This page shows where to retrieve the data files containing the
measurement of DNA concentration in the 96-well plates after collection of the
cDNAs from the capture array. It then presents some commands to
import the data in R, and uses them to produce a consolidated
table for the five runs, that is saved for
integrated analysis later. Finally, some quality controls are run,
showing strong variation between runs for the DNA yield after the on-chip PCR.
An inspection of the standard curves rules out that it
could be a simple problem of measurement.
After a C1 run, the products are recovered from the outlets and transferred to a 96-well plate. Following the standard procedure (PN 100-5950 A1, page 25), 2 μL are quantified on a 384-well fluorometer using the PicoGreen dye. See also PicoGreen Quantitation of DNA: Effective Evaluation of Samples Pre-or Post-PCR. Susan J. Ahn, José Costa and Janet Rettig Emanuel, Nucl. Acids Res. (1996) 24(13):2623-2625.
The measured fluorescence intensities are transferred in an Excel sheet provided by Fluidigm (PN 100-6160), which converts them to DNA concentrations by linear regression to a standard curve.
The R commands here load the Excel sheets and produce one consolidated table
for all runs, on which a few quality controls are run.
Each file is named following the serial ID of the C1 chip from which DNA was collected.
The files are available from GitHub and will be deposited in a proper place later. The files need to be downloaded before running this knitr script.
shasum -c << __CHECKSUMS__
6d58020277bd2eb42f5702eabe17194671d9e87f 1772-062-248.picogreen.xlsx
83844dd8e077d54a619e6bff396992b23d8a26e8 1772-062-249.picogreen.xlsx
656fb4bd93466cd4f005881a046f3f17c17f4a78 1772-064-103.picogreen.xlsx
f4e2064d7c09826a8ad14ed2a95a66255018da39 1772-067-038.picogreen.xlsx
c9a1ece68f037589d40b382f59d202dbb90425de 1772-067-039.picogreen.xlsx
__CHECKSUMS__## 1772-062-248.picogreen.xlsx: OK
## 1772-062-249.picogreen.xlsx: OK
## 1772-064-103.picogreen.xlsx: OK
## 1772-067-038.picogreen.xlsx: OK
## 1772-067-039.picogreen.xlsx: OK
library(gdata)
library(ggplot2)
library(reshape)These files in Excel format contain the final result in their third sheet, from line 42 to 49.
First, extract the concentrations as a 8 × 12 table. An example is shown for
run 1772-062-248.
readConcentrationTable <- function (FILE) {
picogreen <- read.xls( FILE
, sheet = 3
, skip = 41
, nrow = 8
, head = FALSE
, blank.lines.skip = FALSE )[,1:13]
colnames(picogreen) <- c('Row', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12')
picogreen
}
readConcentrationTable('1772-062-248.picogreen.xlsx')## Row 01 02 03 04 05 06 07 08 09 10 11 12
## 1 A 3.743 0.173 2.832 3.739 4.200 4.485 2.444 0.091 4.404 0.046 1.672 4.781
## 2 B 3.099 3.935 3.927 2.192 3.958 0.101 0.243 2.229 0.099 4.557 3.287 4.028
## 3 C 4.190 0.095 2.545 4.009 3.756 3.064 3.155 0.116 4.217 2.505 0.138 3.847
## 4 D 4.760 4.299 3.198 3.174 3.756 2.657 0.108 3.170 4.209 3.512 0.116 2.908
## 5 E 4.742 3.243 2.339 0.120 2.254 3.498 3.248 3.178 3.168 4.259 3.808 3.854
## 6 F 4.244 3.083 3.152 0.122 2.352 3.339 3.384 3.242 3.475 2.524 2.958 2.430
## 7 G 3.408 3.632 2.969 3.526 2.978 3.613 2.921 3.599 3.392 3.799 2.321 3.361
## 8 H 3.370 3.506 3.152 3.402 3.604 0.115 3.995 4.319 3.568 3.079 2.375 3.622
Then, transform this table to have one measurement by line, with the run ID, and the row and column name. This is a typical format when plotting data with ggplot2.
meltConcentrationTable <- function (RUN, TABLE) {
picogreen <- melt(TABLE, id.vars='Row')
colnames(picogreen) <- c('Row', 'Column', 'Concentration')
picogreen[,"Run"] <- RUN
picogreen[,"Well"] <- paste(picogreen$Row, picogreen$Column, sep='')
picogreen <- picogreen[, c('Run', 'Well', 'Row', 'Column', 'Concentration')]
picogreen
}The function below outputs the data for one run, provided that a properly named
file (run ID plus picogreen.xlsx) is available in the same directory.
read_pg <- function(RUN) {
FILE <- paste(RUN, 'picogreen.xlsx', sep='.')
picogreen <- readConcentrationTable(FILE)
picogreen <- meltConcentrationTable(RUN, picogreen)
picogreen
}
head(read_pg('1772-062-248'))## Run Well Row Column Concentration
## 1 1772-062-248 A01 A 01 3.743
## 2 1772-062-248 B01 B 01 3.099
## 3 1772-062-248 C01 C 01 4.190
## 4 1772-062-248 D01 D 01 4.760
## 5 1772-062-248 E01 E 01 4.742
## 6 1772-062-248 F01 F 01 4.244
The standard curve is also in sheet 3, from row 2 to 12. Here is a function to get the standard curve from one run (with background correction already applied). The output is self-explanatory.
