, pstips@tax_table)
median_tip_dist_h1 <- sapply(tips_seq_agreement_h1, function(x) 
  median(x$bp_diff))
summary(median_tip_dist_h1)
```

```{r}
median_tip_agreement_h1 <- sapply(tips_seq_agreement_h1, function(x) 
  median(x$percent_agreement))
summary(median_tip_agreement_h1)
```


```{r}
ps.list <- readRDS("../data/phyloseq/listOfTipGlom_h0.05-h.9.rds")
ps.h.05 <- ps.list[[1]]
ps.h.1 <- ps.list[[2]]
ps.h.25 <- ps.list[[3]]
ps.h.5 <- ps.list[[4]]
ps.h.9 <- ps.list[[5]]
```

```{r}
tips_seq_agreement_h05 <- read_agreement(ps@tax_table, ps.h.05@tax_table)
```


```{r}
median_tip_dist_h05 <- sapply(tips_seq_agreement_h05, function(x) 
  median(x$bp_diff))
summary(median_tip_dist_h05)
```

```{r}
median_tip_agreement_h05 <- sapply(tips_seq_agreement_h05, function(x) 
  median(x$percent_agreement))
summary(median_tip_agreement_h05)
```


```{r}
tips_seq_agreement_h25 <- read_agreement(ps@tax_table, ps.h.25@tax_table)

median_tip_dist_h25 <- sapply(tips_seq_agreement_h25, function(x) 
  median(x$bp_diff))
summary(median_tip_dist_h25)
```

```{r}
median_tip_agreement_h25 <- sapply(tips_seq_agreement_h25, function(x) 
  median(x$percent_agreement))
summary(median_tip_agreement_h25)
```


```{r}
tips_seq_agreement_h5 <- read_agreement(ps@tax_table, ps.h.5@tax_table)
median_tip_dist_h5 <- sapply(tips_seq_agreement_h5, function(x) 
  median(x$bp_diff))
```

```{r}
summary(median_tip_dist_h5)
   # Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   # 1.00   17.00   27.00   24.45   32.25   45.00 
```


```{r}
median_tip_agreement_h5 <- sapply(tips_seq_agreement_h5, function(x) 
  median(x$percent_agreement))
summary(median_tip_agreement_h5)
 #   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 # 0.8069  0.8616  0.8841  0.8951  0.9270  0.9957 
```



```{r}

```