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: novaseq_l1
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/novaseq_l1/output' already exists
dir.create(figures_dir)
Warning in dir.create(figures_dir): '/project/6007998/rfarouni/
index_hopping/data/novaseq_l1/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: 498.264 sec elapsed
Step 2: creating outcome counts datatable with grouping vars: 60.014 sec elapsed
Step 3: creating a chimera counts datatable and estimating hopping rate: 0.138 sec elapsed
Step 4: compute molecular complexity profile and other summary statistics: 0.522 sec elapsed
Step 5: reassign read counts, determine cutoff, and mark retained observations: 6.362 sec elapsed
Step 6: Purge and save read counts datatable to disk: 722.83 sec elapsed
Step 7: create umi counts matrices: 765.427 sec elapsed
Running workflow I: 2053.622 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)
Step 7: identify RNA-containing cells: 1985.566 sec elapsed
Step 9: tallying molecules by cell-barcode: 318.323 sec elapsed
Running workflow II: 2303.89 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"))
$P7_0
# A tibble: 1,315 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   475     40   430       5
 2 AGTGAGGTCTGCCCTA   850    432   417       1
 3 CTAACTTAGTTGTAGA   433     34   399       0
 4 CATGCCTTCAATACCG   785    392   391       2
 5 CTCGAGGGTTTCGCTC   396     27   368       1
 6 AGACGTTGTCCGAGTC   403     36   365       2
 7 GAACATCTCCTTGGTC   369     32   336       1
 8 CGGGTCATCCCAAGTA   369     39   329       1
 9 GTCGTAAGTTAAGATG   367     42   325       0
10 AGCATACCATCATCCC   357     37   320       0
# … with 1,305 more rows

$P7_1
# A tibble: 774 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   555     51   500       4
 2 AGACGTTGTCCGAGTC   518     42   471       5
 3 CTAACTTAGTTGTAGA   492     48   443       1
 4 CGGGTCATCCCAAGTA   486     53   432       1
 5 GAATAAGCATATGGTC   464     48   416       0
 6 TGTTCCGAGGCTAGAC   477     70   407       0
 7 GTGGGTCCAAACTGTC   470     87   382       1
 8 CATGCCTTCAATACCG   434     57   377       0
 9 TAAGAGATCTTGGGTA   480    114   366       0
10 GAACATCTCAGAGACG   424     59   365       0
# … with 764 more rows

$P7_10
# A tibble: 2,460 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   293     39   254       0
 2 CTAACTTAGTTGTAGA   255     21   234       0
 3 GAACATCTCCTTGGTC   227     13   214       0
 4 AGACGTTGTCCGAGTC   234     25   209       0
 5 CGGGTCATCCCAAGTA   224     15   209       0
 6 AGTGAGGTCTGCCCTA   218     21   196       1
 7 CTCGAGGGTTTCGCTC   214     19   195       0
 8 GGACATTTCGTAGGAG   207     17   189       1
 9 GTGCTTCGTCCCTACT   205     40   163       2
10 ACATACGCAGCATGAG 12457  12321   103      33
# … with 2,450 more rows

$P7_11
# A tibble: 3,506 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGTGAGGTCTGCCCTA   394     55   338       1
 2 AGGGATGGTCTAACGT   369     30   334       5
 3 CGGGTCATCCCAAGTA   373     62   310       1
 4 CTCGAGGGTTTCGCTC   323     20   302       1
 5 AGACGTTGTCCGAGTC   304     27   274       3
 6 GAACATCTCCTTGGTC   297     22   272       3
 7 CATGCCTTCAATACCG   316     45   270       1
 8 TGTTCCGAGGCTAGAC   284     23   258       3
 9 GGACATTTCGTAGGAG   271     18   247       6
10 GAACATCTCAGAGACG   268     32   232       4
# … with 3,496 more rows

