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_l2
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_l2/output' already exists
dir.create(figures_dir)
Warning in dir.create(figures_dir): '/project/6007998/rfarouni/
index_hopping/data/novaseq_l2/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: 499.752 sec elapsed
Step 2: creating outcome counts datatable with grouping vars: 61.48 sec elapsed
Step 3: creating a chimera counts datatable and estimating hopping rate: 0.18 sec elapsed
Step 4: compute molecular complexity profile and other summary statistics: 0.49 sec elapsed
Step 5: reassign read counts, determine cutoff, and mark retained observations: 6.194 sec elapsed
Step 6: Purge and save read counts datatable to disk: 724.377 sec elapsed
Step 7: create umi counts matrices: 762.945 sec elapsed
Running workflow I: 2055.481 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: 1989.231 sec elapsed
Step 9: tallying molecules by cell-barcode: 322.964 sec elapsed
Running workflow II: 2312.195 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: 1,160 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   487     49   433       5
 2 AGTGAGGTCTGCCCTA   867    433   430       4
 3 GAACATCTCCTTGGTC   445     34   409       2
 4 CTAACTTAGTTGTAGA   420     27   391       2
 5 CGGGTCATCCCAAGTA   425     33   388       4
 6 CATGCCTTCAATACCG   782    409   373       0
 7 AGACGTTGTCCGAGTC   405     35   370       0
 8 CTCGAGGGTTTCGCTC   378     27   351       0
 9 GAATAAGCATATGGTC   395     48   337      10
10 TGAGAGGCAACACGCC   351     44   307       0
# … with 1,150 more rows

$P7_1
# A tibble: 835 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   595     51   540       4
 2 AGACGTTGTCCGAGTC   482     36   445       1
 3 CGGGTCATCCCAAGTA   493     45   444       4
 4 CTAACTTAGTTGTAGA   499     53   444       2
 5 GAATAAGCATATGGTC   472     55   417       0
 6 TGTTCCGAGGCTAGAC   483     67   416       0
 7 GTGGGTCCAAACTGTC   464     79   385       0
 8 GAACGGAGTTGCGCAC   425     46   379       0
 9 TAAACCGTCTCTGTCG   413     36   374       3
10 GTGCTTCGTCCCTACT   446     87   358       1
# … with 825 more rows

$P7_10
# A tibble: 2,464 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   286     21   265       0
 2 AGTGAGGTCTGCCCTA   279     24   253       2
 3 CTAACTTAGTTGTAGA   272     20   251       1
 4 CGGGTCATCCCAAGTA   263     35   227       1
 5 AGACGTTGTCCGAGTC   251     29   222       0
 6 GAACATCTCCTTGGTC   234     13   221       0
 7 CTCGAGGGTTTCGCTC   203     19   183       1
 8 CATGCCTTCAATACCG   222     33   179      10
 9 TGTTCCGAGGCTAGAC   201     19   179       3
10 GTGCTTCGTCCCTACT   225     51   173       1
# … with 2,454 more rows

$P7_11
# A tibble: 3,514 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   387     36   347       4
 2 GAACATCTCCTTGGTC   351     23   327       1
 3 CGGGTCATCCCAAGTA   382     51   325       6
 4 AGTGAGGTCTGCCCTA   368     57   310       1
 5 AGACGTTGTCCGAGTC   309     25   284       0
 6 TAAACCGTCTCTGTCG   296     19   272       5
 7 AAATGCCTCCCAAGTA   294     25   262       7
 8 TGTTCCGAGGCTAGAC   296     31   260       5
 9 CTCGAGGGTTTCGCTC   294     31   259       4
10 TAAGAGATCTTGGGTA   279     26   241      12
# … with 3,504 more rows

