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: hiseq2500

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/hiseq2500/output' already exists

dir.create(figures_dir)

Warning in dir.create(figures_dir): '/project/6007998/rfarouni/
index_hopping/data/hiseq2500/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: 60.875 sec elapsed
Step 2: creating outcome counts datatable with grouping vars: 8.44 sec elapsed
Step 3: creating a chimera counts datatable and estimating hopping rate: 0.358 sec elapsed
Step 4: compute molecular complexity profile and other summary statistics: 1.383 sec elapsed
Step 5: reassign read counts, determine cutoff, and mark retained observations: 0.782 sec elapsed
Step 6: Purge and save read counts datatable to disk: 100.268 sec elapsed
Step 7: create umi counts matrices: 53.312 sec elapsed
Running workflow I: 225.432 sec elapsed

1. Show data and summary statistics

Read counts datatable

data_list$read_counts

ABCDEFGHIJ0123456789

cell <chr>	umi <int>	gene <chr>	A1 <dbl>
AAACCTGAGAAACCAT	468967	Csn3	18
AAACCTGAGAAACCAT	1012503	Sdc4	2
AAACCTGAGAAACCAT	621442	Txn1	8
AAACCTGAGAAACCAT	648997	Mrpl54	7
AAACCTGAGAAACCAT	325897	Higd1a	4
AAACCTGAGAAACCAT	476545	Rps21	3
AAACCTGAGAAACCAT	776993	Mlec	6
AAACCTGAGAAACCAT	283231	Nap1l1	3
AAACCTGAGAAACCAT	5045	Csn1s2a	6
AAACCTGAGAAACCAT	174081	Rps28	1

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

ABCDEFGHIJ0123456789

outcome <chr>	s <int>	qr <dbl>	r <int>	n <int>	o <dbl>	FP <dbl>	FN <dbl>	TP <dbl>	TN <dbl>
1,0,0,0,0,0,1,0	7	4.9826e-01	2	252	1.0000000	1449	303	44061167	25944
2,0,0,0,0,0,2,0	1	4.8863e-01	4	2	0.9999943	1324	430	44061040	26069
0,1,0,0,1,0,0,0	5	4.3770e-01	2	62	0.9999942	1323	431	44061039	26070
0,1,0,0,0,0,1,0	2	3.5989e-01	2	32	0.9999928	1296	465	44061004	26098
1,1,0,0,0,0,0,0	2	3.5825e-01	2	35	0.9999921	1284	486	44060984	26109
0,0,0,0,1,0,1,0	5	3.0441e-01	2	42	0.9999913	1271	508	44060962	26122
1,0,0,0,1,0,0,0	5	3.0290e-01	2	36	0.9999904	1259	538	44060932	26134
0,0,0,0,1,1,0,0	6	2.8719e-01	2	133	0.9999895	1248	563	44060907	26145
0,1,0,0,0,1,0,0	6	2.3874e-01	2	101	0.9999865	1210	657	44060812	26184
0,0,0,0,0,1,1,0	6	1.4988e-01	2	94	0.9999842	1185	734	44060736	26208

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

ABCDEFGHIJ0123456789

n_reads <int>	n_cugs <dbl>	n_pm <dbl>	u <dbl>	g <dbl>	frm <dbl>	p_chimeras <dbl>
268768037	44062616	27393	0.00059568	0.000026012	6.0997	0.00059245

Marginal summary statistics

 data_list$summary_stats$conditional

ABCDEFGHIJ0123456789

r <int>	n_cugs <dbl>	p_cugs <dbl>	A1 <dbl>	A2 <dbl>	B1 <dbl>	B2 <dbl>
1	11242464	0.255147447442	0.0813012165	0.1380801397	0.00198773	0.002577638
2	7556220	0.171488229387	0.0835708198	0.1497099079	0.00122151	0.001662273
3	5707051	0.129521383842	0.0963883098	0.1726053730	0.00114391	0.001429343
4	4344844	0.098606129060	0.1151406702	0.2038855710	0.00110930	0.001212529
5	3342437	0.075856526539	0.1395874926	0.2378879841	0.00097725	0.000912628
6	2604471	0.059108406092	0.1690894875	0.2651573646	0.00084726	0.000795491
7	2048065	0.046480785435	0.2028776988	0.2792064705	0.00070840	0.000676039
8	1603535	0.036392187881	0.2391953559	0.2793755671	0.00067905	0.000659325
9	1243973	0.028231937023	0.2784262824	0.2663382208	0.00060371	0.000691423
10	946847	0.021488669670	0.3173679591	0.2448392401	0.00063907	0.000892013