$P7_12
# A tibble: 3,055 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   368     40   322       6
 2 CTAACTTAGTTGTAGA   339     26   313       0
 3 AGTGAGGTCTGCCCTA   331     29   297       5
 4 AGACGTTGTCCGAGTC   303     37   261       5
 5 TGTTCCGAGGCTAGAC   273     21   251       1
 6 GAACATCTCAGAGACG   315     60   250       5
 7 CTCGAGGGTTTCGCTC   271     24   246       1
 8 GAACATCTCCTTGGTC   275     32   242       1
 9 GGCTGGTGTGTCGCTG   258     30   225       3
10 TAAACCGTCTCTGTCG   252     25   224       3
# … with 3,045 more rows

$P7_13
# A tibble: 3,409 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   381     42   336       3
 2 CTAACTTAGTTGTAGA   312     22   286       4
 3 AGTGAGGTCTGCCCTA   308     18   285       5
 4 AGACGTTGTCCGAGTC   290     31   255       4
 5 GAACATCTCCTTGGTC   276     22   251       3
 6 CGGGTCATCCCAAGTA   271     19   246       6
 7 CTCGAGGGTTTCGCTC   260     25   234       1
 8 GAACATCTCAGAGACG   272     43   223       6
 9 TGTTCCGAGGCTAGAC   257     36   218       3
10 GAATAAGCATATGGTC   249     22   216      11
# … with 3,399 more rows

$P7_14
# A tibble: 2,629 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   371     35   334       2
 2 AGACGTTGTCCGAGTC   320     38   282       0
 3 CTAACTTAGTTGTAGA   311     28   282       1
 4 CGGGTCATCCCAAGTA   315     31   281       3
 5 AGTGAGGTCTGCCCTA   301     20   279       2
 6 GAACATCTCCTTGGTC   293     16   277       0
 7 CTCGAGGGTTTCGCTC   268     17   249       2
 8 GAATAAGCATATGGTC   273     43   230       0
 9 GAACATCTCAGAGACG   261     37   224       0
10 TGAGAGGCAACACGCC   239     17   221       1
# … with 2,619 more rows

$P7_15
# A tibble: 2,761 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   322     28   289       5
 2 CTAACTTAGTTGTAGA   242     17   221       4
 3 CGGGTCATCCCAAGTA   234     23   209       2
 4 AGTGAGGTCTGCCCTA   223     19   204       0
 5 AGACGTTGTCCGAGTC   218     18   200       0
 6 GAATAAGCATATGGTC   227     24   192      11
 7 TGAGAGGCAACACGCC   204     14   190       0
 8 GCGCCAAAGGCTATCT   226     80   146       0
 9 CATATGGGTTGCGTTA  1696   1606    89       1
10 CTCGTCATCAGAGGTG  5133   5047    81       5
# … with 2,751 more rows

$P7_2
# A tibble: 4,058 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   347     38   309       0
 2 AGTGAGGTCTGCCCTA   317     26   278      13
 3 CGGGTCATCCCAAGTA   282     30   251       1
 4 GAACATCTCCTTGGTC   268     19   247       2
 5 CTAACTTAGTTGTAGA   266     24   241       1
 6 CTCGAGGGTTTCGCTC   275     36   238       1
 7 GAACATCTCAGAGACG   288     46   238       4
 8 CATGCCTTCAATACCG   277     35   227      15
 9 AGACGTTGTCCGAGTC   253     35   217       1
10 TGTTCCGAGGCTAGAC   232     19   211       2
# … with 4,048 more rows

$P7_3
# A tibble: 4,000 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   376     77   299       0
 2 AGTGAGGTCTGCCCTA   310     30   276       4
 3 CTAACTTAGTTGTAGA   307     39   268       0
 4 AGACGTTGTCCGAGTC   290     23   265       2
 5 GAACATCTCCTTGGTC   275     29   245       1
 6 CTCGAGGGTTTCGCTC   258     22   235       1
 7 TGTTCCGAGGCTAGAC   254     29   223       2
 8 CGGGTCATCCCAAGTA   249     31   218       0
 9 CATCCACAGATGGCGT   238     22   215       1
10 CATGCCTTCAATACCG   253     31   213       9
# … with 3,990 more rows

