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WeiLei-TsinghuaWeiLei-Tsinghua
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‎flow_cytometry/analysis.r‎

Lines changed: 15 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -1,10 +1,10 @@
11
library(ggplot2)
22

33
# preset parameters
4-
# threshold of mKate positive cells
5-
min_mKate <- 10^1.8
6-
# range for mKate
7-
mKate_bin <- 10^seq(1.8, 4.8, 0.2)
4+
# threshold of mKate2 positive cells
5+
min_mKate2 <- 10^1.8
6+
# range for mKate2
7+
mKate2_bin <- 10^seq(1.8, 4.8, 0.2)
88
# sampling numbers for noise calculation
99
sample.num <- 100
1010
# threshold for outlier removal
@@ -19,13 +19,13 @@ d.case$miRNA <- "miRNA"
1919
d.ctrl$miRNA <- "ctrl"
2020

2121
d <- rbind(d.case, d.ctrl)
22-
d <- d[d$EYFP > 0 & d$mKate > min_mKate, ]
22+
d <- d[d$EYFP > 0 & d$mKate2 > min_mKate2, ]
2323

2424
# function to calculate noise
2525
CalculateNoise <- function(d){
26-
bin.num <- length(mKate_bin) - 1
27-
mKate_mean <- c()
28-
mKate_sd <- c()
26+
bin.num <- length(mKate2_bin) - 1
27+
mKate2_mean <- c()
28+
mKate2_sd <- c()
2929
EYFP_mean <- c()
3030
EYFP_sd <- c()
3131
EYFP_CV <- c()
@@ -34,34 +34,34 @@ CalculateNoise <- function(d){
3434

3535
# get noise
3636
for(i in 1:bin.num){
37-
this_data <- d[d$mKate > mKate_bin[i] & d$mKate < mKate_bin[i + 1], ]
37+
this_data <- d[d$mKate2 > mKate2_bin[i] & d$mKate2 < mKate2_bin[i + 1], ]
3838
this_data <- this_data[order(this_data$EYFP), ]
3939
# filter out outliers
4040
cut_range_low <- outlier.cut * nrow(this_data)
4141
cut_range_high <- (1 - outlier.cut) * nrow(this_data)
4242
this_data <- this_data[cut_range_low:cut_range_high, ]
4343

44-
mKate_mean_temp <- rep(NA, times = sample.num)
44+
mKate2_mean_temp <- rep(NA, times = sample.num)
4545
EYFP_mean_temp <- rep(NA, times = sample.num)
4646
EYFP_CV_temp <- rep(NA, times = sample.num)
4747

4848
for (j in 1:sample.num){
4949
bin_sample_temp <- this_data[sample(1:nrow(this_data), ceiling(nrow(this_data)/2)),]
50-
mKate_mean_temp[j] <- mean(bin_sample_temp$mKate)
50+
mKate2_mean_temp[j] <- mean(bin_sample_temp$mKate2)
5151
EYFP_mean_temp[j] <- mean(bin_sample_temp$EYFP)
5252
EYFP_CV_temp[j] <- sd(bin_sample_temp$EYFP) / mean(bin_sample_temp$EYFP)
5353
}
5454

55-
mKate_mean[i] <- mean(mKate_mean_temp)
56-
mKate_sd[i] <- sd(mKate_mean_temp)
55+
mKate2_mean[i] <- mean(mKate2_mean_temp)
56+
mKate2_sd[i] <- sd(mKate2_mean_temp)
5757
EYFP_mean[i] <- mean(EYFP_mean_temp)
5858
EYFP_sd[i] <- sd(EYFP_mean_temp)
5959
EYFP_CV[i] <- mean(EYFP_CV_temp)
6060
EYFP_CVsd[i] <- sd(EYFP_CV_temp)
6161
cell_count[i] <- nrow(this_data)
6262
}
6363

64-
this_data <- data.frame(mKate_mean = mKate_mean, mKate_sd = mKate_sd,
64+
this_data <- data.frame(mKate2_mean = mKate2_mean, mKate2_sd = mKate2_sd,
6565
EYFP_mean = EYFP_mean, EYFP_sd = EYFP_sd,
6666
EYFP_CV = EYFP_CV, EYFP_CVsd = EYFP_CVsd,
6767
cell_count = cell_count)
@@ -86,7 +86,7 @@ write.table(d.result, "result/result.txt", sep = "\t", quote = F, row.names = F,
8686

8787
# visualization
8888
pdf("result/mean.pdf", width = 4, height = 3)
89-
p <- ggplot(d.result, aes(x = log10(mKate_mean), y = log10(EYFP_mean),
89+
p <- ggplot(d.result, aes(x = log10(mKate2_mean), y = log10(EYFP_mean),
9090
ymax = log10(EYFP_mean + EYFP_sd), ymin = log10(EYFP_mean - EYFP_sd)))
9191
p <- p + geom_point(aes(color = miRNA)) + geom_line(aes(color = miRNA))
9292
p <- p + theme_bw()

