QC total counts barseq run

library(dplyr)

mycolors = c("darkorange" ,"dodgerblue","limegreen","navy",
             "mediumpurple","royalblue3", "darkolivegreen4",
             "firebrick" ,"hotpink", "plum4","blue" , "magenta2")

mycols = c("#FF8C007F", "#1E90FF7F", "#32CD327F" ,"#0000807F" ,"#FF69B47F", "#8B668B7F", "#0000FF7F" ,
           "#EE00EE7F", "#9370DB7F","#B222227F", "#6E8B3D7F" ,"#3A5FCD7F")

op = par(no.readonly = T)
# par0 = par(no.readonly = T)
# par0$mfrow = c(1,2)
# par0$pch=19

ptrack = read.delim('feb8_4_pmaster_BS5_BS6_BS8_BS9_olti_halo_chlorox.txt',stringsAsFactors = F,header=T)

source('~/RProjects/dec16_2017_barseq/feb9_scratchpad.R')

geneDir =  "/home/common/barseq_gg_pdata_exp_info/summary_barseq"

#source('~/RProjects/dec16_2017_barseq/jan29_working2_source_functions_barseq.R', echo=TRUE)

countDir = '/home/common/barseq_output'
#expDir = '/home/common/barseq_gg_pdata_exp_info'

countFiles = list.files(countDir)       # filenames count matrices

countFiles = countFiles[grep('rds',countFiles)]

cfiles = paste(countDir,countFiles, sep = "/")

ptrack = ptrack %>% arrange
ptrk = distinct(ptrack,file,short)


pfile= ptrk$file
pshort = ptrk$short

wcount = match(pfile,cfiles)

cfiles = cfiles[wcount]

pfile == cfiles

## [1] TRUE TRUE TRUE TRUE

load all count matrices from common/barseq_output directory

tmp = NULL
for (i in 1:length(cfiles)) {

  tmp[[i]] = mybarseqrows(readRDS(cfiles[i]))
  names(tmp)[i] = cfiles[i]
  
}

names(tmp) = pshort

ncoln = sapply(tmp,ncol)
nrow = sapply(tmp,nrow)

coln = sapply(tmp,colnames)

coln = lapply(coln,function(x) x = gsub('BS-0','BS',x))
for (i in 1:length(tmp)) colnames(tmp[[i]]) = coln[[i]]

print(coln)

## $BS5_7201_feb2018
##  [1] "BS138" "BS139" "BS140" "BS141" "BS142" "BS143" "BS144" "BS145"
##  [9] "BS146" "BS147" "BS148" "BS149" "BS150" "BS151" "BS152" "BS153"
## [17] "BS154" "BS155" "BS156" "BS157" "BS158" "BS159" "BS160" "BS161"
## [25] "BS162" "BS163" "BS164" "BS165" "BS166" "BS167" "BS168" "BS169"
## [33] "BS170" "BS171" "BS172" "BS173"
## 
## $BS6_7371_feb2018
##  [1] "BS210" "BS211" "BS212" "BS213" "BS214" "BS215" "BS216" "BS217"
##  [9] "BS218" "BS219" "BS220" "BS221" "BS222" "BS223" "BS224" "BS225"
## [17] "BS226" "BS227" "BS228" "BS229" "BS368" "BS369" "BS370" "BS371"
## [25] "BS372"
## 
## $BS8_14778_feb2018
##  [1] "BS326" "BS327" "BS328" "BS329" "BS332" "BS333" "BS334" "BS335"
##  [9] "BS336" "BS337" "BS338"
## 
## $BS9_18674_feb2018
##  [1] "BS339" "BS340" "BS341" "BS342" "BS343" "BS344" "BS345" "BS346"
##  [9] "BS347" "BS348" "BS349" "BS350" "BS351" "BS352" "BS353" "BS354"
## [17] "BS355" "BS356" "BS357" "BS358" "BS359" "BS360" "BS361" "BS362"
## [25] "BS363" "BS364" "BS365" "BS366" "BS367"

quick check for duplicated samples

coln = sapply(tmp,colnames)

clen = as.vector(sapply(coln,length))
names(clen)=names(coln)

ucoln = unlist(coln,use.names = F)
names(ucoln)= rep(names(clen),times = clen)

coless = sapply(tmp,function(x) x = colSums(x[ess$strain,]))
colnon = sapply(tmp,function(x) x = colSums(x[noness$strain,]))

coless = unlist(coless,use.names = F)
colnon = unlist(colnon,use.names = F)

names(coless) = names(ucoln)

names(colnon) = names(ucoln)

print(table(duplicated(colnon)))

