lgg-1 imaging - summer 2022
ChanJ
7/7/2022
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Get summary data. First, let’s look at the interval between puncta (LGG dots). To get interval, we calculated the pixel length/(number of puncta - 1) All data with 1 puncta were ignored for interval analysis
library(Rmisc)## Loading required package: lattice## Loading required package: plyr## Warning: package 'plyr' was built under R version 4.1.2summarySE(lgg,
measurevar="interval",
groupvars=c("RNAi", "age"))## RNAi age N interval sd se ci
## 1 asm-3 1 10 353.9917 305.64295 96.652787 218.64379
## 2 asm-3 5 10 171.6290 64.25727 20.319934 45.96688
## 3 elo-6 1 9 NA NA NA NA
## 4 elo-6 5 10 189.2244 91.67687 28.990772 65.58168
## 5 fat-2 1 10 229.0533 120.03914 37.959709 85.87083
## 6 fat-2 5 10 159.0638 61.95524 19.591969 44.32011
## 7 fat-3 1 10 232.1400 76.85411 24.303403 54.97812
## 8 fat-3 5 10 147.4707 41.34635 13.074863 29.57740
## 9 fat-4 1 10 NA NA NA NA
## 10 fat-4 5 10 189.8639 85.12865 26.920044 60.89737
## 11 fat-5 1 10 NA NA NA NA
## 12 fat-5 5 10 181.1000 92.55044 29.267017 66.20659
## 13 fat-7 1 10 263.1883 119.63295 37.831259 85.58025
## 14 fat-7 5 10 192.4150 76.43353 24.170404 54.67725
## 15 hyl-1 1 10 NA NA NA NA
## 16 hyl-1 5 10 153.8200 54.06426 17.096621 38.67524
## 17 hyl-2 1 10 278.4917 142.54059 45.075291 101.96739
## 18 hyl-2 5 10 167.3774 52.60853 16.636277 37.63387
## 19 L4440 1 19 225.0456 117.26331 26.902048 56.51911
## 20 L4440 5 20 156.7394 39.20353 8.766175 18.34782
## 21 lagr-1 1 10 250.8833 105.11097 33.239006 75.19186
## 22 lagr-1 5 10 191.9400 65.08563 20.581882 46.55945
## 23 sms-1 1 8 NA NA NA NA
## 24 sms-1 5 10 191.7350 75.30429 23.813309 53.86945
## 25 sphk-1 1 10 NA NA NA NA
## 26 sphk-1 5 10 182.3750 68.09003 21.531957 48.70867Now, let’s get summary by size of the puncta. Size is by worm. Thus, we took the average pixels size, got an average per worm, and then used that number to get a average per group. Those with 1 puncta were included in this analysis.
library(Rmisc)
summarySE(lgg,
measurevar="size",
groupvars=c("RNAi", "age"))## RNAi age N size sd se ci
## 1 asm-3 1 10 8.196667 2.0614450 0.6518862 1.4746689
## 2 asm-3 5 10 13.540278 1.8887061 0.5972613 1.3510989
## 3 elo-6 1 9 6.868519 1.6662592 0.5554197 1.2808002
## 4 elo-6 5 10 11.992619 2.4538631 0.7759797 1.7553879
## 5 fat-2 1 10 7.180000 1.5418403 0.4875727 1.1029661
## 6 fat-2 5 10 13.830278 2.1938820 0.6937664 1.5694086
## 7 fat-3 1 10 6.661667 0.5573333 0.1762443 0.3986922
## 8 fat-3 5 10 15.469603 2.7180726 0.8595300 1.9443920
## 9 fat-4 1 10 6.550000 0.9297550 0.2940144 0.6651067
## 10 fat-4 5 10 14.269286 3.3502694 1.0594482 2.3966384
## 11 fat-5 1 10 6.658333 0.7520035 0.2378044 0.5379509
## 12 fat-5 5 10 13.760000 1.2658796 0.4003063 0.9055557
## 13 fat-7 1 10 6.763333 0.7701531 0.2435438 0.5509343
## 14 fat-7 5 10 12.288333 2.9628987 0.9369508 2.1195300
## 15 hyl-1 1 10 6.286667 1.5480135 0.4895248 1.1073821
## 16 hyl-1 5 10 12.709048 3.0385068 0.9608602 2.1736168
## 17 hyl-2 1 10 6.531667 1.0311342 0.3260733 0.7376289
## 18 hyl-2 5 10 13.040556 1.7716559 0.5602468 1.2673663
## 19 L4440 1 19 7.267043 1.5461373 0.3547082 0.7452143
## 20 L4440 5 20 12.589901 1.2310880 0.2752797 0.5761669
## 21 lagr-1 1 10 6.511667 1.1535252 0.3647767 0.8251822
## 22 lagr-1 5 10 11.693333 0.5736605 0.1814074 0.4103720
## 23 sms-1 1 8 7.195833 1.0883857 0.3848024 0.9099132
## 24 sms-1 5 10 12.659286 3.0596751 0.9675542 2.1887597
## 25 sphk-1 1 10 8.200000 2.0531818 0.6492731 1.4687578
## 26 sphk-1 5 10 11.105952 1.7065364 0.5396542 1.2207826Now, let’s look at some graphs
library(ggplot2)
lgg$age <- as.factor(lgg$age) # make age character information
ggplot(lgg, aes(y=interval, x=RNAi, fill=age)) +
geom_boxplot() +
geom_point(pch = 21,
position = position_jitterdodge(0.2))## Warning: Removed 6 rows containing non-finite values (stat_boxplot).## Warning: Removed 6 rows containing missing values (geom_point).