$P7_4
# A tibble: 996 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GCTGCTTGTCCGAAGA   764    158   606       0
 2 GTGCTTCGTCCCTACT   570     86   483       1
 3 AGGGATGGTCTAACGT   396     41   352       3
 4 TGGGAAGCATTCGACA   358     18   340       0
 5 CTAACTTAGTTGTAGA   324     25   297       2
 6 AGTGAGGTCTGCCCTA   329     33   295       1
 7 AGGCCGTGTGCGATAG   309     16   293       0
 8 AGACGTTGTCCGAGTC   295     26   268       1
 9 AGCGGTCTCTTAGCCC   285     20   265       0
10 CAGATCAAGACAGAGA   289     27   262       0
# … with 986 more rows

$P7_5
# A tibble: 1,981 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   272     27   243       2
 2 CGGGTCATCCCAAGTA   251     26   224       1
 3 CTAACTTAGTTGTAGA   241     22   219       0
 4 AGTGAGGTCTGCCCTA 12662  12418   210      34
 5 GAACATCTCCTTGGTC   224     21   203       0
 6 CTCGAGGGTTTCGCTC   216     15   201       0
 7 AGACGTTGTCCGAGTC   217     18   197       2
 8 TAAACCGTCTCTGTCG   213     20   193       0
 9 GAATAAGCATATGGTC   222     29   183      10
10 GGACATTTCGTAGGAG   257     73   182       2
# … with 1,971 more rows

$P7_6
# A tibble: 7,285 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGTGAGGTCTGCCCTA   266     16   248       2
 2 AGGGATGGTCTAACGT   248     31   217       0
 3 GAACATCTCCTTGGTC   238     22   216       0
 4 CGGGTCATCCCAAGTA   210     19   191       0
 5 CTCGAGGGTTTCGCTC   208     15   191       2
 6 GTGCTTCGTCCCTACT   219     32   182       5
 7 GAATAAGCATATGGTC   208     17   181      10
 8 CTAACTTAGTTGTAGA   201     22   178       1
 9 AAATGCCTCCCAAGTA   208     29   176       3
10 TAAGAGATCTTGGGTA  5376   5228   126      22
# … with 7,275 more rows

$P7_7
# A tibble: 744 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   301     30   269       2
 2 AGTGAGGTCTGCCCTA   258     20   238       0
 3 CTAACTTAGTTGTAGA   247     10   235       2
 4 CTCGAGGGTTTCGCTC   234     17   216       1
 5 GAACATCTCAGAGACG   246     33   213       0
 6 TAAACCGTCTCTGTCG   228     14   211       3
 7 GAACATCTCCTTGGTC   235     25   209       1
 8 AGACGTTGTCCGAGTC   222     17   204       1
 9 GGACATTTCGTAGGAG   219     28   191       0
10 TAAACCGCATGTAGTC   212     22   187       3
# … with 734 more rows

$P7_8
# A tibble: 1,197 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGTGAGGTCTGCCCTA   505     40   465       0
 2 GAATAAGCATATGGTC   468     48   420       0
 3 GAACATCTCCTTGGTC   442     34   408       0
 4 CTCGAGGGTTTCGCTC   410     21   389       0
 5 TGTTCCGAGGCTAGAC   421     44   377       0
 6 GAACATCTCAGAGACG   415     52   363       0
 7 GAACGGAGTTGCGCAC   406     45   361       0
 8 CATGCCTTCAATACCG   405     49   356       0
 9 GTGCTTCGTCCCTACT   425     81   344       0
10 AAATGCCTCCCAAGTA   391     50   341       0
# … with 1,187 more rows

$P7_9
# A tibble: 2,395 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   446     42   403       1
 2 AGTGAGGTCTGCCCTA   409     31   376       2
 3 CGGGTCATCCCAAGTA   411     34   374       3
 4 CTAACTTAGTTGTAGA   385     27   356       2
 5 CTCGAGGGTTTCGCTC   375     26   346       3
 6 AGACGTTGTCCGAGTC   365     26   338       1
 7 GAACATCTCCTTGGTC   359     29   330       0
 8 GAATAAGCATATGGTC   337     34   303       0
 9 GGCTGGTGTGTCGCTG   316     18   292       6
10 GAACATCTCAGAGACG   329     45   284       0
# … with 2,385 more rows