## 
## FALSE 
##   101

print(table(duplicated(coless)))

## 
## FALSE 
##   101

QC total reads essential vs nonessential

working cutoff = 1 million counts for essential; 1 million counts for nonessential

replot after removing samples that faiiled QC

eraw = lapply(tmp,function(x) x = mystats(x[ess$strain,]))

nraw = lapply(tmp,function(x) x = mystats(x[noness$strain,]))

names(xess)=names(tmp)
names(xnon)=names(tmp)


ecoln = sapply(xess,ncol)
ncoln = sapply(xnon,ncol)

wecoln = which(ecoln <= 2 )

wncoln = which(ncoln <= 2 )

if (length(wecoln)>0) xess = xess[-wecoln]

if(sum(ecoln)== 0) {
  xess = NA
  print('no essential samples pass QC')
}

if (length(wncoln)>0) xnon = xnon[-wncoln]

if(sum(ncoln)== 0) {
  xnon = NA
  print('no nonessential samples pass QC')
}

ecoln = sapply(xess,ncol)
ncoln = sapply(xnon,ncol)

cat("pass_essQC:")

## pass_essQC:

ecoln

##  BS5_7201_feb2018 BS8_14778_feb2018 BS9_18674_feb2018 
##                10                11                18

cat("pass_nonQC:")

## pass_nonQC:

ncoln

##  BS5_7201_feb2018  BS6_7371_feb2018 BS8_14778_feb2018 BS9_18674_feb2018 
##                27                24                 8                11

xess = lapply(xess,mysumtags)

## [1] 2264   10
## [1] 1108   10
## [1] 2264   11
## [1] 1108   11
## [1] 2264   18
## [1] 1108   18

xnon = lapply(xnon,mysumtags)

## [1] 10216    27
## [1] 4999   27
## [1] 10216    24
## [1] 4999   24
## [1] 10216     8
## [1] 4999    8
## [1] 10216    11
## [1] 4999   11

estats = lapply(xess,mystats)
nstats = lapply(xnon,mystats)

replot counts after removing samples that failed QC; replot

1. table: summary statistics per sample

2. percent of null counts for samples that passed QC

ESSENTIAL

for (i in 1:elen) {
  print(kable(mystats(xess[[i]]),
              caption = paste0("table",i," stats summed essential tags ",enam[i])))
  
  mybarplotnull(xess[[i]],group = factor(lecolx[[i]]),col = mycols[i],
          
          nam=paste("essential",enam[i]))
}

## 
## 
## Table: table1 stats summed essential tags BS5_7201_feb2018
## 
##          min        Q1   median   mean        Q3     max    libsum   perc05   perc10   perc90   perc95   zeros   percZeros
## ------  ----  --------  -------  -----  --------  ------  --------  -------  -------  -------  -------  ------  ----------
## BS149      0    467.00    882.5    991   1366.25    6564   1097778       22     4030       48     3872      19           2
## BS153      0   1022.75   1995.5   2296   3162.00   14256   2543879      143     4901      301     4694      22           2
## BS155      0    945.00   1766.0   2075   2883.50   11171   2299270     1910       23     4609       38      19           2
## BS157      0    560.00   1030.5   1149   1598.25    5380   1273053     2329      164     5606      234      24           2
## BS159      0    843.50   1504.0   1644   2296.25    7732   1821533       50     2235       21     2904      17           2
## BS161      0   1132.75   2071.5   2348   3258.50   15953   2601648      308     2657      158     3556      18           2
## BS167      0    458.75    854.0    965   1346.00    4894   1069280     4375       37     1811       50      24           2
## BS169      0   1029.50   1889.5   2066   2880.75    9532   2289655     5284      237     2258      344      24           2
## BS171      0    753.50   1449.0   1566   2194.50    6979   1735618       50     3088       59     4108      23           2
## BS173      0   1028.50   1987.5   2163   3013.25   14361   2397102      296     3721      342     5088      20           2