Molecular proportions complexity profile

 data_list$summary_stats$pi_r_hat

ABCDEFGHIJ0123456789

r <int>	A1 <dbl>	A2 <dbl>	B1 <dbl>	B2 <dbl>	C1 <dbl>	C2 <dbl>
1	0.08129612491	0.13808166378	0.00197339857	0.00256337357	0.17659025386	0.49747393973
2	0.08356599262	0.14971278697	0.00120708800	0.00164790207	0.19234124760	0.47740539977
3	0.09638497610	0.17261091982	0.00112947534	0.00141494498	0.21600458354	0.40910513993
4	0.11513952141	0.20389476250	0.00109486805	0.00119810588	0.24214127028	0.31471504643
5	0.13958919228	0.23790113743	0.00096280035	0.00089816931	0.25825297205	0.21398101131
6	0.16909462467	0.26517369528	0.00083279516	0.00078101918	0.25537062669	0.12923889453
7	0.20288677287	0.27922443808	0.00069392185	0.00066155303	0.23355838792	0.07055440574
8	0.23920866152	0.27939355439	0.00066456118	0.00064483683	0.19978607967	0.03576579532
9	0.27844415906	0.26635468903	0.00058921665	0.00067693867	0.16223249252	0.01708962141
10	0.31739037315	0.24485320337	0.00062457832	0.00087755256	0.12568670097	0.00801607384

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

ABCDEFGHIJ0123456789

max_r <int>	phat <dbl>	phat_low <dbl>	phat_high <dbl>	SIHR <dbl>	SBIHR <dbl>
1706	0.9999	0.9999	0.9999	0.00010194	0.00011286

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

ABCDEFGHIJ0123456789

approach <chr>	outcome <chr>	s <int>	qr <dbl>	n <int>	tor <dbl>	o <dbl>	FP <dbl>	FN <dbl>	TP <dbl>
torc_before	0,0,1,0,1,0,0,0	5	0.0062969	41	49.6366	0.99947	984	23384	44038085
discard_torc	0,0,1,0,0,0,0,0	3	0.0073119	22347	2.9735	0.99997	1147	1201	44060269
torc_after	0,1,1,0,0,0,0,0	2	0.0080795	32	2.8675	0.99997	1147	1169	44060301
no_discarding	1,0,0,0,0,0,1,0	7	0.4982596	252	NaN	1.00000	1449	303	44061167
no_purging	NA	NA	NA	NA	NA	1.00060	27393	0	44061470

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)

Using defaultDrops cell calling instead.
      EmptyDrops function call failed for purged sample B1

Warning in smooth.spline(x[new.keep], y[new.keep], df = df, ...): not using
invalid df; must have 1 < df <= n := #{unique x} = 7

Warning in smooth.spline(x[new.keep], y[new.keep], df = df, ...): not using
invalid df; must have 1 < df <= n := #{unique x} = 7

Step 7: identify RNA-containing cells: 733.182 sec elapsed
Step 9: tallying molecules by cell-barcode: 25.698 sec elapsed
Running workflow II: 758.881 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