read_sc <- function(RUN) {
FILE <- paste(RUN, "picogreen.xlsx", sep = ".")
sc <- read.xls(FILE, sheet=3, skip=2, nrow=10, header=FALSE)[,c(2,5)]
sc <- cbind(RUN, sc)
colnames(sc) <- c('Run', 'dna', 'fluorescence')
sc$fluorescence <- as.numeric(as.character(sc$fluorescence))
return(sc)
}
read_sc('1772-062-248')## Run dna fluorescence
## 1 1772-062-248 2.00000000 560415.0
## 2 1772-062-248 1.00000000 332109.0
## 3 1772-062-248 0.50000000 144873.0
## 4 1772-062-248 0.25000000 80629.5
## 5 1772-062-248 0.12500000 38842.0
## 6 1772-062-248 0.06250000 19577.0
## 7 1772-062-248 0.03125000 9952.0
## 8 1772-062-248 0.01562500 4536.5
## 9 1772-062-248 0.00781250 2437.0
## 10 1772-062-248 0.00390625 1204.0
The file cDNA_concentration.csv is made from the files above using R, and has
the following columns.
The serial ID of the C1 capture array for a given run. Example: 1772-062-248.
The coordinates in the 96-well plate where the cDNAs have been transferred at
the end of the C1 run. Examples: A01, F08, C12, etc. Combined with the
run ID, this uniquely identifies a cell.
The row coordinates in the 96-well plate where the cDNAs have been transferred
at the end of the run. Possible values: A, B, C, D, E, F, G and
H.
The column coordinates in the 96-well plate where the cDNAs have been
transferred at the end of the run. Possible values: 01, 02, 03, 04,
05, 06, 07, 08, 09, 10, 11 and 12.
The DNA concentration in ng/μL.
Each Excel sheet is loaded in R, the DNA concentrations are extracted, and
added to a table saved under the name cDNA_concentration.csv.
The cDNA_concentration.csv file contains the data for the following runs.
RUNS <- c('1772-062-248', '1772-062-249', '1772-064-103', '1772-067-038', '1772-067-039')Create a picogreen table for the first run being processed, append the other
runs, and save the file.
for (RUN in RUNS) {
if (! exists('picogreen'))
{picogreen <- read_pg(RUN)}
else
{picogreen <- rbind(picogreen, read_pg(RUN))}
}
summary(picogreen)## Run Well Row Column Concentration
## Length:480 Length:480 A : 60 01 : 40 Min. :0.0040
## Class :character Class :character B : 60 02 : 40 1st Qu.:0.3125
## Mode :character Mode :character C : 60 03 : 40 Median :1.0120
## D : 60 04 : 40 Mean :1.4048
## E : 60 05 : 40 3rd Qu.:2.3750
## F : 60 06 : 40 Max. :4.7810
## (Other):120 (Other):240
write.csv(file='cDNA_concentration.csv', picogreen, row.names=FALSE)There is a strong variation between runs.
qplot( data=picogreen
, Run
, Concentration
, geom="boxplot"
# , ylab="cDNA concentration (ng/\u03BCL)"
, fill=Run) +
coord_flip() + theme_bw() +
theme(axis.title = element_text(size=14, family="Helvetica"),
axis.text = element_text(size=8, family="Helvetica"),
legend.title = element_text(size=12, family="Helvetica")) +
scale_x_discrete(limits=rev(levels(as.factor(picogreen$Run))))
Still, for most runs except 1772-064-103, it is possible to detect low-concentration outliers, were there probably was no cell in the chamber. Note that the scale of each histogram is different.
qplot( data=picogreen
, Concentration
, geom="histogram"
# , xlab="Concentration (ng/\u03BCL)"
, colour=Run) +
facet_wrap( ~Run, scales='free')
Load the data from all runs.
for (RUN in RUNS) {
if (!exists("sc")) {
sc <- read_sc(RUN)
} else {
sc <- rbind(sc, read_sc(RUN))
}
}While the DNA concentrations in 1772-062-248 might have been overestimated
as suggested by the shifted standard curve, overall the calibration of the
fluorometer is stable and does not explain the strong variations of the DNA
yield.
ggplot(
sc,
aes(
x=fluorescence,
y=dna,
colour=Run)
) + geom_point() +
geom_line() +
scale_x_log10('Average fluorescence (background subtracted)') +
scale_y_log10('DNA concentration (ng/\u03BCL)')
sessionInfo()## R version 3.1.0 (2014-04-10)
## Platform: x86_64-apple-darwin13.1.0 (64-bit)
##
## locale:
## [1] C
##
## attached base packages:
## [1] methods stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] reshape_0.8.5 ggplot2_1.0.0 gdata_2.13.3
##
## loaded via a namespace (and not attached):
## [1] MASS_7.3-35 Rcpp_0.11.3 colorspace_1.2-4 digest_0.6.6 evaluate_0.5.5 formatR_1.0
## [7] grid_3.1.0 gtable_0.1.2 gtools_3.4.1 knitr_1.8 labeling_0.3 munsell_0.4.2
## [13] plyr_1.8.1 proto_0.3-10 reshape2_1.4.1 scales_0.2.4 stringr_0.6.2 tools_3.1.0