$P7_12
# A tibble: 3,062 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   384     39   339       6
 2 CTAACTTAGTTGTAGA   336     28   303       5
 3 AGTGAGGTCTGCCCTA   318     31   280       7
 4 AGACGTTGTCCGAGTC   306     27   278       1
 5 GAACATCTCCTTGGTC   313     40   273       0
 6 CTCGAGGGTTTCGCTC   281     31   248       2
 7 GAATAAGCATATGGTC   295     30   247      18
 8 AAATGCCTCCCAAGTA   264     31   230       3
 9 GGACATTTCGTAGGAG   253     23   226       4
10 TGTTCCGAGGCTAGAC   249     21   226       2
# … with 3,052 more rows

$P7_13
# A tibble: 3,416 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CGGGTCATCCCAAGTA   343     45   296       2
 2 AGTGAGGTCTGCCCTA   325     31   292       2
 3 CTAACTTAGTTGTAGA   317     33   283       1
 4 GAACATCTCCTTGGTC   305     22   282       1
 5 AGGGATGGTCTAACGT   307     33   271       3
 6 AGACGTTGTCCGAGTC   293     31   261       1
 7 GAATAAGCATATGGTC   287     36   241      10
 8 CTCGAGGGTTTCGCTC   264     23   240       1
 9 TAAACCGTCTCTGTCG   235     18   215       2
10 GGACATTTCGTAGGAG   249     32   213       4
# … with 3,406 more rows

$P7_14
# A tibble: 2,630 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   380     36   341       3
 2 CTAACTTAGTTGTAGA   307     17   288       2
 3 AGACGTTGTCCGAGTC   319     35   283       1
 4 AGTGAGGTCTGCCCTA   307     32   271       4
 5 GAACATCTCCTTGGTC   284     33   251       0
 6 CTCGAGGGTTTCGCTC   265     22   242       1
 7 TAAACCGTCTCTGTCG   260     18   241       1
 8 GAACATCTCAGAGACG   276     38   238       0
 9 GTGCTTCGTCCCTACT   299     61   234       4
10 GATCTAGTCGAGAACG   247     21   225       1
# … with 2,620 more rows

$P7_15
# A tibble: 2,765 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   290     26   261       3
 2 CTAACTTAGTTGTAGA   248     18   227       3
 3 CGGGTCATCCCAAGTA   242     24   217       1
 4 AGACGTTGTCCGAGTC   229     17   211       1
 5 AGTGAGGTCTGCCCTA   226     22   204       0
 6 GAACATCTCCTTGGTC   220     19   201       0
 7 CTCGAGGGTTTCGCTC   206     16   189       1
 8 GTGCTTCGTCCCTACT   211     43   167       1
 9 CTCGTCATCAGAGGTG  5116   5030    78       8
10 CATATGGGTTGCGTTA  1665   1597    66       2
# … with 2,755 more rows

$P7_2
# A tibble: 4,058 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   341     44   297       0
 2 AGTGAGGTCTGCCCTA   308     29   275       4
 3 CTAACTTAGTTGTAGA   293     26   267       0
 4 GAACATCTCCTTGGTC   280     20   259       1
 5 CGGGTCATCCCAAGTA   287     35   251       1
 6 CTCGAGGGTTTCGCTC   261     29   232       0
 7 AGACGTTGTCCGAGTC   262     41   221       0
 8 TAAACCGTCTCTGTCG   239     26   213       0
 9 TGTTCCGAGGCTAGAC   241     30   208       3
10 AAATGCCTCCCAAGTA   232     28   204       0
# … with 4,048 more rows

$P7_3
# A tibble: 4,006 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   373     82   290       1
 2 CTAACTTAGTTGTAGA   311     43   267       1
 3 AGTGAGGTCTGCCCTA   292     32   258       2
 4 AGACGTTGTCCGAGTC   273     26   247       0
 5 TGTTCCGAGGCTAGAC   278     34   244       0
 6 CGGGTCATCCCAAGTA   281     41   239       1
 7 CTCGAGGGTTTCGCTC   266     25   238       3
 8 GAACATCTCCTTGGTC   269     28   237       4
 9 AAATGCCTCCCAAGTA   252     27   220       5
10 TAAACCGTCTCTGTCG   246     27   218       1
# … with 3,996 more rows