Redo graph in order of RNAi
lgg$RNAi = factor(lgg$RNAi, levels = c("L4440","asm-3","hyl-1", "hyl-2", "lagr-1", "sphk-1", "sms-1", "elo-6", "fat-2", "fat-3", "fat-4", "fat-5", "fat-7"))
levels(lgg$RNAi)## [1] "L4440" "asm-3" "hyl-1" "hyl-2" "lagr-1" "sphk-1" "sms-1" "elo-6"
## [9] "fat-2" "fat-3" "fat-4" "fat-5" "fat-7"ggplot(lgg, aes(y=interval, x=RNAi, fill=age)) +
geom_boxplot() +
geom_point(pch = 21,
position = position_jitterdodge(0.2))## Warning: Removed 6 rows containing non-finite values (stat_boxplot).## Warning: Removed 6 rows containing missing values (geom_point).
Clean up figure more
ggplot(data=lgg, aes(x=age, y=interval)) +
geom_boxplot(aes(fill=age)) +
facet_wrap(~RNAi) +
geom_dotplot(aes(x=age, y=interval),
binaxis = "y", stackdir = "center",
position=position_dodge(0.8),
dotsize = 1, colour = "black")+
theme(axis.text.x=element_text(angle = 90))## Warning: Removed 6 rows containing non-finite values (stat_boxplot).## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.## Warning: Removed 6 rows containing non-finite values (stat_bindot).
Graphs for size
ggplot(lgg, aes(y=size, x=RNAi, fill=age)) +
geom_boxplot() +
geom_point(pch = 21,
position = position_jitterdodge(0.2))
STATISTICS
We will run anovas.
First, the anova for interval
lgg$age <- as.factor(lgg$age) # make age character information
interval <- lm(interval~RNAi*age, data=lgg)
anova(interval)## Analysis of Variance Table
##
## Response: interval
## Df Sum Sq Mean Sq F value Pr(>F)
## RNAi 12 136812 11401 0.8587 0.5898
## age 1 480117 480117 36.1621 6.589e-09 ***
## RNAi:age 12 96822 8069 0.6077 0.8349
## Residuals 244 3239533 13277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The post-hoc test for pairwise comparisons
a <- TukeyHSD(aov(interval~RNAi*age, data=lgg))
b <- a$`RNAi:age`
b## diff lwr upr p adj
## asm-3:1-L4440:1 128.9460526 -38.57093 296.463037 0.430353101
## hyl-1:1-L4440:1 3.3525341 -170.15471 176.859777 1.000000000
## hyl-2:1-L4440:1 53.4460526 -114.07093 220.963037 0.999984848
## lagr-1:1-L4440:1 25.8377193 -141.67927 193.354704 1.000000000
## sphk-1:1-L4440:1 75.6488304 -97.85841 249.156073 0.997071637
## sms-1:1-L4440:1 45.8972431 -143.68493 235.479418 0.999999942
## elo-6:1-L4440:1 18.1814693 -162.53459 198.897524 1.000000000
## fat-2:1-L4440:1 4.0077193 -163.50927 171.524704 1.000000000
## fat-3:1-L4440:1 7.0943860 -160.42260 174.611371 1.000000000
## fat-4:1-L4440:1 -18.1659844 -191.67323 155.341259 1.000000000
## fat-5:1-L4440:1 75.7692008 -97.73804 249.276444 0.997002967
## fat-7:1-L4440:1 38.1427193 -129.37427 205.659704 0.999999984
## L4440:5-L4440:1 -68.3061696 -205.67151 69.059167 0.982797306
## asm-3:5-L4440:1 -53.4165664 -220.93355 114.100418 0.999985005
## hyl-1:5-L4440:1 -71.2256140 -238.74260 96.291371 0.997980492
## hyl-2:5-L4440:1 -57.6682331 -225.18522 109.848752 0.999938603
## lagr-1:5-L4440:1 -33.1056140 -200.62260 134.411371 0.999999999
## sphk-1:5-L4440:1 -42.6706140 -210.18760 124.846371 0.999999829
## sms-1:5-L4440:1 -33.3106140 -200.82760 134.206371 0.999999999
## elo-6:5-L4440:1 -35.8211696 -203.33815 131.695815 0.999999996
## fat-2:5-L4440:1 -65.9818045 -233.49879 101.535180 0.999386917
## fat-3:5-L4440:1 -77.5748998 -245.09188 89.942085 0.993154142
## fat-4:5-L4440:1 -35.1817251 -202.69871 132.335260 0.999999997
## fat-5:5-L4440:1 -43.9456140 -211.46260 123.571371 0.999999685
## fat-7:5-L4440:1 -32.6306140 -200.14760 134.886371 1.000000000
## hyl-1:1-asm-3:1 -125.5935185 -322.60530 71.418261 0.799606979
## hyl-2:1-asm-3:1 -75.5000000 -267.25719 116.257185 0.999390785
## lagr-1:1-asm-3:1 -103.1083333 -294.86552 88.648852 0.957524124
## sphk-1:1-asm-3:1 -53.2972222 -250.30900 143.714558 0.999999408
## sms-1:1-asm-3:1 -83.0488095 -294.35494 128.257317 0.999408102
## elo-6:1-asm-3:1 -110.7645833 -314.15379 92.624626 0.951332371
## fat-2:1-asm-3:1 -124.9383333 -316.69552 66.818852 0.765678128
## fat-3:1-asm-3:1 -121.8516667 -313.60885 69.905519 0.804331564
## fat-4:1-asm-3:1 -147.1120370 -344.12382 49.899743 0.496272668
## fat-5:1-asm-3:1 -53.1768518 -250.18863 143.834928 0.999999435
## fat-7:1-asm-3:1 -90.8033333 -282.56052 100.953852 0.990773390
## L4440:5-asm-3:1 -197.2522222 -363.31882 -31.185628 0.003955727
## asm-3:5-asm-3:1 -182.3626191 -374.11980 9.394566 0.087363127
## hyl-1:5-asm-3:1 -200.1716667 -391.92885 -8.414481 0.029163123
## hyl-2:5-asm-3:1 -186.6142857 -378.37147 5.142900 0.068270762
## lagr-1:5-asm-3:1 -162.0516667 -353.80885 29.705519 0.244540717
## sphk-1:5-asm-3:1 -171.6166667 -363.37385 20.140519 0.155500681
## sms-1:5-asm-3:1 -162.2566667 -354.01385 29.500519 0.242332500
## elo-6:5-asm-3:1 -164.7672222 -356.52441 26.989963 0.216357806
## fat-2:5-asm-3:1 -194.9278571 -386.68504 -3.170672 0.040979726
## fat-3:5-asm-3:1 -206.5209524 -398.27814 -14.763767 0.018976231
## fat-4:5-asm-3:1 -164.1277778 -355.88496 27.629408 0.222785673
## fat-5:5-asm-3:1 -172.8916667 -364.64885 18.865519 0.145748350
## fat-7:5-asm-3:1 -161.5766667 -353.33385 30.180519 0.249707669
## hyl-2:1-hyl-1:1 50.0935185 -146.91826 247.105298 0.999999836
## lagr-1:1-hyl-1:1 22.4851852 -174.52659 219.496965 1.000000000
## sphk-1:1-hyl-1:1 72.2962963 -129.83352 274.426117 0.999879090
## sms-1:1-hyl-1:1 42.5447090 -173.54116 258.630577 0.999999999
## elo-6:1-hyl-1:1 14.8289352 -193.52172 223.179586 1.000000000
## fat-2:1-hyl-1:1 0.6551852 -196.35659 197.666965 1.000000000
## fat-3:1-hyl-1:1 3.7418519 -193.26993 200.753632 1.000000000
## fat-4:1-hyl-1:1 -21.5185185 -223.64834 180.611303 1.000000000
## fat-5:1-hyl-1:1 72.4166667 -129.71315 274.546488 0.999875524
## fat-7:1-hyl-1:1 34.7901852 -162.22159 231.801965 1.000000000
## L4440:5-hyl-1:1 -71.6587037 -243.76604 100.448637 0.998530585
## asm-3:5-hyl-1:1 -56.7691005 -253.78088 140.242679 0.999997891
## hyl-1:5-hyl-1:1 -74.5781481 -271.58993 122.433632 0.999680927
## hyl-2:5-hyl-1:1 -61.0207672 -258.03255 135.991013 0.999991370
## lagr-1:5-hyl-1:1 -36.4581481 -233.46993 160.553632 1.000000000
## sphk-1:5-hyl-1:1 -46.0231481 -243.03493 150.988632 0.999999973
## sms-1:5-hyl-1:1 -36.6631481 -233.67493 160.348632 1.000000000
## elo-6:5-hyl-1:1 -39.1737037 -236.18548 157.838076 0.999999999
## fat-2:5-hyl-1:1 -69.3343386 -266.34612 127.677441 0.999909038
## fat-3:5-hyl-1:1 -80.9274339 -277.93921 116.084346 0.998807049
## fat-4:5-hyl-1:1 -38.5342592 -235.54604 158.477521 0.999999999
## fat-5:5-hyl-1:1 -47.2981481 -244.30993 149.713632 0.999999951
## fat-7:5-hyl-1:1 -35.9831481 -232.99493 161.028632 1.000000000
## lagr-1:1-hyl-2:1 -27.6083333 -219.36552 164.148852 1.000000000
## sphk-1:1-hyl-2:1 22.2027778 -174.80900 219.214558 1.000000000
## sms-1:1-hyl-2:1 -7.5488095 -218.85494 203.757317 1.000000000
## elo-6:1-hyl-2:1 -35.2645833 -238.65379 168.124626 1.000000000
## fat-2:1-hyl-2:1 -49.4383333 -241.19552 142.318852 0.999999780
## fat-3:1-hyl-2:1 -46.3516667 -238.10885 145.405519 0.999999943
## fat-4:1-hyl-2:1 -71.6120370 -268.62382 125.399743 0.999839956
## fat-5:1-hyl-2:1 22.3231482 -174.68863 219.334928 1.000000000
## fat-7:1-hyl-2:1 -15.3033333 -207.06052 176.453852 1.000000000
## L4440:5-hyl-2:1 -121.7522222 -287.81882 44.314372 0.536041770
## asm-3:5-hyl-2:1 -106.8626191 -298.61980 84.894566 0.938256737
## hyl-1:5-hyl-2:1 -124.6716667 -316.42885 67.085519 0.769144660
## hyl-2:5-hyl-2:1 -111.1142857 -302.87147 80.642900 0.909792843
## lagr-1:5-hyl-2:1 -86.5516667 -278.30885 105.205519 0.995194222
## sphk-1:5-hyl-2:1 -96.1166667 -287.87385 95.640519 0.981035658
## sms-1:5-hyl-2:1 -86.7566667 -278.51385 105.000519 0.995032448
## elo-6:5-hyl-2:1 -89.2672222 -281.02441 102.489963 0.992650692
## fat-2:5-hyl-2:1 -119.4278571 -311.18504 72.329328 0.832252406
## fat-3:5-hyl-2:1 -131.0209524 -322.77814 60.736233 