Prepare analysis workflow

Set parameters

knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file(),
                     fig.width=15,
                     digit=5,
                     scipen=8)
options(digits=5, 
        scipen=8,
        future.globals.maxSize = +Inf)

Set filepaths and parameters

dataset_name <- commandArgs(trailingOnly=T)[1]
#dataset_name <-"hiseq2500"
message(sprintf("Dataset name: %s", dataset_name))
Dataset name: hiseq4000
project_dir <- rprojroot::find_rstudio_root_file()

if(is.null(project_dir)){
  project_dir <- getwd()
  warning(sprintf("No rstudio project root file  found. 
                  Setting project directory to current workflow.Rmd file location: %s. 
                  Override if needed.",
                  project_dir))
 
}
message(sprintf("Project directory: %s",
                project_dir))
Project directory: /project/6007998/rfarouni/index_hopping

Each sample’s molecule_info.h5 file should be renamed to {sample_name}.h5 and placed in ../project_dir/data/{dataset_name}/input/. The purged UMI count matrices and other output files are saved to ../project_dir/data/{dataset_name}/output/.

code_dir <- file.path(project_dir, "code")
data_dir <- file.path(project_dir, "data", 
                      dataset_name)
input_dir <- file.path(data_dir, "input")
output_dir <- file.path(data_dir, "output")
figures_dir <- file.path(output_dir, "figures")
read_counts_filepath <- file.path(output_dir,
                                  sprintf("%s_read_counts.rds", 
                                          dataset_name))
results_filepath <- file.path(output_dir, 
                              sprintf("%s_results.rds", 
                                      dataset_name))

Create directories if they don’t exist.

dir.create(output_dir)
Warning in dir.create(output_dir): '/project/6007998/rfarouni/
index_hopping/data/hiseq4000/output' already exists
dir.create(figures_dir)
Warning in dir.create(figures_dir): '/project/6007998/rfarouni/
index_hopping/data/hiseq4000/output/figures' already exists

Set the trade-off ratio cost cutoff (torc). The parameter torc represents the number of real molecules one is willing to incorrectly discard in order to correctly purge one phantom molecule. Since discarding a large proportion of the data is undesirable, reasonable values of torc are expected to be within the range of 1-5.

torc <- 3

Load libraries

library(rhdf5)
#library(DropletUtils) # install but not load
library(tidyverse)
library(matrixStats)
library(broom)
library(furrr)
library(tictoc)
library(data.table)
library(cowplot)
plan(multiprocess)

Load functions

source(file.path(code_dir, "1_create_joined_counts_table.R"))
source(file.path(code_dir, "2_create_counts_by_outcome_table.R"))
source(file.path(code_dir, "3_estimate_sample_index_hopping_rate.R"))
source(file.path(code_dir, "4_compute_summary_statistics.R"))
source(file.path(code_dir, "5_reassign_hopped_reads.R"))
source(file.path(code_dir, "6_purge_phantom_molecules.R"))
source(file.path(code_dir, "7_call_cells.R"))
source(file.path(code_dir, "8_summarize_purge.R"))
source(file.path(code_dir, "9_plotting_functions.R"))

Define workflow functions

purge_phantoms <- function(input_dir,
                           output_dir,
                           read_counts_filepath = NULL,
                           torc = 3,
                           max_r = NULL) {
  tic("Running workflow I")


  tic("Step 1: loading molecule_info files and creating read counts datatable")
  read_counts <- create_joined_counts(input_dir, read_counts_filepath)
  toc()


  sample_names <-
    setdiff(
      colnames(read_counts),
      c("cell", "umi", "gene", "outcome")
    )

  S <- length(sample_names)

  tic("Step 2: creating outcome counts datatable with grouping vars")

  outcome_counts <- create_outcome_counts(read_counts, sample_names)
  toc()

  tic("Step 3: creating a chimera counts datatable and estimating hopping rate")
  fit_out <-
    estimate_hopping_rate(
      outcome_counts,
      S,
      max_r = max_r
    )
  toc()

  # compute_molecular_complexity_profile
  tic("Step 4: compute molecular complexity profile and other summary statistics")
  summary_stats <-
    compute_summary_stats(
      outcome_counts,
      fit_out$glm_estimates$phat,
      sample_names
    )
  toc()


  tic("Step 5: reassign read counts, determine cutoff, and mark retained observations")

  outcome_counts <-
    reassign_reads_and_mark_retained_observations(
      outcome_counts,
      summary_stats,
      sample_names,
      fit_out,
      torc
    )
  # get the tradoff ratio cutoff
  summary_stats <- get_threshold(outcome_counts, summary_stats)

  toc()

  tic("Step 6: Purge and save read counts datatable to disk")

  read_counts <-
    left_join(read_counts %>%
      select(outcome, cell, umi, gene, sample_names),
    outcome_counts %>%
      select(c("outcome", "retain", paste0(sample_names, "_hat"))),
    by = c("outcome")
    ) %>%
    select(-outcome)