$P7_4
# A tibble: 999 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GCTGCTTGTCCGAAGA   727    147   578       2
 2 GTGCTTCGTCCCTACT   583     75   506       2
 3 AGGGATGGTCTAACGT   402     32   368       2
 4 TGGGAAGCATTCGACA   343     17   326       0
 5 AGGCCGTGTGCGATAG   339     18   321       0
 6 CGGGTCATCCCAAGTA   326     29   294       3
 7 CTAACTTAGTTGTAGA   304     17   287       0
 8 AGTGAGGTCTGCCCTA   302     25   277       0
 9 AGACGTTGTCCGAGTC   306     28   276       2
10 CAGATCAAGACAGAGA   298     29   269       0
# … with 989 more rows

$P7_5
# A tibble: 2,041 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   307     34   272       1
 2 AGACGTTGTCCGAGTC   276     29   247       0
 3 AGTGAGGTCTGCCCTA 12731  12472   219      40
 4 CTAACTTAGTTGTAGA   242     26   215       1
 5 CGGGTCATCCCAAGTA   234     27   207       0
 6 GAACATCTCCTTGGTC   224     23   201       0
 7 CTCGAGGGTTTCGCTC   219     19   200       0
 8 AAATGCCTCCCAAGTA   217     18   196       3
 9 CATGCCTTCAATACCG   221     29   192       0
10 TGTTCCGAGGCTAGAC   210     19   190       1
# … with 2,031 more rows

$P7_6
# A tibble: 7,280 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGTGAGGTCTGCCCTA   260     23   234       3
 2 AGGGATGGTCTAACGT   243     27   215       1
 3 CTAACTTAGTTGTAGA   234     19   214       1
 4 GAACATCTCCTTGGTC   227     18   208       1
 5 CTCGAGGGTTTCGCTC   217     18   198       1
 6 CGGGTCATCCCAAGTA   220     24   195       1
 7 AAATGCCTCCCAAGTA   201     21   177       3
 8 GTGCTTCGTCCCTACT   212     48   157       7
 9 TAAGAGATCTTGGGTA  5407   5268   116      23
10 CTAACTTCATCGATGT  3752   3619   103      30
# … with 7,270 more rows

$P7_7
# A tibble: 743 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   285     29   256       0
 2 GAACATCTCCTTGGTC   235      9   225       1
 3 AGTGAGGTCTGCCCTA   237     20   216       1
 4 CTCGAGGGTTTCGCTC   241     23   216       2
 5 CTAACTTAGTTGTAGA   232     22   208       2
 6 AGACGTTGTCCGAGTC   222     19   202       1
 7 TGTTCCGAGGCTAGAC   208     15   193       0
 8 GGACATTTCGTAGGAG   213     18   189       6
 9 CGGGTCATCCCAAGTA   211     23   186       2
10 GAACATCTCAGAGACG   201     27   174       0
# … with 733 more rows

$P7_8
# A tibble: 1,209 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGTGAGGTCTGCCCTA   495     38   457       0
 2 GAACATCTCCTTGGTC   425     29   396       0
 3 GAACATCTCAGAGACG   443     50   393       0
 4 GAATAAGCATATGGTC   421     40   381       0
 5 CTCGAGGGTTTCGCTC   408     29   379       0
 6 TGTTCCGAGGCTAGAC   400     28   372       0
 7 TAAGAGATCTTGGGTA   403     39   364       0
 8 GAACGGAGTTGCGCAC   398     38   360       0
 9 CATGCCTTCAATACCG   398     43   355       0
10 GGCTGGTGTGTCGCTG   365     27   338       0
# … with 1,199 more rows