## 
## 
## Table: table2 stats summed essential tags BS8_14778_feb2018
## 
##          min         Q1    median    mean         Q3      max     libsum   perc05   perc10   perc90   perc95   zeros   percZeros
## ------  ----  ---------  --------  ------  ---------  -------  ---------  -------  -------  -------  -------  ------  ----------
## BS326      0   13104.00   24648.5   26962   38641.75   122538   29873810      226    39292    65173      723      37           3
## BS327      0   12102.75   21987.0   23450   33018.00   122119   25982389     2893      725    75955    13904      24           2
## BS328      0    7887.50   15865.0   17428   25096.00    97947   19310680    52498     3646      384    16377      42           4
## BS329      0   14675.00   31626.0   30800   43327.50   135358   34126201    58860    54899     2439      182      20           2
## BS332      0    9653.00   17741.5   18481   25954.00    91697   20477067      430    61301    23907     1042      19           2
## BS333      0   20406.75   36650.5   36758   51233.75   173910   40728133     2942      450    28175    20499      15           1
## BS334      0    7658.25   12971.0   13609   19330.75    43838   15078299    43297     2866      333    23709      15           1
## BS335      0    9437.50   19764.5   21429   32104.75    96180   23743673    51171    33701     2899      303      15           1
## BS336      0    3696.50    6879.0    7373   10503.25    34232    8169428       67    38443    40261     1749      21           2
## BS337      0    5117.25   10845.5   11061   15761.50    49807   12255895     1852      911    46399    32760      25           2
## BS338      0    6451.25   17531.0   17498   25685.75    66186   19387789    33856     5954      115    36991      27           2

## 
## 
## Table: table3 stats summed essential tags BS9_18674_feb2018
## 
##          min        Q1    median    mean         Q3     max     libsum   perc05   perc10   perc90   perc95   zeros   percZeros
## ------  ----  --------  --------  ------  ---------  ------  ---------  -------  -------  -------  -------  ------  ----------
## BS340      0   8093.25   14357.5   14759   20841.50   68092   16352479      404    27063      200     9601      12           1
## BS341      0   4525.50    8340.5    8575   11824.25   36493    9501415     2309    30417      919    10924      16           1
## BS342      0   6400.25   11874.0   12122   17127.00   44082   13430934    26428      230    13835      134      15           1
## BS343      0   4330.00    7543.5    7773   10864.75   28986    8612809    29874     1189    15958      716      15           1
## BS344      0   7954.25   14275.5   14836   21249.50   72612   16437909      312    13372      129    15032      16           1
## BS345      0   3740.75    6891.0    7298   10450.75   30678    8086020     1448    15208      591    17832      19           2
## BS346      0   5077.00    8651.5    8860   12309.25   34184    9816468    15526      425    11052      168      12           1
## BS347      0   3414.00    6063.5    6291    8853.50   24600    6970742    18491     1447    13065      708      15           1
## BS349      0   1725.75    3248.5    3435    4898.50   18064    3806424      472    15479       95     8517      20           2
## BS351      0   3612.75    7090.5    7390   10714.75   30514    8187727     2068    18307      427     9624      19           2
## BS353      0   2822.50    5494.0    5871    8409.50   32292    6505418    21762      228    10833       20      21           2
## BS355      0   2609.25    5369.5    5687    8271.25   30911    6301091    24872     1019    12730      112      18           2
## BS357      0   2004.50    3887.5    4148    6038.50   17819    4596235      273    11413       84     2900      18           2
## BS359      0   2564.50    4804.0    5086    7282.25   25263    5635547     1061    12844      441     3291      18           2
## BS361      0   3615.50    7542.5    7997   11590.00   36589    8861077    13952       77     7858       59      18           2
## BS363      0   2548.00    4577.5    4693    6675.75   18774    5200293    16017      394     9041      338      17           2
## BS365      0    527.50    1476.5    1501    2256.00    9738    1663042      458     6438      105     7195      29           3
## BS367      0   1687.75    3522.5    3713    5376.25   17011    4113614     2297     7300      519     8164      23           2