0.681171571
## fat-4:5-hyl-2:1 -88.6277778 -280.38496 103.129408 0.993332630
## fat-5:5-hyl-2:1 -97.3916667 -289.14885 94.365519 0.977779227
## fat-7:5-hyl-2:1 -86.0766667 -277.83385 105.680519 0.995552084
## sphk-1:1-lagr-1:1 49.8111111 -147.20067 246.822891 0.999999854
## sms-1:1-lagr-1:1 20.0595238 -191.24660 231.365651 1.000000000
## elo-6:1-lagr-1:1 -7.6562500 -211.04546 195.732959 1.000000000
## fat-2:1-lagr-1:1 -21.8300000 -213.58719 169.927185 1.000000000
## fat-3:1-lagr-1:1 -18.7433333 -210.50052 173.013852 1.000000000
## fat-4:1-lagr-1:1 -44.0037037 -241.01548 153.008076 0.999999990
## fat-5:1-lagr-1:1 49.9314815 -147.08030 246.943261 0.999999846
## fat-7:1-lagr-1:1 12.3050000 -179.45219 204.062185 1.000000000
## L4440:5-lagr-1:1 -94.1438889 -260.21048 71.922705 0.926806799
## asm-3:5-lagr-1:1 -79.2542857 -271.01147 112.502900 0.998687986
## hyl-1:5-lagr-1:1 -97.0633333 -288.82052 94.693852 0.978656897
## hyl-2:5-lagr-1:1 -83.5059524 -275.26314 108.251233 0.997122276
## lagr-1:5-lagr-1:1 -58.9433333 -250.70052 132.813852 0.999992541
## sphk-1:5-lagr-1:1 -68.5083333 -260.26552 123.248852 0.999881471
## sms-1:5-lagr-1:1 -59.1483333 -250.90552 132.608852 0.999992027
## elo-6:5-lagr-1:1 -61.6588889 -253.41607 130.098296 0.999982457
## fat-2:5-lagr-1:1 -91.8195238 -283.57671 99.937662 0.989327221
## fat-3:5-lagr-1:1 -103.4126191 -295.16980 88.344566 0.956153074
## fat-4:5-lagr-1:1 -61.0194444 -252.77663 130.737741 0.999985580
## fat-5:5-lagr-1:1 -69.7833333 -261.54052 121.973852 0.999836702
## fat-7:5-lagr-1:1 -58.4683333 -250.22552 133.288852 0.999993618
## sms-1:1-sphk-1:1 -29.7515873 -245.83746 186.334281 1.000000000
## elo-6:1-sphk-1:1 -57.4673611 -265.81801 150.883290 0.999999123
## fat-2:1-sphk-1:1 -71.6411111 -268.65289 125.370669 0.999838830
## fat-3:1-sphk-1:1 -68.5544444 -265.56622 128.457335 0.999925591
## fat-4:1-sphk-1:1 -93.8148148 -295.94464 108.315006 0.992941808
## fat-5:1-sphk-1:1 0.1203704 -202.00945 202.250192 1.000000000
## fat-7:1-sphk-1:1 -37.5061111 -234.51789 159.505669 1.000000000
## L4440:5-sphk-1:1 -143.9550000 -316.06234 28.152341 0.262914627
## asm-3:5-sphk-1:1 -129.0653968 -326.07718 67.946383 0.756674207
## hyl-1:5-sphk-1:1 -146.8744444 -343.88622 50.137335 0.499788844
## hyl-2:5-sphk-1:1 -133.3170635 -330.32884 63.694716 0.699420848
## lagr-1:5-sphk-1:1 -108.7544444 -305.76622 88.257335 0.943950504
## sphk-1:5-sphk-1:1 -118.3194444 -315.33122 78.692335 0.875657377
## sms-1:5-sphk-1:1 -108.9594444 -305.97122 88.052335 0.942855364
## elo-6:5-sphk-1:1 -111.4700000 -308.48178 85.541780 0.928185573
## fat-2:5-sphk-1:1 -141.6306349 -338.64241 55.381145 0.578096218
## fat-3:5-sphk-1:1 -153.2237302 -350.23551 43.788050 0.408209497
## fat-4:5-sphk-1:1 -110.8305555 -307.84234 86.181224 0.932146429
## fat-5:5-sphk-1:1 -119.5944444 -316.60622 77.417335 0.863803862
## fat-7:5-sphk-1:1 -108.2794444 -305.29122 88.732335 0.946430069
## elo-6:1-sms-1:1 -27.7157738 -249.63159 194.200047 1.000000000
## fat-2:1-sms-1:1 -41.8895238 -253.19565 169.416603 0.999999999
## fat-3:1-sms-1:1 -38.8028571 -250.10898 172.503270 1.000000000
## fat-4:1-sms-1:1 -64.0632275 -280.14910 152.022641 0.999996302
## fat-5:1-sms-1:1 29.8719577 -186.21391 245.957826 1.000000000
## fat-7:1-sms-1:1 -7.7545238 -219.06065 203.551603 1.000000000
## L4440:5-sms-1:1 -114.2034127 -302.50523 74.098405 0.864837948
## asm-3:5-sms-1:1 -99.3138095 -310.61994 111.992317 0.991644949
## hyl-1:5-sms-1:1 -117.1228571 -328.42898 94.183270 0.941552038
## hyl-2:5-sms-1:1 -103.5654762 -314.87160 107.740651 0.985627193
## lagr-1:5-sms-1:1 -79.0028571 -290.30898 132.303270 0.999740998
## sphk-1:5-sms-1:1 -88.5678571 -299.87398 122.738270 0.998373108
## sms-1:5-sms-1:1 -79.2078571 -290.51398 132.098270 0.999729418
## elo-6:5-sms-1:1 -81.7184127 -293.02454 129.587714 0.999545162
## fat-2:5-sms-1:1 -111.8790476 -323.18517 99.427079 0.964136365
## fat-3:5-sms-1:1 -123.4721429 -334.77827 87.833984 0.902530020
## fat-4:5-sms-1:1 -81.0789682 -292.38509 130.227158 0.999600398
## fat-5:5-sms-1:1 -89.8428571 -301.14898 121.463270 0.997980903
## fat-7:5-sms-1:1 -78.5278571 -289.83398 132.778270 0.999766145
## fat-2:1-elo-6:1 -14.1737500 -217.56296 189.215459 1.000000000
## fat-3:1-elo-6:1 -11.0870833 -214.47629 192.302126 1.000000000
## fat-4:1-elo-6:1 -36.3474537 -244.69810 172.003197 1.000000000
## fat-5:1-elo-6:1 57.5877315 -150.76292 265.938382 0.999999085
## fat-7:1-elo-6:1 19.9612500 -183.42796 223.350459 1.000000000
## L4440:5-elo-6:1 -86.4876389 -265.86006 92.884783 0.988325563
## asm-3:5-elo-6:1 -71.5980357 -274.98724 131.791173 0.999908604
## hyl-1:5-elo-6:1 -89.4070833 -292.79629 113.982126 0.996701440
## hyl-2:5-elo-6:1 -75.8497024 -279.23891 127.539507 0.999751904
## lagr-1:5-elo-6:1 -51.2870833 -254.67629 152.102126 0.999999862
## sphk-1:5-elo-6:1 -60.8520833 -264.24129 142.537126 0.999995580
## sms-1:5-elo-6:1 -51.4920833 -254.88129 151.897126 0.999999850
## elo-6:5-elo-6:1 -54.0026389 -257.39185 149.386570 0.999999597
## fat-2:5-elo-6:1 -84.1632738 -287.55248 119.225935 0.998663347
## fat-3:5-elo-6:1 -95.7563691 -299.14558 107.632840 0.991452867
## fat-4:5-elo-6:1 -53.3631944 -256.75240 150.026015 0.999999684
## fat-5:5-elo-6:1 -62.1270833 -265.51629 141.262126 0.999993392
## fat-7:5-elo-6:1 -50.8120833 -254.20129 152.577126 0.999999886
## fat-3:1-fat-2:1 3.0866667 -188.67052 194.843852 1.000000000
## fat-4:1-fat-2:1 -22.1737037 -219.18548 174.838076 1.000000000
## fat-5:1-fat-2:1 71.7614815 -125.25030 268.773261 0.999834093
## fat-7:1-fat-2:1 34.1350000 -157.62219 225.892185 1.000000000
## L4440:5-fat-2:1 -72.3138889 -238.38048 93.752705 0.997124826
## asm-3:5-fat-2:1 -57.4242857 -249.18147 134.332900 0.999995500
## hyl-1:5-fat-2:1 -75.2333333 -266.99052 116.523852 0.999424710
## hyl-2:5-fat-2:1 -61.6759524 -253.43314 130.081233 0.999982365
## lagr-1:5-fat-2:1 -37.1133333 -228.87052 154.643852 1.000000000
## sphk-1:5-fat-2:1 -46.6783333 -238.43552 145.078852 0.999999934
## sms-1:5-fat-2:1 -37.3183333 -229.07552 154.438852 1.000000000
## elo-6:5-fat-2:1 -39.8288889 -231.58607 151.928296 0.999999998
## fat-2:5-fat-2:1 -69.9895238 -261.74671 121.767662 0.999828183
## fat-3:5-fat-2:1 -81.5826191 -273.33980 110.174566 0.997961676
## fat-4:5-fat-2:1 -39.1894444 -230.94663 152.567741 0.999999999
## fat-5:5-fat-2:1 -47.9533333 -239.71052 143.803852 0.999999884
## fat-7:5-fat-2:1 -36.6383333 -228.39552 155.118852 1.000000000
## fat-4:1-fat-3:1 -25.2603704 -222.27215 171.751409 1.000000000
## fat-5:1-fat-3:1 68.6748148 -128.33696 265.686595 0.999923228
## fat-7:1-fat-3:1 31.0483333 -160.70885 222.805519 1.000000000
## L4440:5-fat-3:1 -75.4005556 -241.46715 90.666038 0.994780464
## asm-3:5-fat-3:1 -60.5109524 -252.26814 131.246233 0.999987690
## hyl-1:5-fat-3:1 -78.3200000 -270.07719 113.437185 0.998908694
## hyl-2:5-fat-3:1 -64.7626190 -256.51980 126.994566 0.999956543
## lagr-1:5-fat-3:1 -40.2000000 -231.95719 151.557185 0.999999997
## sphk-1:5-fat-3:1 -49.7650000 -241.52219 141.992185 0.999999748
## sms-1:5-fat-3:1 -40.4050000 -232.16219 151.352185 0.999999997
## elo-6:5-fat-3:1 -42.9155556 -234.67274 148.841630 0.999999989
## fat-2:5-fat-3:1 -73.0761905 -264.83338 118.680995 0.999643269
## fat-3:5-fat-3:1 -84.6692857 -276.42647 107.087900 0.996483104
## fat-4:5-fat-3:1 -42.2761111 -234.03330 149.481074 0.999999992
## fat-5:5-fat-3:1 -51.0400000 -242.79719 140.717185 0.999999576
## fat-7:5-fat-3:1 -39.7250000 -231.48219 152.032185 0.999999998
## fat-5:1-fat-4:1 93.9351852 -108.19464 296.065006 0.992818344
## fat-7:1-fat-4:1 56.3087037 -140.70308 253.320483 0.999998206
## L4440:5-fat-4:1 -50.1401852 -222.24753 121.967156 0.999997378
## asm-3:5-fat-4:1 -35.2505820 -232.26236 161.761198 