ABCDEFGHIJ0123456789

sample <chr>	m <dbl>	prm_ret <dbl>	prm_disc <dbl>	ppm <dbl>	prop_m <dbl>	prop_reads <dbl>	FRM <dbl>	rm_ret <dbl>
A1	5.93931	0.99862	0.0000075766	0.00136884	0.1347123	0.13436	6.0801	5931139
A2	8.01873	0.99967	0.0000185815	0.00030878	0.1818765	0.13477	4.5171	8016103
B1	0.14831	0.97948	0.0000000000	0.02051783	0.0033639	0.12624	228.7785	145267
B2	0.19973	0.98648	0.0000000000	0.01351859	0.0045301	0.13215	177.8310	197025
C1	8.51224	0.99966	0.0000243179	0.00031778	0.1930701	0.11791	3.7228	8509327
C2	14.19085	0.99979	0.0000359387	0.00016933	0.3218692	0.12152	2.3015	14187933
D1	5.45395	0.99954	0.0000518890	0.00041089	0.1237036	0.11167	5.5033	5451425
D2	1.62575	0.99843	0.0000036906	0.00156789	0.0368744	0.12138	20.0664	1623197

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

ABCDEFGHIJ0123456789

sample <chr>	m <dbl>	prm_ret <dbl>	prm_disc <dbl>	ppm <dbl>	prop_m <dbl>	prop_reads <dbl>	FRM <dbl>
A1	5.40001	0.99998	0.0000070370	0.00000907406	0.1340182	0.13436	6.6874
A2	7.35779	0.99998	0.0000159015	0.00000489277	0.1826066	0.13477	4.9229
B1	0.11064	0.99521	0.0000000000	0.00479013774	0.0027460	0.12624	306.6604
B2	0.14207	0.99999	0.0000000000	0.00000703898	0.0035258	0.13215	250.0056
C1	7.86794	0.99998	0.0000191918	0.00000381294	0.1952675	0.11791	4.0276
C2	12.98775	0.99997	0.0000312602	0.00000046197	0.3223316	0.12152	2.5147
D1	4.94792	0.99994	0.0000511326	0.00000727579	0.1227981	0.11167	6.0661
D2	1.47901	0.99999	0.0000020284	0.00000540904	0.0367062	0.12138	22.0573

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

ABCDEFGHIJ0123456789

barcode <chr>	A1 <dbl>	A2 <dbl>	B1 <dbl>	B2 <dbl>	C1 <dbl>	C2 <dbl>	D1 <dbl>	D2 <dbl>
consensus_background	123769	138948	26138	29971	145283	195614	98969	61353
transition_cell	1	0	0	0	0	4	1	1
phantom_background	1332	770	1258	1136	808	668	778	975
transition_background	0	2	17	0	0	1	0	0
consensus_cell	409	564	199	16	739	1316	685	160
phantom_cell	0	0	4	0	0	0	0	0

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

data_list$umi_counts_cell %>% 
  map(list("called_cells"))

$A1
# A tibble: 409 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AGGTCCGGTCCGAGTC 37482  37479     3       0
 2 ACGTCAAGTTGCCTCT 31541  31538     2       1
 3 CACCACTTCGGTTCGG 18666  18664     2       0
 4 CGTAGGCCAACACCTA 65340  65338     2       0
 5 CTGCTGTTCCGGGTGT 17297  17294     2       1
 6 TCTCTAAAGATCACGG 21938  21936     2       0
 7 AACCGCGTCATGCTCC 47423  47422     1       0
 8 ACATACGCATTCGACA  3517   3516     1       0
 9 ACGAGGACATCACCCT 15208  15207     1       0
10 ACGGCCACAAGGACAC 49067  49064     1       2
# … with 399 more rows

$A2
# A tibble: 566 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 ACGAGCCCACCGTTGG 43895  43892     3       0
 2 CGTGTAATCTAACTCT 22943  22941     2       0
 3 CTTAGGAAGAAACGCC 19123  19121     2       0
 4 GCTGGGTAGTAGGTGC 55651  55648     2       1
 5 AAAGATGAGGCCCTCA 68719  68718     1       0
 6 AACTCCCCATTCGACA 15035  15034     1       0
 7 ACAGCTAAGGCCCGTT 29916  29914     1       1
 8 ACGGGCTTCGGTCCGA 34499  34498     1       0
 9 ACTTACTCAACACCCG 29456  29455     1       0
10 CAAGATCCAACACGCC 44113  44112     1       0
# … with 556 more rows