NONESSENTIAL

for (i in 1:nlen) {
  print(kable(mystats(xnon[[i]]),
              caption = paste0("table",i," stats summed nonessential tags ",nnam[i])))
  
  mybarplotnull(xnon[[i]],group = factor(lncolx[[i]]),col = mycols[i],
          
          nam=paste("nonessential",nnam[i]))
}

## 
## 
## Table: table1 stats summed nonessential tags BS5_7201_feb2018
## 
##          min       Q1   median   mean       Q3     max     libsum   perc05   perc10   perc90   perc95   zeros   percZeros
## ------  ----  -------  -------  -----  -------  ------  ---------  -------  -------  -------  -------  ------  ----------
## BS146      0   1319.5     3258   3627   5048.5   33729   18132687        0     5897     4625      204     291           6
## BS148      0    638.5     1641   1843   2580.0   17823    9215364       66        0     5702     1893     348           7
## BS149      0    549.5      982   1055   1453.0    5693    5274417     7287       21        1     2238     238           5
## BS150      0    133.0      407    488    703.0    5846    2437121     9078     2195      350        0     437           9
## BS151      0    254.0      447    477    656.5    2560    2384497        0     2728     4663       37     248           5
## BS152      0    580.5     1730   2058   2999.5   21111   10286336       29        2     5563     5035     337           7
## BS153      0   1240.5     2215   2499   3452.0   12958   12493639     3781      313        0     6309     233           5
## BS154      0    402.5      971   1079   1498.0   10200    5394318     4756     4144       14        4     296           6
## BS155      0   1200.0     2015   2216   3036.5   13466   11076807        1     4944     4279      411     234           5
## BS156      0    155.0      386    431    593.0    4005    2152546      167        0     5365     3927     404           8
## BS157      0    656.5     1115   1205   1652.0    7750    6024380     1954        6        1     4546     228           5
## BS158      0    458.0     1122   1260   1755.0   12291    6296869     2310      882      105        0     364           7
## BS159      0    988.0     1641   1776   2423.0    9094    8878159        0     1102      948       24     235           5
## BS160      0    832.5     2069   2275   3159.5   22685   11374095        3        2     1105     2700     320           6
## BS161      0   1318.5     2332   2515   3465.0   14130   12572260     1054      160        0     3319     237           5
## BS162      0    622.5     1812   2045   2924.0   22823   10222686     1307     2231       18        4     350           7
## BS163      0    274.0      488    517    705.0    2786    2584662        1     2628     6652      324     249           5
## BS164      0    972.5     2778   3217   4648.0   30751   16080019       78        0     8348     3088     313           6
## BS165      0    264.0      459    512    698.5    2721    2561219      891       20        0     3642     260           5
## BS166      0    693.5     1708   1857   2568.5   16311    9285242     1058     2570       90        0     383           8
## BS167      0    547.0      952   1027   1416.0    6327    5133731        0     3176      969       38     247           5
## BS168      0    914.5     2271   2487   3449.0   22556   12432919       11        3     1173     4903     354           7
## BS169      0   1158.5     2031   2151   2969.0   12971   10751019     4355      305        0     6064     225           5
## BS170      0    484.0     1236   1335   1846.0   12271    6671412     5404     3250       34        1     375           8
## BS171      0    900.0     1576   1681   2310.5    8620    8400995        3     3850     3735      443     237           5
## BS172      0    881.0     2221   2425   3365.5   23950   12122672      331        0     4610     4236     351           7
## BS173      0   1222.0     2156   2304   3146.5   13969   11519462     4806       39        1     5056     244           5