  purge_and_save_read_counts(
    read_counts,
    dataset_name,
    sample_names,
    output_dir
  )

  toc()


  tic("Step 7: create umi counts matrices")
  umi_counts_cell_gene <-
    create_umi_counts(
      read_counts,
      sample_names
    )
  toc()

  outcome_counts <-
    outcome_counts %>%
    arrange(-qr) %>%
    select(-c(paste0(sample_names, "_hat")))

  read_counts <-
    read_counts %>%
    select(-c("retain", paste0(sample_names, "_hat")))

  summary_stats$sample_names <- sample_names

  data_list <-
    list(
      umi_counts_cell_gene = umi_counts_cell_gene,
      read_counts = read_counts,
      outcome_counts = outcome_counts,
      fit_out = fit_out,
      summary_stats = summary_stats
    )

  toc()

  return(data_list)
}

identify_rna_cells <- function(data_list, output_dir) {
  tic("Running workflow II")

  tic("Step 7: identify RNA-containing cells")
  called_cells_out <- call_cells_all_samples(data_list$umi_counts_cell_gene, output_dir)
  toc()

  tic("Step 9: tallying molecules by cell-barcode")

  umi_counts_cell <- map2(
    called_cells_out$called_cells,
    data_list$umi_counts_cell_gene,
    get_umi_counts_cell
  )


  umi_counts_sample <-
    map(umi_counts_cell,
      map_dfr,
      get_umi_counts_sample,
      .id = "split"
    ) %>%
    bind_rows(.id = "sample")


  data_list$summary_stats <-
    update_summary_stats(
      data_list$summary_stats,
      umi_counts_sample
    )
  toc()

  data_list <-
    c(
      data_list,
      list(
        umi_counts_cell = umi_counts_cell,
        called_cells_tally = called_cells_out$called_cells_tally
      )
    )

  toc()

  return(data_list)
}

Run workflow Part I

Estimate the sample index hopping probability, infer the true sample of origin, and reassign reads.

data_list <- purge_phantoms(input_dir, output_dir, read_counts_filepath, torc=torc)
Step 1: loading molecule_info files and creating read counts datatable: 79.727 sec elapsed
Step 2: creating outcome counts datatable with grouping vars: 18.495 sec elapsed
Step 3: creating a chimera counts datatable and estimating hopping rate: 0.882 sec elapsed
Step 4: compute molecular complexity profile and other summary statistics: 2.702 sec elapsed
Step 5: reassign read counts, determine cutoff, and mark retained observations: 5.818 sec elapsed
Step 6: Purge and save read counts datatable to disk: 127.33 sec elapsed
Step 7: create umi counts matrices: 57.812 sec elapsed
Running workflow I: 292.864 sec elapsed

1. Show data and summary statistics

Read counts datatable

data_list$read_counts

Outcome counts datatable

The datatable is ordered in descending order of qr, the posterior probability of incorrectly assigning s as the inferred sample of origin. n is the number of CUGs with the corresponding outcome and p_outcome is the observed marginal probability of that outcome.

data_list$outcome_counts

Summary statistics of the joined read counts datatable

p_chimeras is the proportion CUGs that are chimeric. g is the estimated proportion of fugue molecules and u is the molecule inflation factor such that n_cugs x u would give the number of non-fugue phantom molecules. The estimated total number of phantom molecules present in the dataset is given by n_pm=n_cugs x (u+g).

 data_list$summary_stats$summary_estimates

Marginal summary statistics

 data_list$summary_stats$conditional

Molecular proportions complexity profile

 data_list$summary_stats$pi_r_hat

Plot

p_read <- plot_molecules_distributions(data_list, dataset_name, x_lim=250)
p_read <- plot_grid(p_read$p, 
          p_read$legend,
          ncol=2,
          rel_widths=c(1, 0.1))

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_molcomplexity.pdf")),
       p_read,
       width=9,
       height=6)
p_read

2. Estimating the sample index hopping rate (SIHR)

GLM fit estimates

data_list$fit_out$glm_estimates

Model fit plot

p_fit <- plot_fit(data_list, dataset_name, x_lim=250)
p_fit <-plot_grid(p_fit$p,
          p_fit$legend,
          ncol=2,
          rel_widths=c(1, 0.2))

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_fit.pdf")),
       p_fit,
       width=9,
       height=6)
p_fit

3. Minimizing false positives using the the tradoff ratio cutoff (torc)

data_list$summary_stats$cutoff_dt

The row discard_torc shows the outcome whose qr value is the maximum allowed. Reads corresponding to outcomes with greater qr values are discarded. no_discarding corresponds to retaining all reassigned reads and no_purging corresponds to keeping the data as it is.