$P7_9
# A tibble: 2,400 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGGATGGTCTAACGT   446     39   406       1
 2 CTAACTTAGTTGTAGA   420     37   381       2
 3 AGTGAGGTCTGCCCTA   411     31   378       2
 4 AGACGTTGTCCGAGTC   375     24   348       3
 5 CGGGTCATCCCAAGTA   380     35   343       2
 6 GAACATCTCCTTGGTC   365     25   337       3
 7 GAATAAGCATATGGTC   372     38   334       0
 8 CTCGAGGGTTTCGCTC   346     27   318       1
 9 CATGCCTTCAATACCG   340     36   304       0
10 GGCTGGTGTGTCGCTG   322     17   303       2
# … with 2,390 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: 309,061 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   429     21   403       5
 2 GCTGCTTGTCCGAAGA   555    220   317      18
 3 AAATGCCTCCCAAGTA   370     45   315      10
 4 GCAAACTAGCTTATCG   252     20   228       4
 5 AACTCAGTCTAACGGT   249     24   222       3
 6 TTAACTCCAATGGTCT   234     15   216       3
 7 CTTAACTCACTACAGT   220     18   201       1
 8 ACGCCGAGTCTACCTC   216     23   191       2
 9 ACACCAACATAAGACA   208     18   189       1
10 ACATACGGTAATCGTC   210     20   189       1
# … with 309,051 more rows

$P7_1
# A tibble: 306,030 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GCTGCTTGTCCGAAGA   580    162   418       0
 2 GAACATCTCAGAGACG   439     63   376       0
 3 CAGATCAAGACAGAGA   261     60   201       0
 4 CCTAGCTGTTCTGGTA   199     11   186       2
 5 TTAGTTCGTTCAGCGC   198     11   186       1
 6 GTTTCTACAGTAAGAT   194      7   185       2
 7 CTCTAATAGGACATTA   197     14   183       0
 8 TCTTTCCCAGACAGGT   195     10   183       2
 9 ATCATCTAGTTACGGG   193     11   182       0
10 TAGACCACAAACGCGA   197     15   182       0
# … with 306,020 more rows

$P7_10
# A tibble: 418,600 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GAATAAGCATATGGTC   266     28   230       8
 2 CACACCTTCAACGCTA   224     16   207       1
 3 GCTGCTTGTCCGAAGA   260     71   186       3
 4 GAACGGAGTTGCGCAC   215     28   178       9
 5 CGGACACAGCGCTTAT   186     14   172       0
 6 TCAGGTACATAACCTG   186     15   171       0
 7 TGAGAGGCAACACGCC   180     15   165       0
 8 TAAACCGTCTCTGTCG   182     19   163       0
 9 AAATGCCTCCCAAGTA   190     29   159       2
10 ACTTACTCAGCTCGAC   176     17   159       0
# … with 418,590 more rows

$P7_11
# A tibble: 325,495 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTAACTTAGTTGTAGA   345     23   321       1
 2 CACACCTTCAACGCTA   317     18   297       2
 3 GCTGCTTGTCCGAAGA   374     90   273      11
 4 ACTTACTCAGCTCGAC   279     17   262       0
 5 GAACGGAGTTGCGCAC   304     37   258       9
 6 GAATAAGCATATGGTC   294     44   242       8
 7 CGGACACAGCGCTTAT   257     26   230       1
 8 CATCCACAGATGGCGT   247     29   215       3
 9 AAACGGGAGGATATAC   229     16   210       3
10 GTGCTTCGTCCCTACT   276     60   208       8
# … with 325,485 more rows

$P7_12
# A tibble: 352,841 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   314     30   278       6
 2 CGGGTCATCCCAAGTA   298     31   265       2
 3 GCTGCTTGTCCGAAGA   347     98   238      11
 4 GAACGGAGTTGCGCAC   264     30   223      11
 5 CATCCACAGATGGCGT   242     20   221       1
 6 CGGACACAGCGCTTAT   222     10   211       1
 7 GGATGTTAGGGAACGG   223     20   200       3
 8 AAACGGGAGGATATAC   215     16   198       1
 9 GCGGGTTTCAGGATCT   199     15   183       1
10 GTATTCTTCACCATAG   190      7   183       0
# … with 352,831 more rows