Table of background cell-barcodes with highest number of phantoms

data_list$umi_counts_cell %>% 
  map(list("background_cells"))
$P7_0
# A tibble: 307,850 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   449     30   413       6
 2 GCTGCTTGTCCGAAGA   533    222   302       9
 3 AAATGCCTCCCAAGTA   353     54   296       3
 4 GGACATTTCGTAGGAG   315     34   277       4
 5 TAAACCGCATGTAGTC   278     33   238       7
 6 GCAAACTAGCTTATCG   253     13   237       3
 7 ACACCAACATAAGACA   238      8   227       3
 8 ACGCCGAGTCTACCTC   234     22   210       2
 9 TTAACTCCAATGGTCT   228     18   209       1
10 AACTCAGTCTAACGGT   226     16   208       2
# … with 307,840 more rows

$P7_1
# A tibble: 305,602 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GCTGCTTGTCCGAAGA   546    145   400       1
 2 AGAGTGGTCATGTCCC   199     10   188       1
 3 GACCAATAGTACGACG   200     14   186       0
 4 CTCGTCATCAGAGGTG   200     14   185       1
 5 TGGCTGGAGAAGGTGA   196     12   184       0
 6 AGGGATGAGTACTTGC   197     15   182       0
 7 GCTTCCAGTTCCGGCA   195     11   182       2
 8 CATGGCGGTGCGATAG   198     17   180       1
 9 CCACCTAAGGCCCGTT   199     19   180       0
10 GGCAATTTCTTCTGGC   195     15   180       0
# … with 305,592 more rows

$P7_10
# A tibble: 417,050 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   225     20   205       0
 2 GAATAAGCATATGGTC   227     34   188       5
 3 GATCTAGTCGAGAACG   189     15   174       0
 4 TGTTCCGAGGCTAGAC   195     19   171       5
 5 GAACATCTCAGAGACG   197     31   166       0
 6 GGCTGGTGTGTCGCTG   182     17   165       0
 7 TAAGAGATCTTGGGTA   196     27   164       5
 8 GCTGCTTGTCCGAAGA   243     79   163       1
 9 AAATGCCTCCCAAGTA   183     21   162       0
10 GAACGGAGTTGCGCAC   190     19   162       9
# … with 417,040 more rows

$P7_11
# A tibble: 323,694 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTAACTTAGTTGTAGA   332     26   304       2
 2 CACACCTTCAACGCTA   323     23   298       2
 3 GCTGCTTGTCCGAAGA   403    131   263       9
 4 TAAACCGTCTCTGTCG   272     21   250       1
 5 GAATAAGCATATGGTC   290     24   249      17
 6 AAATGCCTCCCAAGTA   288     36   248       4
 7 CATCCACAGATGGCGT   243     20   218       5
 8 GATCTAGTCGAGAACG   236     19   217       0
 9 GAACGGAGTTGCGCAC   263     39   213      11
10 GCAAACTTCGACAGCC   233     18   213       2
# … with 323,684 more rows

$P7_12
# A tibble: 351,075 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CGGGTCATCCCAAGTA   342     37   302       3
 2 CACACCTTCAACGCTA   292     31   261       0
 3 GCTGCTTGTCCGAAGA   383    110   259      14
 4 GAATAAGCATATGGTC   300     34   255      11
 5 GAACGGAGTTGCGCAC   276     32   232      12
 6 ACTTACTCAGCTCGAC   225     16   206       3
 7 TAAGAGATCTTGGGTA   239     32   202       5
 8 AAACGGGAGGATATAC   219     26   191       2
 9 GGATGTTAGGGAACGG   204     11   191       2
10 CATCCACAGATGGCGT   220     30   188       2
# … with 351,065 more rows