Plot tradoff plots

p_fp <- plot_fp_reduction(data_list, dataset_name)

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_fp_performance.pdf")),
       p_fp,
       width=9,
       height=6)
p_fp

p_tradeoff <- plot_fp_tradoff(data_list, dataset_name)

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_tradeoff.pdf")),
       p_tradeoff,
       width=9,
       height=6)

p_tradeoff

Part II: Process data for downstream analysis

data_list <- identify_rna_cells(data_list, output_dir)
Warning in smooth.spline(x[new.keep], y[new.keep], df = df, ...): not using
invalid df; must have 1 < df <= n := #{unique x} = 11
Warning in smooth.spline(x[new.keep], y[new.keep], df = df, ...): not using
invalid df; must have 1 < df <= n := #{unique x} = 6
Step 7: identify RNA-containing cells: 877.212 sec elapsed
Step 9: tallying molecules by cell-barcode: 34.345 sec elapsed
Running workflow II: 911.558 sec elapsed

Examine the consequences of index hopping

Here we examine the extent of the effects of index hopping on individual samples and then on cell-barcodes.

Tally of predicted phantoms by sample

Here m is the number of total molecules in millions; rm_ret is the number of predicted real molecules and prm_ret is the proportion; rm_disc is the number of discarded real molecules; and pm is the number of predicted phantom molecules.

data_list$summary_stats$marginal

Tally of predicted phantoms in called cells

The called cells were determined from the unpurged data in order to show the level of contamination by phantom molecules if data were not purged.

data_list$summary_stats$marginal_called_cells

Tally of barcodes by concordance between purged and unpurged data

The rows corresponding to consensus_background and consensus_cell refer to the number of barcodes that were categorized as background cells or rna-containing cells, respectively, no matter whether the data was purged or not. In contrast, transition_background and transition_cell refer to the number of barcodes that were recatgorized as background and cell, respectively. phantom_background and phantom_cell are phantom cells that disappear once phantom molecules are purged.

data_list$called_cells_tally

Table of called cell-barcodes with the highest number of phantoms

data_list$umi_counts_cell %>% 
  map(list("called_cells"))
$A1
# A tibble: 538 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT  8325   1084  7238       3
 2 GGCTGGTGTCCCTACT  6661    888  5773       0
 3 TCGTACCGTCCATGAT  5765    804  4960       1
 4 CATGCCTAGTGCTGCC  5496    805  4691       0
 5 TGCACCTTCTGGCGAC  4967    676  4290       1
 6 CACACAAAGGCGATAC  3464    542  2922       0
 7 GTGCAGCCAAGCGAGT  3020    370  2650       0
 8 GGAATAAGTCAGGACA  2961    443  2518       0
 9 CTAGCCTTCCGCGCAA  2717    411  2306       0
10 AAGCCGCGTGCTCTTC  2501    363  2137       1
# … with 528 more rows

$A2
# A tibble: 670 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT  7834   1194  6637       3
 2 GGCTGGTGTCCCTACT  6399    970  5428       1
 3 TCGTACCGTCCATGAT  5485    882  4602       1
 4 CATGCCTAGTGCTGCC  5421    828  4593       0
 5 TGCACCTTCTGGCGAC  4687    757  3930       0
 6 CACACAAAGGCGATAC  3280    530  2749       1
 7 GTGCAGCCAAGCGAGT  2841    404  2437       0
 8 GGAATAAGTCAGGACA  2869    476  2392       1
 9 CTAGCCTTCCGCGCAA  2628    426  2202       0
10 AAGCCGCGTGCTCTTC  2533    369  2164       0
# … with 660 more rows

$B1
# A tibble: 1,023 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT 23426   1449 21976       1
 2 TCGTACCGTCCATGAT 16849    934 15913       2
 3 TGCACCTTCTGGCGAC 13898    858 13038       2
 4 GTGCAGCCAAGCGAGT  8279    440  7839       0
 5 GAAACTCAGTTCCACA  7856    259  7597       0
 6 CTAGCCTTCCGCGCAA  7648    485  7163       0
 7 AGCAGCCGTTAAGGGC  5905    377  5528       0
 8 TGGCCAGTCATGTCTT  4906    151  4755       0
 9 GCATGTATCCCAGGTG  4308    125  4183       0
10 GTGCTTCAGGCATTGG  4303    137  4166       0
# … with 1,013 more rows

$B2
# A tibble: 905 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GGCTGGTGTCCCTACT 15818   1118 14699       1
 2 CATGCCTAGTGCTGCC 12752   1006 11739       7
 3 CACACAAAGGCGATAC  8024    669  7354       1
 4 GAAACTCAGTTCCACA  7246    258  6988       0
 5 GGAATAAGTCAGGACA  6611    508  6101       2
 6 AAGCCGCGTGCTCTTC  6047    453  5592       2
 7 CATCGAAAGTCAATAG  4913    379  4530       4
 8 TGGCCAGTCATGTCTT  4521    186  4335       0
 9 GTGCTTCAGGCATTGG  4036    120  3916       0
10 GCATGTATCCCAGGTG  3977    130  3847       0
# … with 895 more rows