$P7_13
# A tibble: 360,836 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   251     20   226       5
 2 GAACGGAGTTGCGCAC   261     37   209      15
 3 CATCCACAGATGGCGT   243     31   208       4
 4 GCTGCTTGTCCGAAGA   302     90   205       7
 5 ACTTACTCAGCTCGAC   222     27   194       1
 6 AAACGGGAGGATATAC   205     13   191       1
 7 GGATGTTAGGGAACGG   216     25   188       3
 8 GCTGCGACAGTAAGCG   200     19   181       0
 9 CCTACCAAGGTAAACT   195     14   178       3
10 CCTACCACATCGGTTA   207     29   177       1
# … with 360,826 more rows

$P7_14
# A tibble: 310,691 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   314     17   295       2
 2 CGGGTCATCCCAAGTA   292     26   262       4
 3 GAATAAGCATATGGTC   281     35   245       1
 4 GCTGCTTGTCCGAAGA   331     82   237      12
 5 GAACGGAGTTGCGCAC   252     36   216       0
 6 CGGACACAGCGCTTAT   209     18   191       0
 7 AAACGGGAGGATATAC   216     24   190       2
 8 GCTGCGACAGTAAGCG   200     17   183       0
 9 CGACTTCAGACCTAGG   199     16   181       2
10 GTAGGCCAGCCGATTT   203     22   181       0
# … with 310,681 more rows

$P7_15
# A tibble: 298,328 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   197     17   178       2
 2 GCTGCGACAGTAAGCG   179      8   171       0
 3 TAAACCGTCTCTGTCG   189     18   170       1
 4 GAACATCTCAGAGACG   197     25   169       3
 5 GATCTAGTCGAGAACG   188     19   167       2
 6 ACTTACTCAGCTCGAC   179     13   166       0
 7 TCTATTGCATAAAGGT   175      9   166       0
 8 TGAGAGGCAACACGCC   178     12   166       0
 9 GAATAAGCATATGGTC   193     23   165       5
10 GCAAACTTCGACAGCC   180     15   164       1
# … with 298,318 more rows

$P7_2
# A tibble: 361,126 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   270     23   239       8
 2 GCTGCTTGTCCGAAGA   309     75   228       6
 3 GAATAAGCATATGGTC   251     31   206      14
 4 ACTTACTCAGCTCGAC   246     41   205       0
 5 CATCCACAGATGGCGT   237     37   200       0
 6 GAACGGAGTTGCGCAC   232     26   197       9
 7 GCTGCGACAGTAAGCG   195     16   179       0
 8 CGACTTCAGACCTAGG   193     18   174       1
 9 CGGACACAGCGCTTAT   196     22   172       2
10 GCGCCAAAGGCTATCT   190     18   172       0
# … with 361,116 more rows

$P7_3
# A tibble: 381,119 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GCTGCTTGTCCGAAGA   325     81   233      11
 2 CACACCTTCAACGCTA   255     19   231       5
 3 GAATAAGCATATGGTC   264     33   221      10
 4 GAACGGAGTTGCGCAC   247     34   205       8
 5 CATCCACAGATGGCGT   219     22   196       1
 6 CGGACACAGCGCTTAT   217     22   195       0
 7 TCAGGTACATAACCTG   195     20   175       0
 8 GCTGCGACAGTAAGCG   188     17   171       0
 9 CGACTTCAGACCTAGG   193     22   170       1
10 GATCTAGTCGAGAACG   210     39   170       1
# … with 381,109 more rows

$P7_4
# A tibble: 315,858 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   303     18   285       0
 2 CGTCCATGTGTTCGAT   199     10   189       0
 3 AGATTGCAGAGCAATT   200     16   184       0
 4 CGATGTACATATGCTG   199     14   184       1
 5 AAATGCCGTGAACCTT   199     15   182       2
 6 AACGTTGTCAACACAC   195     14   181       0
 7 GCTGCGACAGTAAGCG   194     12   180       2
 8 TAAGCGTTCAGAGACG   194     14   180       0
 9 TCGAGGCTCCCTCTTT   193     10   180       3
10 ACACCAACATAAGACA   198     20   178       0
# … with 315,848 more rows