## 
## 
## Table: table2 stats summed nonessential tags BS6_7371_feb2018
## 
##          min       Q1   median   mean       Q3     max     libsum   perc05   perc10   perc90   perc95   zeros   percZeros
## ------  ----  -------  -------  -----  -------  ------  ---------  -------  -------  -------  -------  ------  ----------
## BS210      0    275.5      675    751   1043.0    6572    3753164        0        0        0        0     403           8
## BS211      0    272.0      639    703    966.5    7566    3514720       13       16       11       15     392           8
## BS212      0    224.0      537    589    816.0    5206    2946048     1520     1760     1286     1265     386           8
## BS213      0    296.0      704    775   1079.0    7521    3874784     1903     2262     1622     1601     380           8
## BS214      0    273.5      666    740   1029.5    6675    3699775        0        0        0        0     390           8
## BS215      0    263.0      620    689    948.0    6431    3442747       14       14       13       14     393           8
## BS216      0    332.5      803    889   1229.0    9029    4442461     1416     1874     1385     1141     387           8
## BS217      0    332.0      821    920   1277.5    8503    4600633     1758     2344     1762     1442     361           7
## BS218      0    318.0      757    843   1173.5    7893    4215738        0        0        0        0     394           8
## BS219      0    327.0      790    877   1214.0    8840    4382993       12       17       26       10     365           7
## BS220      0    262.0      672    742   1032.5    7068    3709990     1170     1699     1346     1858     404           8
## BS221      0    278.0      674    746   1027.0    7182    3727229     1470     2138     1698     2390     359           7
## BS222      0    224.0      569    637    900.0    5934    3183432        0        0        0        0     398           8
## BS223      0    259.0      631    694    969.0    7331    3467507       13       15       87       14     398           8
## BS224      0    303.0      626    687    937.0    6122    3432735     1548     1761     5464     1641     311           6
## BS225      0   1123.0     2534   2789   3836.5   26082   13941470     1905     2194     6950     2083     321           6
## BS226      0    372.5      820    898   1238.5    7778    4490712        0        0        0        0     354           7
## BS227      0    326.0      747    820   1128.5    7456    4101276       14       11       25       13     372           7
## BS228      0    252.0      587    643    891.0    5678    3214675     1488     1521     1776     1378     360           7
## BS229      0    227.0      514    569    781.0    4691    2846412     1883     1876     2241     1741     390           8
## BS368      0    300.5      799    908   1279.0    8910    4539171        0        0        0        0     398           8
## BS369      0    296.0      722    810   1125.5    7755    4049437       15       15       22       22     395           8
## BS370      0    261.5      619    687    959.0    6639    3433528     1372     1491     1644     2180     393           8
## BS372      0    398.5      953   1072   1467.0   10027    5359584     1728     1865     2033     2772     389           8

## 
## 
## Table: table3 stats summed nonessential tags BS8_14778_feb2018
## 
##          min   Q1   median   mean    Q3      max    libsum   perc05   perc10   perc90   perc95   zeros   percZeros
## ------  ----  ---  -------  -----  ----  -------  --------  -------  -------  -------  -------  ------  ----------
## BS326      0    0        0    375     0   110116   1876553        0        0        0        0    4481          90
## BS327      0    0        0    306     0    91485   1530156        0        0        0        0    4551          91
## BS328      0    0        0    871   927    67457   4351691        1     2713        2        1    3234          65
## BS329      0    0        0    384     0   149018   1921533        1     3872      304       21    3911          78
## BS332      0    0        0    240     0    83251   1197286        0        0        0        0    3987          80
## BS333      0    0        0    459     1   156694   2296849        0        0        0        0    3748          75
## BS335      0    0        0    239     0    83062   1192392        0      197      190        9    4247          85
## BS338      0    0        0    225     0    60379   1123263        1      320      338      261    4234          85

## 
## 
## Table: table4 stats summed nonessential tags BS9_18674_feb2018
## 
##          min       Q1   median   mean       Q3     max     libsum   perc05   perc10   perc90   perc95   zeros   percZeros
## ------  ----  -------  -------  -----  -------  ------  ---------  -------  -------  -------  -------  ------  ----------
## BS339      0   1162.5     2774   3051   4242.0   27978   15253388        0     4039     2302       25     371           7
## BS348      0    304.5      735    837   1178.5    8196    4182818       56        0     2906     4394     440           9
## BS350      0    475.0     1408   1574   2233.5   15160    7867578     6116       40        0     5468     385           8
## BS352      0    910.0     2729   3185   4584.5   29925   15922881     7698     6648       14        0     368           7
## BS354      0    603.5     1616   1858   2627.0   20757    9288053        0     8292     1963        8     385           8
## BS356      0    357.0      953   1102   1564.0   13467    5510482       16        0     2414     1565     380           8
## BS358      0    313.5      819    934   1328.0   10587    4671403     1693       30        0     1947     409           8
## BS360      0    495.5     1311   1518   2130.5   19018    7588753     2116     3838       23        0     382           8
## BS362      0    629.0     1771   2074   2969.0   23996   10367561        0     4775     3176       13     379           8
## BS364      0    202.0      595    722   1034.0    7502    3611423       20        0     3953     1778     397           8
## BS366      0    269.0      732    853   1196.5   10805    4264054     3246       16        0     2239     393           8

density plots before and after normalization

mynormdensity(do.call(cbind,xess),group = ecolx,filename = "Figs/ess_norm_dens.png")

## png 
##   2

mynormdensity(do.call(cbind,xnon),group = ncolx,filename = "Figs/non_norm_dens.png")

## png 
##   2

Figure 1a: Density distribution of essential raw and normalized counts.