$C1
# A tibble: 848 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT  7983   1100  6880       3
 2 GGCTGGTGTCCCTACT  6334    852  5481       1
 3 TCGTACCGTCCATGAT  5530    718  4810       2
 4 CATGCCTAGTGCTGCC  5396    776  4619       1
 5 TGCACCTTCTGGCGAC  4715    609  4105       1
 6 CACACAAAGGCGATAC  3351    516  2835       0
 7 GTGCAGCCAAGCGAGT  2941    373  2568       0
 8 GGAATAAGTCAGGACA  2852    432  2419       1
 9 CTAGCCTTCCGCGCAA  2597    344  2252       1
10 AAGCCGCGTGCTCTTC  2485    337  2148       0
# … with 838 more rows

$C2
# A tibble: 1,472 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT  8207   1285  6916       6
 2 GGCTGGTGTCCCTACT  6761    978  5783       0
 3 TCGTACCGTCCATGAT  5509    838  4668       3
 4 CATGCCTAGTGCTGCC  5478    826  4647       5
 5 TGCACCTTCTGGCGAC  4786    742  4040       4
 6 CACACAAAGGCGATAC  3515    515  3000       0
 7 GTGCAGCCAAGCGAGT  2849    391  2458       0
 8 GGAATAAGTCAGGACA  2877    474  2403       0
 9 AAGCCGCGTGCTCTTC  2588    399  2189       0
10 CTAGCCTTCCGCGCAA  2608    441  2164       3
# … with 1,462 more rows

$D1
# A tibble: 725 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT  7496   1036  6460       0
 2 GGCTGGTGTCCCTACT  5953    786  5164       3
 3 TCGTACCGTCCATGAT  5054    699  4354       1
 4 CATGCCTAGTGCTGCC  5060    735  4325       0
 5 TGCACCTTCTGGCGAC  4437    639  3797       1
 6 CACACAAAGGCGATAC  3188    500  2688       0
 7 GTGCAGCCAAGCGAGT  2707    337  2370       0
 8 GGAATAAGTCAGGACA  2657    390  2267       0
 9 AAGCCGCGTGCTCTTC  2311    315  1995       1
10 CTAGCCTTCCGCGCAA  2344    352  1992       0
# … with 715 more rows

$D2
# A tibble: 194 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT  8986   1146  7832       8
 2 GGCTGGTGTCCCTACT  7238    911  6326       1
 3 TCGTACCGTCCATGAT  6015    759  5253       3
 4 CATGCCTAGTGCTGCC  6039    842  5194       3
 5 TGCACCTTCTGGCGAC  5328    728  4595       5
 6 CACACAAAGGCGATAC  3797    545  3250       2
 7 GTGCAGCCAAGCGAGT  3315    392  2921       2
 8 GGAATAAGTCAGGACA  3158    467  2690       1
 9 CTAGCCTTCCGCGCAA  2964    395  2564       5
10 AAGCCGCGTGCTCTTC  2936    378  2555       3
# … with 184 more rows

Table of background cell-barcodes with highest number of phantoms

data_list$umi_counts_cell %>% 
  map(list("background_cells"))
$A1
# A tibble: 150,609 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GAGGTGATCCAAGCCG   544     78   466       0
 2 TGCGCAGCACGTGAGA   298     38   260       0
 3 CTACACCTCGCCTGAG   307     48   259       0
 4 TGTTCCGAGTACGTAA   293     44   249       0
 5 CACTCCAAGCTAAACA   285     40   245       0
 6 AAAGCAAGTGAACCTT   256     28   228       0
 7 CCATGTCCACGAGGTA   258     31   227       0
 8 CAGATCAGTGCTCTTC   259     33   226       0
 9 AAACGGGAGTGGGCTA   267     44   223       0
10 TGTGGTATCTCCGGTT   249     31   218       0
# … with 150,599 more rows

$A2
# A tibble: 168,114 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GAGGTGATCCAAGCCG   506     67   439       0
 2 TGAAAGACATCCGTGG   372     43   324       5
 3 ACACCAACAATGCCAT   379     64   309       6
 4 ACGGCCACAAGGACAC   343     50   290       3
 5 CCAGCGATCTGTTTGT   333     44   285       4
 6 AACTCAGTCCGCTGTT   350     64   283       3
 7 ATAGACCAGTACGTAA   325     48   273       4
 8 GCACATAAGTACGTAA   325     54   270       1
 9 TTTACTGAGTAGGTGC   298     36   258       4
10 GAGTCCGAGCGATATA   305     46   254       5
# … with 168,104 more rows

$B1
# A tibble: 110,209 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GTGCTTCGTCCGTCAG  2099     80  2019       0
 2 CGAGCACAGTGAAGTT  1941     82  1859       0
 3 TACACGACAGGCGATA  1918     87  1831       0
 4 TCTGAGACACACCGAC  1813     56  1757       0
 5 GCTCCTATCCTAGAAC  1644     60  1584       0
 6 TTTATGCAGTTAAGTG  1571     55  1516       0
 7 ACATGGTGTCGTTGTA  1499     55  1444       0
 8 ACTGTCCCACCGAAAG  1375     57  1318       0
 9 CGTTCTGTCCCACTTG  1368     53  1315       0
10 TGACTTTGTAGAGCTG  1370     63  1307       0
# … with 110,199 more rows