$P7_5
# A tibble: 364,537 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   288     48   239       1
 2 TAAACCGTCTCTGTCG   190     16   174       0
 3 GAACATCTCAGAGACG   199     31   166       2
 4 ACTTACTCAGCTCGAC   178     13   165       0
 5 GGCTGGTGTGTCGCTG   178     15   161       2
 6 TAAACCGCATGTAGTC   181     21   158       2
 7 GTGGGTCCAAACTGTC   190     34   156       0
 8 GCTGCGACAGTAAGCG   166     11   155       0
 9 CCGTACTGTCAGATAA   175     19   154       2
10 CATCCACAGATGGCGT   164     10   151       3
# … with 364,527 more rows

$P7_6
# A tibble: 351,694 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GCTGCTTGTCCGAAGA   327     86   231      10
 2 CACACCTTCAACGCTA   224     22   196       6
 3 TAAACCGTCTCTGTCG   188     15   173       0
 4 TGTTCCGAGGCTAGAC   200     23   173       4
 5 GGACATTTCGTAGGAG   180      9   168       3
 6 ACTTACTCAGCTCGAC   183     17   165       1
 7 AGACGTTGTCCGAGTC   185     21   164       0
 8 GGCTGGTGTGTCGCTG   172      9   163       0
 9 CGGACACAGCGCTTAT   199     42   157       0
10 CGACTTCAGACCTAGG   169     14   154       1
# … with 351,684 more rows

$P7_7
# A tibble: 275,155 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   238     16   215       7
 2 GCTGCTTGTCCGAAGA   272     65   200       7
 3 ACTTACTCAGCTCGAC   206     14   191       1
 4 GTGCTTCGTCCCTACT   245     50   187       8
 5 TGAGAGGCAACACGCC   194      6   186       2
 6 CATCCACAGATGGCGT   197     14   182       1
 7 TAAACCGTCTCTGTCG   194     13   180       1
 8 TCTATTGCATAAAGGT   195     15   179       1
 9 CATGCCTTCAATACCG   199     29   170       0
10 TAAACCGCATGTAGTC   197     26   167       4
# … with 275,145 more rows

$P7_8
# A tibble: 272,056 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   478     33   445       0
 2 AAATGCCTCCCAAGTA   418     48   370       0
 3 GCTGCTTGTCCGAAGA   474    152   322       0
 4 ACGCCGAGTCTACCTC   253     19   234       0
 5 ACACCAACATAAGACA   251     22   229       0
 6 GCAAACTAGCTTATCG   247     23   224       0
 7 TTAACTCCAATGGTCT   249     29   220       0
 8 GATCGTATCGGCGCAT   249     43   206       0
 9 ACGCCGAAGATCCGAG   203     12   191       0
10 ATTCTACCAGGACGTA   199      9   190       0
# … with 272,046 more rows

$P7_9
# A tibble: 316,062 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CACACCTTCAACGCTA   397     32   362       3
 2 ACTTACTCAGCTCGAC   321     26   295       0
 3 GTAGGCCAGCCGATTT   284     33   251       0
 4 CATCCACAGATGGCGT   271     22   248       1
 5 AAATGCCTCCCAAGTA   275     29   244       2
 6 CGGACACAGCGCTTAT   274     28   244       2
 7 CGGAGTCCATGAGCGA   248     12   236       0
 8 AAACGGGAGGATATAC   269     43   225       1
 9 CGATGTACATATGCTG   245     22   223       0
10 GCAAACTTCGACAGCC   243     18   223       2
# … with 316,052 more rows

Save ouput to file (optional)

tic("saving output")
data_list$read_counts <- NULL
saveRDS(data_list, results_filepath)
toc()
saving output: 3582.473 sec elapsed

Session Info

# memory usage
gc()
             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    6381580   340.9     9589840    512.2     9589840    512.2
Vcells 8960661518 68364.5 31123741425 237455.4 32387901149 247100.1
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()
```