$B1
# A tibble: 220 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT   103      9    94       0
 2 TCGTACCGTCCATGAT    74     11    63       0
 3 TGCACCTTCTGGCGAC    74     11    63       0
 4 GTGCAGCCAAGCGAGT    43      5    38       0
 5 CTAGCCTTCCGCGCAA    41      4    37       0
 6 AGCAGCCGTTAAGGGC    28      2    26       0
 7 TGCACCTCATGCCACG    28      3    25       0
 8 TCATTACCAGGCAGTA    19      3    16       0
 9 GATCGTAGTCACCCAG    18      4    14       0
10 GTGCTTCAGGCATTGG    15      1    14       0
# … with 210 more rows

$B2
# A tibble: 16 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 TCGTACCGTCCATGAT 26473  26472     1       0
 2 ACGCCAGAGTACGTAA   417    417     0       0
 3 AGCAGCCGTTAAGGGC  8853   8853     0       0
 4 CAAGGCCGTCCGAGTC   232    232     0       0
 5 CGTGAGCCAAGGACTG  1478   1478     0       0
 6 CTAGCCTTCCGCGCAA 11808  11808     0       0
 7 CTCACACGTCTCTCGT 36274  36274     0       0
 8 CTTGGCTCAAAGTGCG  1034   1034     0       0
 9 GACAGAGTCAATACCG   211    211     0       0
10 GATCGTAGTCACCCAG  5904   5904     0       0
11 GTGCAGCCAAGCGAGT 12113  12113     0       0
12 GTGGGTCCAGGCTGAA  2190   2190     0       0
13 TCATTACCAGGCAGTA  5893   5893     0       0
14 TGCACCTCATGCCACG  5479   5479     0       0
15 TGCACCTTCTGGCGAC 22310  22310     0       0
16 TGGCTGGCATCCGTGG  1397   1397     0       0

$C1
# A tibble: 739 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 ACTATCTTCCTGTACC 33437  33434     2       1
 2 CCCTCCTGTGATGTCT 10899  10897     2       0
 3 GGTGCGTCAGTTAACC 38377  38375     2       0
 4 ACCTTTACAAAGTGCG 19839  19837     1       1
 5 ACGGCCAGTACTCAAC 26169  26168     1       0
 6 AGCGTATGTTCTCATT 15803  15802     1       0
 7 AGGGATGTCGCCGTGA  9354   9353     1       0
 8 AGGTCATGTAAGCACG 24932  24930     1       1
 9 ATCTGCCTCTTGCATT  4779   4777     1       1
10 ATTATCCGTTAAGATG 30075  30072     1       2
# … with 729 more rows

$C2
# A tibble: 1,317 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTTAACTGTGAGTATA  6233   6230     3       0
 2 ATAACGCCACGGTTTA  1584   1583     1       0
 3 GGAACTTTCAAAGTAG  1634   1633     1       0
 4 TACCTATCATCACAAC  6081   6080     1       0
 5 AAACCTGCAGTGGGAT  8200   8200     0       0
 6 AAACCTGGTCGAAAGC  7482   7482     0       0
 7 AAACGGGCAAGTTAAG  9265   9265     0       0
 8 AAACGGGGTTATCACG 13268  13268     0       0
 9 AAACGGGGTTGTCTTT 13437  13437     0       0
10 AAAGATGAGAGGTTGC   795    795     0       0
# … with 1,307 more rows

$D1
# A tibble: 685 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AAAGATGAGGCCGAAT 13329  13326     1       2
 2 AAGGCAGGTTATGTGC  4278   4277     1       0
 3 ACCGTAACAGGACGTA 10930  10929     1       0
 4 AGCTCCTTCTCAAACG 34839  34837     1       1
 5 ATAACGCGTTGTCTTT 11030  11027     1       2
 6 ATCCACCGTAAGGGAA 20527  20526     1       0
 7 ATCGAGTCAGATCTGT 25174  25172     1       1
 8 CATATTCGTACGAAAT 23309  23304     1       4
 9 CATCGGGGTATGAAAC 29755  29753     1       1
10 CCAGCGATCGACCAGC 13789  13785     1       3
# … with 675 more rows