$B2
# A tibble: 110,504 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 TACACGACAGGCGATA  1785     55  1728       2
 2 CGAGCACAGTGAAGTT  1820     98  1722       0
 3 GAGGTGATCCAAGCCG  1719     79  1640       0
 4 TCTGAGACACACCGAC  1683     70  1612       1
 5 ACTGAACTCCCGACTT  1621     55  1565       1
 6 TTTATGCAGTTAAGTG  1605     58  1546       1
 7 GCTCCTATCCTAGAAC  1536     57  1479       0
 8 GGAACTTCATTTGCTT  1544     66  1478       0
 9 ACATGGTGTCGTTGTA  1519     59  1460       0
10 GTTCATTTCTTGAGAC  1435     55  1380       0
# … with 110,494 more rows

$C1
# A tibble: 180,232 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GAGGTGATCCAAGCCG   581     81   500       0
 2 GGAATAATCTGGGCCA   394     55   339       0
 3 TATCTCAGTCGCGAAA   344     41   303       0
 4 TGAAAGACATCCGTGG   344     38   302       4
 5 AACTCAGTCCGCTGTT   342     40   298       4
 6 CCAGCGATCTGTTTGT   377     81   295       1
 7 ACGGCCACAAGGACAC   346     51   292       3
 8 GTGCTTCGTCCGTCAG   334     40   292       2
 9 TCAATCTTCCAGAGGA   334     49   285       0
10 GCACATAAGTACGTAA   320     42   276       2
# … with 180,222 more rows

$C2
# A tibble: 242,496 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GAGGTGATCCAAGCCG   597    100   497       0
 2 AACCATGTCAGCGACC   412     74   338       0
 3 CTGGTCTTCGGTTCGG   354     29   325       0
 4 TGAAAGACATCCGTGG   376     58   313       5
 5 CCACCTAGTCGATTGT   351     44   307       0
 6 ACGGCCACAAGGACAC   439    130   304       5
 7 ATCACGACATGCTAGT   348     53   295       0
 8 TTCGAAGCAAGGTGTG   337     44   293       0
 9 CAGGTGCTCTGAAAGA   330     43   287       0
10 TATCTCAGTCGCGAAA   328     43   285       0
# … with 242,486 more rows

$D1
# A tibble: 131,097 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GAGGTGATCCAAGCCG   513     88   424       1
 2 GATCGTAAGATACACA   401     48   353       0
 3 TTTCCTCCATCACGAT   375     42   328       5
 4 ACTGATGTCAATCACG   419     98   321       0
 5 GGAGCAATCCGCATCT   358     49   308       1
 6 CAGCAGCCAGAGCCAA   347     46   300       1
 7 TTCGAAGCAACAACCT   348     49   299       0
 8 ACGGCCACAAGGACAC   327     38   285       4
 9 GCACATAAGTACGTAA   330     48   278       4
10 ATAGACCAGTACGTAA   321     43   277       1
# … with 131,087 more rows

$D2
# A tibble: 98,206 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GAGGTGATCCAAGCCG   577     74   502       1
 2 AGAATAGTCATTCACT   525     75   450       0
 3 ACTGATGTCAATCACG   554    127   427       0
 4 CTACACCCACGCGAAA   464     69   394       1
 5 CAGCAGCCAGAGCCAA   446     53   393       0
 6 ACACCAACAATGCCAT   440     52   388       0
 7 ACGGCCACAAGGACAC   420     46   373       1
 8 ATAGACCAGTACGTAA   410     42   368       0
 9 CGTAGGCCAACACCTA   415     49   366       0
10 AACTCAGTCCGCTGTT   415     49   365       1
# … with 98,196 more rows

Save ouput to file (optional)

tic("saving output")
data_list$read_counts <- NULL
saveRDS(data_list, results_filepath)
toc()
saving output: 332.045 sec elapsed

Session Info

# memory usage
gc()
            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   6271619  335.0   12858931   686.8   12858931   686.8
Vcells 834233111 6364.7 2727834242 20811.8 3402985819 25962.8
sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /cvmfs/soft.computecanada.ca/easybuild/software/2017/Core/imkl/2018.3.222/compilers_and_libraries_2018.3.222/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] cowplot_0.9.4      data.table_1.12.2  tictoc_1.0        
 [4] furrr_0.1.0        future_1.13.0      broom_0.5.2       
 [7] matrixStats_0.54.0 forcats_0.4.0      stringr_1.4.0     
[10] dplyr_0.8.1        purrr_0.3.2        readr_1.3.1       
[13] tidyr_0.8.3        tibble_2.1.2       ggplot2_3.1.1     
[16] tidyverse_1.2.1    rhdf5_2.26.2       rmarkdown_1.13    