Figure 1b: Density distribution of nonessential raw and normalized counts.

Visualization of first principal components of the PCA. The first principal component (PC1) is expected to separate samples by biological or technical variability is the main source of variance in the data. Here we see grouping by barseq run, though the sample have not been rigorously normalized together nor has any low count threshold been applied

myPCAPlot(do.call(cbind,xess),group = ecolx,filename = "Figs/ess_PCA.png")

## png 
##   2

myPCAPlot(do.call(cbind,xnon),group = ncolx,filename = "Figs/non_PCA.png")

## png 
##   2

Figure 2a: PCA essential with percentages of variance associated with each axis.

Figure 2b: PCA nonessential

plot percentage of null counts essential vs nonessential

summarize and write data to file

eraw = lapply(tmp,function(x) x = mystats(x[ess$strain,]))
nraw = lapply(tmp,function(x) x = mystats(x[noness$strain,]))

eraw = do.call(rbind,eraw,quote  = F)
nraw= do.call(rbind,nraw,quote  = F)


rownames(eraw ) = paste0('ess:',rownames(eraw ))
rownames(nraw ) = paste0('noness:',rownames(nraw ))

eraw$pool = 'essential'
nraw$pool = 'nonessential'

stats = rbind(eraw,nraw)

xdf = data.frame(file = names(ucoln),colnam = ucoln,essCPM = coless,nonCPM = colnon,stringsAsFactors = F)

tally = t(sapply(tmp,tally_CPM))
write.table(xdf,"feb9_masterfile_descrepancy.txt",sep = '\t')
write.table(stats,"feb9_stats_master_count_matrices.txt",sep = '\t')

kable(xdf,caption = 'summary of total counts')