$P7_13
# A tibble: 358,946 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   275     25   245       5
 2 GCTGCTTGTCCGAAGA   311     91   213       7
 3 CGGACACAGCGCTTAT   223     15   206       2
 4 CATCCACAGATGGCGT   215     29   184       2
 5 GAACGGAGTTGCGCAC   225     36   183       6
 6 GCTGCGACAGTAAGCG   190      8   181       1
 7 GGCTGGTGTGTCGCTG   198     16   178       4
 8 ACATACGCAGCATGAG   198     24   174       0
 9 ACTTTCAGTTTGACTG   194     18   174       2
10 CGACTTCAGACCTAGG   194     20   174       0
# … with 358,936 more rows

$P7_14
# A tibble: 308,824 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   306     21   284       1
 2 GAACGGAGTTGCGCAC   257     25   232       0
 3 GCTGCTTGTCCGAAGA   309     77   225       7
 4 GATCTAGTCGAGAACG   240     32   208       0
 5 CATCCACAGATGGCGT   223     21   199       3
 6 GCAAACTTCGACAGCC   213     21   190       2
 7 ACTTACTCAGCTCGAC   206     16   189       1
 8 AAATGCCGTGAACCTT   197     13   184       0
 9 CGACTTCAGACCTAGG   198     15   182       1
10 TGGGAAGTCAACCAAC   198     17   181       0
# … with 308,814 more rows

$P7_15
# A tibble: 296,796 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 TAAACCGTCTCTGTCG   198     11   187       0
 2 GAACGGAGTTGCGCAC   212     24   180       8
 3 TGTTCCGAGGCTAGAC   195     22   173       0
 4 CACACCTTCAACGCTA   193     18   172       3
 5 CTCGAGGGTTTCGCTC   188     18   170       0
 6 GAACATCTCCTTGGTC   180     10   170       0
 7 CATGCCTTCAATACCG   197     31   166       0
 8 GGCTGGTGTGTCGCTG   182     16   166       0
 9 GTGCTTCGTCCCTACT   201     33   165       3
10 ACTTACTCAGCTCGAC   177     13   162       2
# … with 296,786 more rows

$P7_2
# A tibble: 360,298 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   254     18   234       2
 2 GATCTAGTCGAGAACG   219     17   201       1
 3 GAATAAGCATATGGTC   238     32   196      10
 4 ACTTACTCAGCTCGAC   228     36   192       0
 5 GCTGCTTGTCCGAAGA   271     69   191      11
 6 CGGACACAGCGCTTAT   208     26   181       1
 7 GAACGGAGTTGCGCAC   208     20   181       7
 8 GCTGCGACAGTAAGCG   197     17   180       0
 9 AAACGGGAGGATATAC   189     12   176       1
10 CATCCACAGATGGCGT   197     27   170       0
# … with 360,288 more rows

$P7_3
# A tibble: 379,883 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   247     18   223       6
 2 GCTGCTTGTCCGAAGA   306     85   215       6
 3 GAATAAGCATATGGTC   263     43   209      11
 4 TAAACCGTCTCTGTCG   231     26   205       0
 5 AGATTGCAGAGCAATT   200     13   187       0
 6 ACTTTCAGTTTGACTG   198     19   179       0
 7 TCGAGGCTCCCTCTTT   194     16   178       0
 8 GAACGGAGTTGCGCAC   214     29   176       9
 9 CTCAGAACATATGCTG   194     19   170       5
10 CGGACACAGCGCTTAT   188     20   168       0
# … with 379,873 more rows

$P7_4
# A tibble: 314,515 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   320     16   304       0
 2 CGTCTACTCTTGACGA   195      9   186       0
 3 CCTACCACATCGGTTA   197     19   178       0
 4 GACTACAGTTCAACCA   189     11   178       0
 5 GGTGAAGAGGTGATAT   189     12   177       0
 6 ACTTTCAGTTTGACTG   192     14   176       2
 7 AACGTTGTCAACACAC   187     11   175       1
 8 AAATGCCGTGAACCTT   199     25   174       0
 9 TCGAGGCTCGCGCCAA   191     17   174       0
10 TGTTCCGCACTTAAGC   187     13   174       0
# … with 314,505 more rows