1.000000000
## hyl-1:5-fat-4:1 -53.0596296 -250.07141 143.952150 0.999999460
## hyl-2:5-fat-4:1 -39.5022487 -236.51403 157.509531 0.999999999
## lagr-1:5-fat-4:1 -14.9396296 -211.95141 182.072150 1.000000000
## sphk-1:5-fat-4:1 -24.5046296 -221.51641 172.507150 1.000000000
## sms-1:5-fat-4:1 -15.1446296 -212.15641 181.867150 1.000000000
## elo-6:5-fat-4:1 -17.6551852 -214.66696 179.356595 1.000000000
## fat-2:5-fat-4:1 -47.8158201 -244.82760 149.195960 0.999999938
## fat-3:5-fat-4:1 -59.4089154 -256.42070 137.602864 0.999994850
## fat-4:5-fat-4:1 -17.0157407 -214.02752 179.996039 1.000000000
## fat-5:5-fat-4:1 -25.7796296 -222.79141 171.232150 1.000000000
## fat-7:5-fat-4:1 -14.4646296 -211.47641 182.547150 1.000000000
## fat-7:1-fat-5:1 -37.6264815 -234.63826 159.385298 1.000000000
## L4440:5-fat-5:1 -144.0753704 -316.18271 28.031971 0.261399445
## asm-3:5-fat-5:1 -129.1857672 -326.19755 67.826013 0.755118957
## hyl-1:5-fat-5:1 -146.9948148 -344.00659 50.016965 0.498006861
## hyl-2:5-fat-5:1 -133.4374339 -330.44921 63.574346 0.697737587
## lagr-1:5-fat-5:1 -108.8748148 -305.88659 88.136965 0.943309306
## sphk-1:5-fat-5:1 -118.4398148 -315.45159 78.571965 0.874566092
## sms-1:5-fat-5:1 -109.0798148 -306.09159 87.931965 0.942205200
## elo-6:5-fat-5:1 -111.5903704 -308.60215 85.421409 0.927422461
## fat-2:5-fat-5:1 -141.7510053 -338.76279 55.260775 0.576295122
## fat-3:5-fat-5:1 -153.3441005 -350.35588 43.667679 0.406534892
## fat-4:5-fat-5:1 -110.9509259 -307.96271 86.060854 0.931412722
## fat-5:5-fat-5:1 -119.7148148 -316.72659 77.296965 0.862651342
## fat-7:5-fat-5:1 -108.3998148 -305.41159 88.611965 0.945809338
## L4440:5-fat-7:1 -106.4488889 -272.51548 59.617705 0.791370005
## asm-3:5-fat-7:1 -91.5592857 -283.31647 100.197900 0.989714117
## hyl-1:5-fat-7:1 -109.3683333 -301.12552 82.388852 0.922376073
## hyl-2:5-fat-7:1 -95.8109524 -287.56814 95.946233 0.981757295
## lagr-1:5-fat-7:1 -71.2483333 -263.00552 120.508852 0.999766954
## sphk-1:5-fat-7:1 -80.8133333 -272.57052 110.943852 0.998232759
## sms-1:5-fat-7:1 -71.4533333 -263.21052 120.303852 0.999755316
## elo-6:5-fat-7:1 -73.9638889 -265.72107 117.793297 0.999564373
## fat-2:5-fat-7:1 -104.1245238 -295.88171 87.632662 0.952818172
## fat-3:5-fat-7:1 -115.7176190 -307.47480 76.039566 0.870446669
## fat-4:5-fat-7:1 -73.3244444 -265.08163 118.432741 0.999622599
## fat-5:5-fat-7:1 -82.0883333 -273.84552 109.668852 0.997764554
## fat-7:5-fat-7:1 -70.7733333 -262.53052 120.983852 0.999792038
## asm-3:5-L4440:5 14.8896032 -151.17699 180.956197 1.000000000
## hyl-1:5-L4440:5 -2.9194444 -168.98604 163.147149 1.000000000
## hyl-2:5-L4440:5 10.6379365 -155.42866 176.704530 1.000000000
## lagr-1:5-L4440:5 35.2005556 -130.86604 201.267149 0.999999997
## sphk-1:5-L4440:5 25.6355556 -140.43104 191.702149 1.000000000
## sms-1:5-L4440:5 34.9955556 -131.07104 201.062149 0.999999997
## elo-6:5-L4440:5 32.4850000 -133.58159 198.551594 0.999999999
## fat-2:5-L4440:5 2.3243651 -163.74223 168.390959 1.000000000
## fat-3:5-L4440:5 -9.2687302 -175.33532 156.797864 1.000000000
## fat-4:5-L4440:5 33.1244445 -132.94215 199.191038 0.999999999
## fat-5:5-L4440:5 24.3605556 -141.70604 190.427149 1.000000000
## fat-7:5-L4440:5 35.6755556 -130.39104 201.742149 0.999999996
## hyl-1:5-asm-3:5 -17.8090476 -209.56623 173.948138 1.000000000
## hyl-2:5-asm-3:5 -4.2516666 -196.00885 187.505519 1.000000000
## lagr-1:5-asm-3:5 20.3109524 -171.44623 212.068138 1.000000000
## sphk-1:5-asm-3:5 10.7459524 -181.01123 202.503138 1.000000000
## sms-1:5-asm-3:5 20.1059524 -171.65123 211.863138 1.000000000
## elo-6:5-asm-3:5 17.5953968 -174.16179 209.352582 1.000000000
## fat-2:5-asm-3:5 -12.5652381 -204.32242 179.191947 1.000000000
## fat-3:5-asm-3:5 -24.1583333 -215.91552 167.598852 1.000000000
## fat-4:5-asm-3:5 18.2348413 -173.52234 209.992027 1.000000000
## fat-5:5-asm-3:5 