summary of total counts

file	colnam	essCPM	nonCPM
BS5_7201_feb2018	BS138	140	482298
BS5_7201_feb2018	BS139	82851	401653
BS5_7201_feb2018	BS140	200	599059
BS5_7201_feb2018	BS141	26886	130819
BS5_7201_feb2018	BS142	66	182546
BS5_7201_feb2018	BS143	11919	56071
BS5_7201_feb2018	BS144	80	268677
BS5_7201_feb2018	BS145	68693	320940
BS5_7201_feb2018	BS146	5613	18132687
BS5_7201_feb2018	BS147	432	3065
BS5_7201_feb2018	BS148	3008	9215364
BS5_7201_feb2018	BS149	1097778	5274417
BS5_7201_feb2018	BS150	551	2437121
BS5_7201_feb2018	BS151	493562	2384497
BS5_7201_feb2018	BS152	2692	10286336
BS5_7201_feb2018	BS153	2543879	12493639
BS5_7201_feb2018	BS154	2316	5394318
BS5_7201_feb2018	BS155	2299270	11076807
BS5_7201_feb2018	BS156	724	2152546
BS5_7201_feb2018	BS157	1273053	6024380
BS5_7201_feb2018	BS158	1741	6296869
BS5_7201_feb2018	BS159	1821533	8878159
BS5_7201_feb2018	BS160	3634	11374095
BS5_7201_feb2018	BS161	2601648	12572260
BS5_7201_feb2018	BS162	2984	10222686
BS5_7201_feb2018	BS163	533741	2584662
BS5_7201_feb2018	BS164	5009	16080019
BS5_7201_feb2018	BS165	523348	2561219
BS5_7201_feb2018	BS166	2881	9285242
BS5_7201_feb2018	BS167	1069280	5133731
BS5_7201_feb2018	BS168	4475	12432919
BS5_7201_feb2018	BS169	2289655	10751019
BS5_7201_feb2018	BS170	1997	6671412
BS5_7201_feb2018	BS171	1735618	8400995
BS5_7201_feb2018	BS172	3953	12122672
BS5_7201_feb2018	BS173	2397102	11519462
BS6_7371_feb2018	BS210	1192	3753164
BS6_7371_feb2018	BS211	1097	3514720
BS6_7371_feb2018	BS212	1061	2946048
BS6_7371_feb2018	BS213	1441	3874784
BS6_7371_feb2018	BS214	1372	3699775
BS6_7371_feb2018	BS215	1529	3442747
BS6_7371_feb2018	BS216	1617	4442461
BS6_7371_feb2018	BS217	1684	4600633
BS6_7371_feb2018	BS218	1296	4215738
BS6_7371_feb2018	BS219	1489	4382993
BS6_7371_feb2018	BS220	1122	3709990
BS6_7371_feb2018	BS221	3216	3727229
BS6_7371_feb2018	BS222	909	3183432
BS6_7371_feb2018	BS223	1313	3467507
BS6_7371_feb2018	BS224	3557	3432735
BS6_7371_feb2018	BS225	6197	13941470
BS6_7371_feb2018	BS226	1825	4490712
BS6_7371_feb2018	BS227	1642	4101276
BS6_7371_feb2018	BS228	1367	3214675
BS6_7371_feb2018	BS229	1170	2846412
BS6_7371_feb2018	BS368	1439	4539171
BS6_7371_feb2018	BS369	1288	4049437
BS6_7371_feb2018	BS370	1264	3433528
BS6_7371_feb2018	BS371	0	299
BS6_7371_feb2018	BS372	1336	5359584
BS8_14778_feb2018	BS326	29873810	1876553
BS8_14778_feb2018	BS327	25982389	1530156
BS8_14778_feb2018	BS328	19310680	4351691
BS8_14778_feb2018	BS329	34126201	1921533
BS8_14778_feb2018	BS332	20477067	1197286
BS8_14778_feb2018	BS333	40728133	2296849
BS8_14778_feb2018	BS334	15078299	854108
BS8_14778_feb2018	BS335	23743673	1192392
BS8_14778_feb2018	BS336	8169428	419948
BS8_14778_feb2018	BS337	12255895	955334
BS8_14778_feb2018	BS338	19387789	1123263
BS9_18674_feb2018	BS339	9797	15253388
BS9_18674_feb2018	BS340	16352479	891590
BS9_18674_feb2018	BS341	9501415	546221
BS9_18674_feb2018	BS342	13430934	813487
BS9_18674_feb2018	BS343	8612809	489015
BS9_18674_feb2018	BS344	16437909	934784
BS9_18674_feb2018	BS345	8086020	500459
BS9_18674_feb2018	BS346	9816468	618775
BS9_18674_feb2018	BS347	6970742	750223
BS9_18674_feb2018	BS348	60525	4182818
BS9_18674_feb2018	BS349	3806424	270977
BS9_18674_feb2018	BS350	8223	7867578
BS9_18674_feb2018	BS351	8187727	459640
BS9_18674_feb2018	BS352	23399	15922881
BS9_18674_feb2018	BS353	6505418	370154
BS9_18674_feb2018	BS354	21268	9288053
BS9_18674_feb2018	BS355	6301091	365261
BS9_18674_feb2018	BS356	8891	5510482
BS9_18674_feb2018	BS357	4596235	253941
BS9_18674_feb2018	BS358	6736	4671403
BS9_18674_feb2018	BS359	5635547	363351
BS9_18674_feb2018	BS360	14721	7588753
BS9_18674_feb2018	BS361	8861077	506612
BS9_18674_feb2018	BS362	14449	10367561
BS9_18674_feb2018	BS363	5200293	300038
BS9_18674_feb2018	BS364	4140	3611423
BS9_18674_feb2018	BS365	1663042	94087
BS9_18674_feb2018	BS366	10707	4264054
BS9_18674_feb2018	BS367	4113614	241053

summary of counts per barseq run; should reflect pool designated in master file

annotated experiments in masterfile is currently uncertain as ~30% of the sample ids are duplicated

kable(tally)

	no_samples	pass_essential	pass_nonessential	pass_both
BS5_7201_feb2018	36	13	27	13
BS6_7371_feb2018	25	0	24	0
BS8_14778_feb2018	11	11	8	8
BS9_18674_feb2018	29	18	11	0

ovarall summary of samples passing count threshold