$D2
# A tibble: 160 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 AACCATGTCAGCGACC 18595  18594     1       0
 2 AGATTGCCAGGTTTCA 14970  14969     1       0
 3 CGCGTTTCATGTTGAC 11987  11986     1       0
 4 CGTCAGGAGCTTCGCG 14085  14084     1       0
 5 GCATGTATCCCAGGTG 27178  27177     1       0
 6 GTGTTAGGTAGCGTCC 11840  11839     1       0
 7 TCAGGTATCATAACCG  8832   8831     1       0
 8 TGTCCCAGTCATCCCT  3194   3193     1       0
 9 AAAGCAAGTGAACCTT  9734   9734     0       0
10 AAAGCAATCTCCTATA 11917  11917     0       0
# … with 150 more rows

Table of background cell-barcodes with highest number of phantoms

data_list$umi_counts_cell %>% 
  map(list("background_cells"))

$A1
# A tibble: 125,102 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GGCTGGTGTCCCTACT    86      4    82       0
 2 CTCACACGTCTCTCGT    99     19    80       0
 3 CATGCCTAGTGCTGCC    92     18    74       0
 4 TGCACCTTCTGGCGAC    57      5    52       0
 5 GGAATAAGTCAGGACA    53      3    50       0
 6 CACACAAAGGCGATAC    67     19    48       0
 7 TCGTACCGTCCATGAT    56     10    46       0
 8 CGGCTAGGTAAACCTC    42      0    42       0
 9 CTTCTCTCAACTGCTA    48      6    42       0
10 TGGTTAGTCCTGCAGG    40      1    39       0
# … with 125,092 more rows

$A2
# A tibble: 139,718 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GGCTGGTGTCCCTACT    97     15    82       0
 2 CTCACACGTCTCTCGT    93     21    72       0
 3 CATGCCTAGTGCTGCC    84     15    69       0
 4 TCGTACCGTCCATGAT    76     18    58       0
 5 TGCACCTTCTGGCGAC    66     14    52       0
 6 CACACAAAGGCGATAC    55     14    41       0
 7 GTGCAGCCAAGCGAGT    50     12    38       0
 8 AAGCCGCGTGCTCTTC    44      8    36       0
 9 GGAATAAGTCAGGACA    38      5    33       0
10 CTAGCCTTCCGCGCAA    40      8    32       0
# … with 139,708 more rows

$B1
# A tibble: 27,396 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 ACGGCCACAAGGACAC     8      0     8       0
 2 AGATTGCCAGGTTTCA     8      0     8       0
 3 CAGCCGACAGGATCGA     8      0     8       0
 4 GATCGTAAGATACACA     8      0     8       0
 5 ACTGTCCCAGCGAACA     8      1     7       0
 6 ATTATCCGTGTGAATA     8      1     7       0
 7 CAGATCAGTGCTCTTC     7      0     7       0
 8 CTACACCCACGCGAAA     7      0     7       0
 9 TTTGGTTCAGGAACGT     8      1     7       0
10 ACGGGCTTCGGTCCGA     6      0     6       0
# … with 27,386 more rows

$B2
# A tibble: 31,107 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GGCTGGTGTCCCTACT    96     10    86       0
 2 CATGCCTAGTGCTGCC    85      9    76       0
 3 CACACAAAGGCGATAC    66      8    58       0
 4 AAGCCGCGTGCTCTTC    46      6    40       0
 5 GGAATAAGTCAGGACA    42      3    39       0
 6 ATAAGAGAGTGTGGCA    32      3    29       0
 7 TGCCCATCAGTAAGCG    32      3    29       0
 8 CACACTCCACATAACC    29      3    26       0
 9 CATCGAAAGTCAATAG    28      6    22       0
10 CATATTCGTTGTACAC    14      0    14       0
# … with 31,097 more rows