9.4709524 -182.28623 201.228138 1.000000000
## fat-7:5-asm-3:5 20.7859524 -170.97123 212.543138 1.000000000
## hyl-2:5-hyl-1:5 13.5573810 -178.19980 205.314566 1.000000000
## lagr-1:5-hyl-1:5 38.1200000 -153.63719 229.877185 0.999999999
## sphk-1:5-hyl-1:5 28.5550000 -163.20219 220.312185 1.000000000
## sms-1:5-hyl-1:5 37.9150000 -153.84219 229.672185 0.999999999
## elo-6:5-hyl-1:5 35.4044444 -156.35274 227.161630 1.000000000
## fat-2:5-hyl-1:5 5.2438095 -186.51338 197.000995 1.000000000
## fat-3:5-hyl-1:5 -6.3492857 -198.10647 185.407900 1.000000000
## fat-4:5-hyl-1:5 36.0438889 -155.71330 227.801074 1.000000000
## fat-5:5-hyl-1:5 27.2800000 -164.47719 219.037185 1.000000000
## fat-7:5-hyl-1:5 38.5950000 -153.16219 230.352185 0.999999999
## lagr-1:5-hyl-2:5 24.5626190 -167.19457 216.319804 1.000000000
## sphk-1:5-hyl-2:5 14.9976190 -176.75957 206.754804 1.000000000
## sms-1:5-hyl-2:5 24.3576190 -167.39957 216.114804 1.000000000
## elo-6:5-hyl-2:5 21.8470635 -169.91012 213.604249 1.000000000
## fat-2:5-hyl-2:5 -8.3135714 -200.07076 183.443614 1.000000000
## fat-3:5-hyl-2:5 -19.9066667 -211.66385 171.850519 1.000000000
## fat-4:5-hyl-2:5 22.4865079 -169.27068 214.243693 1.000000000
## fat-5:5-hyl-2:5 13.7226190 -178.03457 205.479804 1.000000000
## fat-7:5-hyl-2:5 25.0376190 -166.71957 216.794804 1.000000000
## sphk-1:5-lagr-1:5 -9.5650000 -201.32219 182.192185 1.000000000
## sms-1:5-lagr-1:5 -0.2050000 -191.96219 191.552185 1.000000000
## elo-6:5-lagr-1:5 -2.7155556 -194.47274 189.041630 1.000000000
## fat-2:5-lagr-1:5 -32.8761905 -224.63338 158.880995 1.000000000
## fat-3:5-lagr-1:5 -44.4692857 -236.22647 147.287900 0.999999977
## fat-4:5-lagr-1:5 -2.0761111 -193.83330 189.681074 1.000000000
## fat-5:5-lagr-1:5 -10.8400000 -202.59719 180.917185 1.000000000
## fat-7:5-lagr-1:5 0.4750000 -191.28219 192.232185 1.000000000
## sms-1:5-sphk-1:5 9.3600000 -182.39719 201.117185 1.000000000
## elo-6:5-sphk-1:5 6.8494444 -184.90774 198.606630 1.000000000
## fat-2:5-sphk-1:5 -23.3111905 -215.06838 168.445995 1.000000000
## fat-3:5-sphk-1:5 -34.9042857 -226.66147 156.852900 1.000000000
## fat-4:5-sphk-1:5 7.4888889 -184.26830 199.246074 1.000000000
## fat-5:5-sphk-1:5 -1.2750000 -193.03219 190.482185 1.000000000
## fat-7:5-sphk-1:5 10.0400000 -181.71719 201.797185 1.000000000
## elo-6:5-sms-1:5 -2.5105556 -194.26774 189.246630 1.000000000
## fat-2:5-sms-1:5 -32.6711905 -224.42838 159.085995 1.000000000
## fat-3:5-sms-1:5 -44.2642857 -236.02147 147.492900 0.999999979
## fat-4:5-sms-1:5 -1.8711111 -193.62830 189.886074 1.000000000
## fat-5:5-sms-1:5 -10.6350000 -202.39219 181.122185 1.000000000
## fat-7:5-sms-1:5 0.6800000 -191.07719 192.437185 1.000000000
## fat-2:5-elo-6:5 -30.1606349 -221.91782 161.596550 1.000000000
## fat-3:5-elo-6:5 -41.7537302 -233.51092 150.003455 0.999999994
## fat-4:5-elo-6:5 0.6394445 -191.11774 192.396630 1.000000000
## fat-5:5-elo-6:5 -8.1244444 -199.88163 183.632741 1.000000000
## fat-7:5-elo-6:5 3.1905556 -188.56663 194.947741 1.000000000
## fat-3:5-fat-2:5 -11.5930952 -203.35028 180.164090 1.000000000
## fat-4:5-fat-2:5 30.8000794 -160.95711 222.557265 1.000000000
## fat-5:5-fat-2:5 22.0361905 -169.72099 213.793376 1.000000000
## fat-7:5-fat-2:5 33.3511905 -158.40599 225.108376 1.000000000
## fat-4:5-fat-3:5 42.3931746 -149.36401 234.150360 0.999999992
## fat-5:5-fat-3:5 33.6292857 -158.12790 225.386471 1.000000000
## fat-7:5-fat-3:5 44.9442857 -146.81290 236.701471 0.999999971
## fat-5:5-fat-4:5 -8.7638889 -200.52107 182.993296 1.000000000
## fat-7:5-fat-4:5 2.5511111 -189.20607 194.308296 1.000000000
## fat-7:5-fat-5:5 11.3150000 -180.44219 203.072185 1.000000000write.csv(b, file = "interval.csv")Now the stats for size
lgg$age <- as.factor(lgg$age) # make age character information
size <- lm(size~RNAi*age, data=lgg)
anova(size)## Analysis of Variance Table