loaded via a namespace (and not attached):
 [1] nlme_3.1-137                bitops_1.0-6               
 [3] lubridate_1.7.4             httr_1.4.0                 
 [5] rprojroot_1.3-2             GenomeInfoDb_1.18.2        
 [7] tools_3.5.2                 backports_1.1.4            
 [9] utf8_1.1.4                  R6_2.4.0                   
[11] HDF5Array_1.10.1            lazyeval_0.2.2             
[13] BiocGenerics_0.28.0         colorspace_1.4-1           
[15] withr_2.1.2                 tidyselect_0.2.5           
[17] compiler_3.5.2              cli_1.1.0                  
[19] rvest_0.3.4                 Biobase_2.42.0             
[21] xml2_1.2.0                  DelayedArray_0.8.0         
[23] labeling_0.3                scales_1.0.0               
[25] digest_0.6.19               XVector_0.22.0             
[27] base64enc_0.1-3             pkgconfig_2.0.2            
[29] htmltools_0.3.6             limma_3.38.3               
[31] rlang_0.3.4                 readxl_1.3.1               
[33] rstudioapi_0.10             generics_0.0.2             
[35] jsonlite_1.6                BiocParallel_1.16.6        
[37] RCurl_1.95-4.12             magrittr_1.5               
[39] GenomeInfoDbData_1.2.0      Matrix_1.2-15              
[41] Rcpp_1.0.1                  munsell_0.5.0              
[43] S4Vectors_0.20.1            Rhdf5lib_1.4.2             
[45] fansi_0.4.0                 stringi_1.4.3              
[47] yaml_2.2.0                  edgeR_3.24.3               
[49] MASS_7.3-51.1               SummarizedExperiment_1.12.0
[51] zlibbioc_1.28.0             plyr_1.8.4                 
[53] grid_3.5.2                  parallel_3.5.2             
[55] listenv_0.7.0               crayon_1.3.4               
[57] lattice_0.20-38             haven_2.1.0                
[59] hms_0.4.2                   locfit_1.5-9.1             
[61] zeallot_0.1.0               knitr_1.23                 
[63] pillar_1.4.1                GenomicRanges_1.34.0       
[65] codetools_0.2-16            stats4_3.5.2               
[67] glue_1.3.1                  evaluate_0.14              
[69] modelr_0.1.4                vctrs_0.1.0                
[71] cellranger_1.1.0            gtable_0.3.0               
[73] assertthat_0.2.1            xfun_0.7                   
[75] DropletUtils_1.2.2          viridisLite_0.3.0          
[77] SingleCellExperiment_1.4.1  IRanges_2.16.0             
[79] globals_0.12.4
---
title: "Phantom Purge"
subtitle: "Analysis workflow for `r commandArgs(trailingOnly=T)[1]` data"
author: 
- name: Rick Farouni
  affiliation:
  - &cruk Génome Québec Innovation Centre, McGill University, Montreal, Canada
date: '`r format(Sys.Date(), "%Y-%B-%d")`'
output:
  html_notebook:
    df_print: paged
    code_folding: show
    toc: no
    toc_float: 
      collapsed: false
      smooth_scroll: false
---


# Prepare analysis workflow

### Set parameters

```{r setup}
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file(),
                     fig.width=15,
                     digit=5,
                     scipen=8)
options(digits=5, 
        scipen=8,
        future.globals.maxSize = +Inf)
```


### Set filepaths and parameters

```{r}
dataset_name <- commandArgs(trailingOnly=T)[1]
#dataset_name <-"hiseq2500"
message(sprintf("Dataset name: %s", dataset_name))
```


```{r}
project_dir <- rprojroot::find_rstudio_root_file()

if(is.null(project_dir)){
  project_dir <- getwd()
  warning(sprintf("No rstudio project root file  found. 
                  Setting project directory to current workflow.Rmd file location: %s. 
                  Override if needed.",
                  project_dir))
 
}
message(sprintf("Project directory: %s",
                project_dir))
```


Each sample's *molecule_info.h5* file should be renamed to *{sample_name}.h5* and placed in *../project_dir/data/{dataset_name}/input/*. The purged UMI count matrices and other output files are saved to *../project_dir/data/{dataset_name}/output/*.
```{r}
code_dir <- file.path(project_dir, "code")
data_dir <- file.path(project_dir, "data", 
                      dataset_name)
input_dir <- file.path(data_dir, "input")
output_dir <- file.path(data_dir, "output")
figures_dir <- file.path(output_dir, "figures")
read_counts_filepath <- file.path(output_dir,
                                  sprintf("%s_read_counts.rds", 
                                          dataset_name))
results_filepath <- file.path(output_dir, 
                              sprintf("%s_results.rds", 
                                      dataset_name))
```


Create directories if they don't exist.
```{r}
dir.create(output_dir)
dir.create(figures_dir)
```