$C1
# A tibble: 146,091 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GGCTGGTGTCCCTACT   113     10   103       0
 2 CATGCCTAGTGCTGCC   107      9    98       0
 3 CTCACACGTCTCTCGT    88     16    72       0
 4 CACACAAAGGCGATAC    71     12    59       0
 5 TCGTACCGTCCATGAT    64     10    54       0
 6 TGCACCTTCTGGCGAC    57     12    45       0
 7 GGAATAAGTCAGGACA    50      8    42       0
 8 AAGCCGCGTGCTCTTC    47      7    40       0
 9 CATCGAAAGTCAATAG    42      5    37       0
10 ATAAGAGAGTGTGGCA    38      7    31       0
# … with 146,081 more rows

$C2
# A tibble: 196,286 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CATGCCTAGTGCTGCC   103     13    90       0
 2 GGCTGGTGTCCCTACT   109     20    89       0
 3 CACACAAAGGCGATAC    67      2    65       0
 4 CTCACACGTCTCTCGT    69      8    61       0
 5 TCGTACCGTCCATGAT    67      9    58       0
 6 TGCACCTTCTGGCGAC    54      8    46       0
 7 GGAATAAGTCAGGACA    43      5    38       0
 8 AAGCCGCGTGCTCTTC    44      8    36       0
 9 CATCGAAAGTCAATAG    40      7    33       0
10 CTAGCCTTCCGCGCAA    33      2    31       0
# … with 196,276 more rows

$D1
# A tibble: 99,748 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 CTCACACGTCTCTCGT    94     20    74       0
 2 GGCTGGTGTCCCTACT    80      8    72       0
 3 CATGCCTAGTGCTGCC    86     15    71       0
 4 TCGTACCGTCCATGAT    62     16    46       0
 5 CACACAAAGGCGATAC    46      5    41       0
 6 TGCACCTTCTGGCGAC    44      7    37       0
 7 AAGCCGCGTGCTCTTC    32      3    29       0
 8 CATCGAAAGTCAATAG    28      4    24       0
 9 CTAGCCTTCCGCGCAA    33      9    24       0
10 GTGCAGCCAAGCGAGT    27      4    23       0
# … with 99,738 more rows

$D2
# A tibble: 62,329 x 5
   cell                 m rm_ret    pm rm_disc
   <chr>            <int>  <dbl> <int>   <dbl>
 1 GGCTGGTGTCCCTACT   120     19   101       0
 2 CTCACACGTCTCTCGT    90      8    82       0
 3 CATGCCTAGTGCTGCC    93     12    81       0
 4 TCGTACCGTCCATGAT    83     22    60       1
 5 CACACAAAGGCGATAC    66      7    59       0
 6 TGCACCTTCTGGCGAC    62      5    57       0
 7 GTGCAGCCAAGCGAGT    40      5    35       0
 8 ATAAGAGAGTGTGGCA    32      2    30       0
 9 AAGCCGCGTGCTCTTC    28      0    28       0
10 CTAGCCTTCCGCGCAA    33      5    28       0
# … with 62,319 more rows

Save ouput to file (optional)

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

saving output: 289.009 sec elapsed

Session Info

# memory usage
gc()

            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   6055524  323.4    9523112   508.6    9523112   508.6
Vcells 705180869 5380.2 2345841278 17897.4 2931643582 22366.7

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()
```


Phantom Purge

Analysis workflow for hiseq2500 data

Rick Farouni

2019-June-18

Prepare analysis workflow

Set parameters

Set filepaths and parameters

Load libraries

Load functions

Define workflow functions

Run workflow Part I

1. Show data and summary statistics

Read counts datatable

Outcome counts datatable

Summary statistics of the joined read counts datatable

Marginal summary statistics

Molecular proportions complexity profile

2. Estimating the sample index hopping rate (SIHR)

GLM fit estimates

Model fit plot

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

Plot tradoff plots

Part II: Process data for downstream analysis

Examine the consequences of index hopping

Tally of predicted phantoms by sample

Tally of predicted phantoms in called cells

Tally of barcodes by concordance between purged and unpurged data

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

Table of background cell-barcodes with highest number of phantoms

Save ouput to file (optional)

Session Info