‎flow_cytometry/data/case.txt‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
EYFP mKate
1+
EYFP mKate2
22
4.25 15.39
33
99.450005 40.5
44
4.25 13.77

‎flow_cytometry/data/ctrl.txt‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
EYFP mKate
1+
EYFP mKate2
22
27863 4667.220215
33
80191.55469 25444.5293
44
153.850006 31.59

‎flow_cytometry/result/CV.pdf‎

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‎flow_cytometry/result/mean.pdf‎

-3 Bytes
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‎flow_cytometry/result/result.txt‎

Lines changed: 31 additions & 31 deletions
Original file line numberDiff line numberDiff line change
@@ -1,31 +1,31 @@
1-
mKate_mean mKate_sd EYFP_mean EYFP_sd EYFP_CV EYFP_CVsd cell_count miRNA
2-
79.9634800300833 0.25969688696582 184.256134085238 2.3719276083127 0.520953375627805 0.00713160285732154 1679 miRNA
3-
127.19123044959 0.414518236765515 273.222886176645 3.27380891938846 0.46499808714287 0.00694499417485264 1561 miRNA
4-
202.429649936908 0.659098139230777 369.412731675625 4.63131490773689 0.447672472115947 0.00639315875964887 1824 miRNA
5-
321.957668641893 1.03075890505942 504.846627230263 4.98869810581723 0.449823947959455 0.00568673076509625 1902 miRNA
6-
508.580630531852 1.24339003846424 696.472187407502 6.49122816746213 0.416699992461318 0.00531409240035522 2290 miRNA
7-
803.457087297453 2.12153061259661 960.017853306717 7.54562897386048 0.407881478653695 0.00472771085905441 2638 miRNA
8-
1281.2322409603 2.72803098399684 1350.75600955173 10.0479765952505 0.386860448896969 0.00433641103250396 2790 miRNA
9-
2013.43649343441 4.79883203541259 1881.48436920164 13.232154168205 0.369171611501547 0.00402262303292208 3032 miRNA
10-
3185.41558385067 8.26451575469749 2765.06463632545 20.7997098457374 0.392201446218978 0.0049644167159322 2880 miRNA
11-
5032.27318351961 11.9928340969002 3958.12700677881 24.9991766623024 0.383285266938212 0.00394298593662609 2893 miRNA
12-
7980.15062087709 19.461897547791 5802.09628870198 41.6445121264068 0.375408683254784 0.00474825274944306 2504 miRNA
13-
12601.021055089 37.5576636080905 8352.65057027576 59.704920735244 0.385656495445576 0.00601682636701808 2114 miRNA
14-
19977.9927252088 65.0611666698694 12953.2717208937 127.157412878097 0.409174812575195 0.00598618278949601 1591 miRNA
15-
31296.0091977024 138.82790564693 18403.0167873906 255.513236314731 0.40999361208174 0.00913544109953388 991 miRNA
16-
48862.3587971221 300.657729230714 28920.9571566477 467.628298177187 0.377193886524096 0.00974761286023157 515 miRNA
17-
79.1775974253961 0.288182329624648 986.545628395889 12.3470864868365 0.523693168136245 0.00921358630848748 1338 ctrl
18-
125.610320362198 0.535170701237475 1447.99344155753 17.5218622981079 0.415008405950637 0.0074676382680393 1091 ctrl
19-
201.741457760719 0.836383368442088 1953.62506219978 21.8374335961711 0.383883888051625 0.00603858505487138 1168 ctrl
20-
320.809565292446 1.26653362974178 2642.95862870587 25.419088863 0.347501604572368 0.00604171136963341 1210 ctrl
21-
511.40845020804 1.8838163329171 3695.24267648552 30.7546719275348 0.327198647097995 0.00495903804747395 1398 ctrl
22-
805.86428714375 2.61280962966928 5198.4519396387 44.5711945285286 0.303992667292617 0.00486712943903828 1631 ctrl
23-
1282.56563636618 4.11093102046002 7135.50597554228 49.0676165017179 0.293484313507402 0.00357124819710075 1917 ctrl
24-
2033.04451969069 5.38822843944051 10201.9935933859 52.0058588597026 0.290288457045043 0.00382014224386738 2215 ctrl
25-
3215.54447825055 8.94348285953018 14827.7141381982 93.7598180470102 0.272124769201791 0.00322347312905082 2434 ctrl
26-
5104.25722961498 13.785616866259 21288.758828783 117.660916415383 0.266487134974513 0.00367241598873578 2678 ctrl
27-
8063.25662566133 20.0226096197607 30366.9385159472 140.556878180735 0.252927097321036 0.00283854192613378 2998 ctrl
28-
12762.1510001048 28.3537365985509 44034.0436209503 181.146517668211 0.234713866395335 0.00221533684941516 3254 ctrl
29-