Set the trade-off ratio cost cutoff (*torc*). The parameter *torc* represents the number of real molecules one is willing to incorrectly discard in order to correctly purge one phantom molecule. Since discarding a large proportion of the data is undesirable, reasonable values of *torc* are expected to be within the range of 1-5.

```{r}
torc <- 3 
```

### Load libraries


```{r message=FALSE, warning=FALSE}
library(rhdf5)
#library(DropletUtils) # install but not load
library(tidyverse)
library(matrixStats)
library(broom)
library(furrr)
library(tictoc)
library(data.table)
library(cowplot)
plan(multiprocess)
```


### Load functions


```{r message=FALSE}
source(file.path(code_dir, "1_create_joined_counts_table.R"))
source(file.path(code_dir, "2_create_counts_by_outcome_table.R"))
source(file.path(code_dir, "3_estimate_sample_index_hopping_rate.R"))
source(file.path(code_dir, "4_compute_summary_statistics.R"))
source(file.path(code_dir, "5_reassign_hopped_reads.R"))
source(file.path(code_dir, "6_purge_phantom_molecules.R"))
source(file.path(code_dir, "7_call_cells.R"))
source(file.path(code_dir, "8_summarize_purge.R"))
source(file.path(code_dir, "9_plotting_functions.R"))
```



### Define workflow functions

```{r}
purge_phantoms <- function(input_dir,
                           output_dir,
                           read_counts_filepath = NULL,
                           torc = 3,
                           max_r = NULL) {
  tic("Running workflow I")


  tic("Step 1: loading molecule_info files and creating read counts datatable")
  read_counts <- create_joined_counts(input_dir, read_counts_filepath)
  toc()


  sample_names <-
    setdiff(
      colnames(read_counts),
      c("cell", "umi", "gene", "outcome")
    )

  S <- length(sample_names)

  tic("Step 2: creating outcome counts datatable with grouping vars")

  outcome_counts <- create_outcome_counts(read_counts, sample_names)
  toc()

  tic("Step 3: creating a chimera counts datatable and estimating hopping rate")
  fit_out <-
    estimate_hopping_rate(
      outcome_counts,
      S,
      max_r = max_r
    )
  toc()

  # compute_molecular_complexity_profile
  tic("Step 4: compute molecular complexity profile and other summary statistics")
  summary_stats <-
    compute_summary_stats(
      outcome_counts,
      fit_out$glm_estimates$phat,
      sample_names
    )
  toc()


  tic("Step 5: reassign read counts, determine cutoff, and mark retained observations")

  outcome_counts <-
    reassign_reads_and_mark_retained_observations(
      outcome_counts,
      summary_stats,
      sample_names,
      fit_out,
      torc
    )
  # get the tradoff ratio cutoff
  summary_stats <- get_threshold(outcome_counts, summary_stats)

  toc()

  tic("Step 6: Purge and save read counts datatable to disk")

  read_counts <-
    left_join(read_counts %>%
      select(outcome, cell, umi, gene, sample_names),
    outcome_counts %>%
      select(c("outcome", "retain", paste0(sample_names, "_hat"))),
    by = c("outcome")
    ) %>%
    select(-outcome)

  purge_and_save_read_counts(
    read_counts,
    dataset_name,
    sample_names,
    output_dir
  )

  toc()


  tic("Step 7: create umi counts matrices")
  umi_counts_cell_gene <-
    create_umi_counts(
      read_counts,
      sample_names
    )
  toc()

  outcome_counts <-
    outcome_counts %>%
    arrange(-qr) %>%
    select(-c(paste0(sample_names, "_hat")))

  read_counts <-
    read_counts %>%
    select(-c("retain", paste0(sample_names, "_hat")))

  summary_stats$sample_names <- sample_names

  data_list <-
    list(
      umi_counts_cell_gene = umi_counts_cell_gene,
      read_counts = read_counts,
      outcome_counts = outcome_counts,
      fit_out = fit_out,
      summary_stats = summary_stats
    )

  toc()

  return(data_list)
}

identify_rna_cells <- function(data_list, output_dir) {
  tic("Running workflow II")

  tic("Step 7: identify RNA-containing cells")
  called_cells_out <- call_cells_all_samples(data_list$umi_counts_cell_gene, output_dir)
  toc()

  tic("Step 9: tallying molecules by cell-barcode")

  umi_counts_cell <- map2(
    called_cells_out$called_cells,
    data_list$umi_counts_cell_gene,
    get_umi_counts_cell
  )


  umi_counts_sample <-
    map(umi_counts_cell,
      map_dfr,
      get_umi_counts_sample,
      .id = "split"
    ) %>%
    bind_rows(.id = "sample")


  data_list$summary_stats <-
    update_summary_stats(
      data_list$summary_stats,
      umi_counts_sample
    )
  toc()

  data_list <-
    c(
      data_list,
      list(
        umi_counts_cell = umi_counts_cell,
        called_cells_tally = called_cells_out$called_cells_tally
      )
    )

  toc()

  return(data_list)
}
```