$P7_5
# A tibble: 363,944 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   287     40   244       3
 2 GCTGCTTGTCCGAAGA   301     79   217       5
 3 TGAGAGGCAACACGCC   198     15   180       3
 4 AAATGCCTCCCAAGTA   195     22   169       4
 5 TGTTCCGAGGCTAGAC   191     21   167       3
 6 ACTTACTCAGCTCGAC   181     13   166       2
 7 AGATTGCAGAGCAATT   167      8   159       0
 8 CCGTACTGTCAGATAA   178     17   159       2
 9 TAAGAGATCTTGGGTA   182     20   156       6
10 TAAACCGCATGTAGTC   174     17   154       3
# … with 363,934 more rows

$P7_6
# A tibble: 349,966 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   220     11   204       5
 2 GCTGCTTGTCCGAAGA   268     78   187       3
 3 AGACGTTGTCCGAGTC   195     15   180       0
 4 TGTTCCGAGGCTAGAC   195     22   170       3
 5 GGACATTTCGTAGGAG   187     14   168       5
 6 TGAGAGGCAACACGCC   179     16   162       1
 7 AAATGCCGTGAACCTT   167     11   155       1
 8 TCTATTGCATAAAGGT   168     13   155       0
 9 ACTTACTCAGCTCGAC   169     15   153       1
10 CATGCCTTCAATACCG   182     22   152       8
# … with 349,956 more rows

$P7_7
# A tibble: 273,888 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   230     10   219       1
 2 CGGGTCATCCCAAGTA   235     25   210       0
 3 CATCCACAGATGGCGT   196     12   184       0
 4 GTGCTTCGTCCCTACT   235     48   184       3
 5 TGTTCCGAGGCTAGAC   199     21   178       0
 6 GCTGCTTGTCCGAAGA   253     75   173       5
 7 GATCTAGTCGAGAACG   190     20   170       0
 8 GCTGCGACAGTAAGCG   179      9   169       1
 9 AAATGCCTCCCAAGTA   199     30   168       1
10 ACTTACTCAGCTCGAC   181     13   168       0
# … with 273,878 more rows

$P7_8
# A tibble: 270,528 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   470     38   432       0
 2 GCTGCTTGTCCGAAGA   473    137   336       0
 3 GCAAACTAGCTTATCG   299     15   284       0
 4 GAATAAGTCCTAGGGC   304     29   275       0
 5 GTAGGCCAGCCGATTT   309     39   270       0
 6 ACACCAACATAAGACA   247     15   232       0
 7 GCTTCCACACTTCGAA   251     28   223       0
 8 TTAACTCCAATGGTCT   252     30   222       0
 9 GCACTCTAGATACACA   243     25   218       0
10 ACGCCGAGTCTACCTC   233     18   215       0
# … with 270,518 more rows

$P7_9
# A tibble: 314,582 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   389     27   358       4
 2 GAACGGAGTTGCGCAC   362     44   318       0
 3 AAATGCCTCCCAAGTA   322     29   292       1
 4 ACTTACTCAGCTCGAC   290     33   256       1
 5 CATCCACAGATGGCGT   274     24   249       1
 6 GTAGGCCAGCCGATTT   272     37   235       0
 7 GATCTAGTCGAGAACG   254     19   233       2
 8 CGGACACAGCGCTTAT   259     28   230       1
 9 AAACGGGAGGATATAC   264     35   228       1
10 GGATGTTAGGGAACGG   247     27   219       1
# … with 314,572 more rows

Save ouput to file (optional)

tic("saving output")
data_list$read_counts <- NULL
saveRDS(data_list, results_filepath)
toc()
saving output: 3599.002 sec elapsed

Session Info

# memory usage
gc()
             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    6380399   340.8    12111514    646.9    12111514    646.9
Vcells 8947572279 68264.6 31057732902 236951.7 32330482593 246662.1
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()
```