20045.1372432969 55.6100650201513 61070.392352273 231.744808196305 0.215560385355298 0.00235548101472832 3154 ctrl
30-
31447.6122883374 84.89511554568 83838.3825228814 348.836820962109 0.193134970909446 0.00234044761767828 2404 ctrl
31-
48773.2986776375 181.649789239773 112661.370922771 490.196487744286 0.179841328575033 0.00285615287098863 1306 ctrl
1+
mKate2_mean mKate2_sd EYFP_mean EYFP_sd EYFP_CV EYFP_CVsd cell_count miRNA
2+
79.9476946788929 0.246315735462628 184.861779339417 2.53225192402416 0.51858112033612 0.00670887178468535 1679 miRNA
3+
127.235993070743 0.437488927930476 272.417889381805 3.05761940329889 0.463013979587739 0.00707540745913834 1561 miRNA
4+
202.521423286491 0.681150437466004 370.199559534386 4.0616284246877 0.44853869220286 0.00595668701977389 1824 miRNA
5+
321.617937087108 0.992836112016952 505.262599984932 5.71018337579606 0.449183185978108 0.00546320283998141 1902 miRNA
6+
508.812283528646 1.38663431422514 695.512051205624 6.50021118958191 0.417275785835846 0.00493742799910122 2290 miRNA
7+
803.260888058658 2.30733065285247 960.435629391751 6.67134161767128 0.408251147770472 0.004816003683434 2638 miRNA
8+
1281.4534261839 2.89832521553664 1351.75539600579 9.06888081138134 0.386440922471472 0.00432742122362602 2790 miRNA
9+
2013.41611536412 4.88443602909036 1881.65574839189 13.9709930715029 0.369531335800365 0.0039899614317989 3032 miRNA
10+
3187.25542015981 6.40820437422428 2766.81342286037 18.1720996549701 0.392305307350201 0.00454661584259018 2880 miRNA
11+
5031.54725682718 11.4907717411321 3951.76179265393 27.1987210966229 0.383499312534209 0.00467718486623839 2893 miRNA
12+
7980.82222322923 22.4278759884303 5808.86471200601 44.0061362373856 0.376068058356348 0.00441599402937326 2504 miRNA
13+
12598.5195889527 31.3965737539548 8362.89776707495 76.7834216504706 0.386057425776612 0.00518019199418706 2114 miRNA
14+
19978.4234130613 65.0102649357137 12944.7776461226 135.546570607861 0.408261120025107 0.00643904304316192 1591 miRNA
15+
31305.7160657278 149.139227058819 18419.7708892455 254.037443214398 0.408928879841955 0.00955587465590005 991 miRNA
16+
48827.4406291229 327.22212928407 28918.0086496128 482.536373195976 0.374991849344747 0.00965083591574873 515 miRNA
17+
79.1885790434529 0.28888278466604 985.11593856994 15.9166688777247 0.522430935987765 0.0107613323399241 1338 ctrl
18+
125.672657735293 0.49090564366015 1445.25359242505 16.830951797539 0.415994569222277 0.00794660483480986 1091 ctrl
19+
201.715937194812 0.796219245411641 1957.53328682221 20.7110239373514 0.384755025226292 0.00707980747980847 1168 ctrl
20+
320.622729752463 1.09778857449948 2646.01298079164 27.077706588342 0.34689747553247 0.0055498258920734 1210 ctrl
21+
511.423004654406 1.71783638837783 3691.37472614991 29.3762283050652 0.327088603592837 0.00569111699632952 1398 ctrl
22+
806.018326179865 2.60393033052217 5198.33930499866 34.724102775349 0.30394409703204 0.00418533983358409 1631 ctrl
23+
1281.78937083469 3.41462148931378 7134.63509344423 42.6730075562727 0.293153373646924 0.00365687817716539 1917 ctrl
24+
2033.26411868205 5.48159636192312 10197.5501728342 57.7441046985114 0.290773647364366 0.0034733325563172 2215 ctrl
25+
3213.97645963758 7.65708564349792 14806.7924600547 84.0409335791608 0.271764308805592 0.00332615076821356 2434 ctrl
26+
5104.7177079231 12.7363871773604 21297.9167303198 109.791296658186 0.266534412165733 0.00330577561117381 2678 ctrl
27+
8063.68126716624 21.0391606047894 30372.1770903462 137.887018484087 0.253154965832851 0.00301330831082442 2998 ctrl
28+
12757.5612731095 27.0519367706542 44029.7612563315 181.420233905853 0.235033200081101 0.00247764927958956 3254 ctrl
29+
20035.7396875545 44.194279119513 61048.8898966088 241.783104913589 0.215666708079035 0.00188322712636069 3154 ctrl
30+
31446.7784622389 81.472086368373 83759.9443655279 317.616264525002 0.193328524965105 0.00238744436757967 2404 ctrl
31+
48731.4863913534 160.972629859108 112607.606182302 528.107834413398 0.1803514283773 0.00315807245574622 1306 ctrl

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