# Run workflow Part I

Estimate the sample index hopping probability, infer the true sample of origin, and reassign reads.


```{r}
data_list <- purge_phantoms(input_dir, output_dir, read_counts_filepath, torc=torc)
```


## 1. Show data and summary statistics

### Read counts datatable

```{r}
data_list$read_counts 
```

### Outcome counts datatable

The datatable is ordered in descending order of *qr*, the posterior probability of incorrectly assigning *s* as the inferred sample of origin. *n* is the number of CUGs with the corresponding *outcome* and *p_outcome* is the observed marginal probability of that *outcome*.  

```{r }
data_list$outcome_counts 
```


### Summary statistics of the joined read counts datatable

*p_chimeras* is the proportion CUGs that are chimeric. *g* is the estimated proportion of fugue molecules and *u* is the molecule inflation factor such that *n_cugs x u* would give the number of non-fugue phantom molecules. The estimated total number of phantom molecules present in the dataset is given by  *n_pm=n_cugs x (u+g)*.

```{r}
 data_list$summary_stats$summary_estimates
```

### Marginal summary statistics



```{r}
 data_list$summary_stats$conditional
```



### Molecular proportions complexity profile 


```{r}
 data_list$summary_stats$pi_r_hat
```

Plot

```{r fig.height=9, fig.width=15, message=FALSE, warning=FALSE}
p_read <- plot_molecules_distributions(data_list, dataset_name, x_lim=250)
p_read <- plot_grid(p_read$p, 
          p_read$legend,
          ncol=2,
          rel_widths=c(1, 0.1))

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_molcomplexity.pdf")),
       p_read,
       width=9,
       height=6)
p_read
```



## 2. Estimating the sample index hopping rate (*SIHR*)


### GLM fit estimates

```{r}
data_list$fit_out$glm_estimates
```

### Model fit plot 

```{r fig.height=9, fig.width=15, message=FALSE, warning=FALSE}
p_fit <- plot_fit(data_list, dataset_name, x_lim=250)
p_fit <-plot_grid(p_fit$p,
          p_fit$legend,
          ncol=2,
          rel_widths=c(1, 0.2))

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_fit.pdf")),
       p_fit,
       width=9,
       height=6)
p_fit
```



## 3. Minimizing false positives using the the tradoff ratio cutoff (torc)

```{r}
data_list$summary_stats$cutoff_dt
```


The row  *discard_torc* shows the outcome whose *qr* value is the maximum allowed. Reads corresponding to outcomes with greater *qr* values are discarded. *no_discarding* corresponds to retaining all reassigned reads and *no_purging* corresponds to keeping the data as it is. 

### Plot tradoff plots

```{r fig.height=7, fig.width=12, message=FALSE, warning=FALSE}
p_fp <- plot_fp_reduction(data_list, dataset_name)

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_fp_performance.pdf")),
       p_fp,
       width=9,
       height=6)
p_fp
```


```{r fig.height=7, fig.width=10, message=FALSE, warning=FALSE}
p_tradeoff <- plot_fp_tradoff(data_list, dataset_name)

ggsave(file.path(figures_dir,
                 paste0(dataset_name, "_tradeoff.pdf")),
       p_tradeoff,
       width=9,
       height=6)

p_tradeoff
```



# Part II: Process data for downstream analysis


```{r}
data_list <- identify_rna_cells(data_list, output_dir)
```

##  Examine the consequences of index hopping 

Here we examine the extent of the effects of index hopping on individual samples and then on cell-barcodes.

### Tally of predicted phantoms by sample

Here *m* is the number of total molecules in millions; *rm_ret* is the number of predicted real molecules and *prm_ret* is the proportion; *rm_disc* is the number of discarded real molecules; and *pm* is the number of predicted phantom molecules. 

```{r}
data_list$summary_stats$marginal
```

### Tally of predicted phantoms in called cells 

The called cells were determined from the unpurged data in order to show the level of contamination by phantom molecules if data were not purged.

```{r}
data_list$summary_stats$marginal_called_cells
```


### Tally of barcodes by concordance between purged and unpurged data

The rows corresponding to *consensus_background* and *consensus_cell* refer to the number of barcodes that were categorized as background cells or rna-containing cells, respectively, no matter whether the data was purged or not. In contrast, *transition_background* and *transition_cell* refer to the number of barcodes that were recatgorized as background and cell, respectively. *phantom_background* and *phantom_cell* are phantom cells that disappear once phantom molecules are purged.

```{r}
data_list$called_cells_tally
```

### Table of called cell-barcodes with the highest number of phantoms

```{r}
data_list$umi_counts_cell %>% 
  map(list("called_cells"))
```

### Table of background cell-barcodes with highest number of phantoms

```{r}
data_list$umi_counts_cell %>% 
  map(list("background_cells"))
```


### Save ouput to file (optional)

```{r}
tic("saving output")
data_list$read_counts <- NULL
saveRDS(data_list, results_filepath)
toc()
```


# Session Info

```{r}
# memory usage
gc()
```

```{r}
sessionInfo()
```