##
## Response: size
## Df Sum Sq Mean Sq F value Pr(>F)
## RNAi 12 80.41 6.70 1.9050 0.0342212 *
## age 1 2453.79 2453.79 697.6153 < 2.2e-16 ***
## RNAi:age 12 128.37 10.70 3.0414 0.0005157 ***
## Residuals 250 879.35 3.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1post hoc analysis by size
c <- TukeyHSD(aov(size~RNAi*age, data=lgg))
c## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = size ~ RNAi * age, data = lgg)
##
## $RNAi
## diff lwr upr p adj
## asm-3-L4440 0.87175875 -0.85373600 2.5972535 0.8970174
## hyl-1-L4440 -0.49885633 -2.22435108 1.2266384 0.9990511
## hyl-2-L4440 -0.21060236 -1.93609711 1.5148924 0.9999999
## lagr-1-L4440 -0.89421347 -2.61970822 0.8312813 0.8790096
## sphk-1-L4440 -0.34373728 -2.06923203 1.3817575 0.9999809
## sms-1-L4440 0.23437118 -1.55336656 2.0221089 0.9999998
## elo-6-L4440 -0.43129994 -2.18655337 1.3239535 0.9998189
## fat-2-L4440 0.50842542 -1.21706933 2.2339202 0.9988557
## fat-3-L4440 1.06892145 -0.65657330 2.7944162 0.6832619
## fat-4-L4440 0.41292939 -1.31256536 2.1384241 0.9998627
## fat-5-L4440 0.21245320 -1.51304155 1.9379479 0.9999999
## fat-7-L4440 -0.47088014 -2.19637489 1.2546146 0.9994673
## hyl-1-asm-3 -1.37061508 -3.35458433 0.6133542 0.5111878
## hyl-2-asm-3 -1.08236111 -3.06633036 0.9016081 0.8353935
## lagr-1-asm-3 -1.76597222 -3.74994147 0.2179970 0.1373672
## sphk-1-asm-3 -1.21549603 -3.19946528 0.7684732 0.6989487
## sms-1-asm-3 -0.63738757 -2.67572221 1.4009471 0.9979639
## elo-6-asm-3 -1.30305869 -3.31296328 0.7068459 0.6148353
## fat-2-asm-3 -0.36333333 -2.34730259 1.6206359 0.9999925
## fat-3-asm-3 0.19716270 -1.78680655 2.1811320 1.0000000
## fat-4-asm-3 -0.45882937 -2.44279862 1.5251399 0.9999043
## fat-5-asm-3 -0.65930556 -2.64327481 1.3246637 0.9963870
## fat-7-asm-3 -1.34263889 -3.32660814 0.6413304 0.5455536
## hyl-2-hyl-1 0.28825397 -1.69571528 2.2722232 0.9999994
## lagr-1-hyl-1 -0.39535714 -2.37932639 1.5886121 0.9999808
## sphk-1-hyl-1 0.15511905 -1.82885020 2.1390883 1.0000000
## sms-1-hyl-1 0.73322751 -1.30510713 2.7715622 0.9925830
## elo-6-hyl-1 0.06755639 -1.94234820 2.0774610 1.0000000
## fat-2-hyl-1 1.00728175 -0.97668751 2.9912510 0.8937145
## fat-3-hyl-1 1.56777778 -0.41619147 3.5517470 0.2903601
## fat-4-hyl-1 0.91178571 -1.07218354 2.8957550 0.9461405
## fat-5-hyl-1 0.71130952 -1.27265973 2.6952788 0.9927980
## fat-7-hyl-1 0.02797619 -1.95599306 2.0119454 1.0000000
## lagr-1-hyl-2 -0.68361111 -2.66758036 1.3003581 0.9949609
## sphk-1-hyl-2 -0.13313492 -2.11710417 1.8508343 1.0000000
## sms-1-hyl-2 0.44497354 -1.59336110 2.4833082 0.9999484
## elo-6-hyl-2 -0.22069758 -2.23060217 1.7892070 1.0000000
## fat-2-hyl-2 0.71902778 -1.26494147 2.7029970 0.9920759
## fat-3-hyl-2 1.27952381 -0.70444544 3.2634931 0.6230146
## fat-4-hyl-2 0.62353175 -1.36043751 2.6075010 0.9978631
## fat-5-hyl-2 0.42305556 -1.56091370 2.4070248 0.9999599
## fat-7-hyl-2 -0.26027778 -2.24424703 1.7236915 0.9999998
## sphk-1-lagr-1 0.55047619 -1.43349306 2.5344454 0.9993703
## sms-1-lagr-1 1.12858466 -0.90974998 3.1669193 0.8209493
## elo-6-lagr-1 0.46291353 -1.54699106 2.4728181 0.9999085
## fat-2-lagr-1 1.40263889 -0.58133036 3.3866081 0.4722813
## fat-3-lagr-1 1.96313492 -0.02083433 3.9471042 0.0555039
## fat-4-lagr-1 1.30714286 -0.67682639 3.2911121 0.5892539
## fat-5-lagr-1 1.10666667 -0.87730259 3.0906359 0.8133723
## fat-7-lagr-1 0.42333333 -1.56063592 2.4073026 0.9999596
## sms-1-sphk-1 0.57810847 -1.46022617 2.6164431 0.9992156
## elo-6-sphk-1 -0.08756266 -2.09746725 1.9223419 1.0000000
## fat-2-sphk-1 0.85216270 -1.13180655 2.8361320 0.9675364
## fat-3-sphk-1 1.41265873 -0.57131052 3.3966280 0.4602554
## fat-4-sphk-1 0.75666667 -1.22730259 2.7406359 0.9876637
## fat-5-sphk-1 0.55619048 -1.42777878 2.5401597 0.9993016
## fat-7-sphk-1 -0.12714286 -2.11111211 1.8568264 1.0000000
## elo-6-sms-1 -0.66567112 -2.72925795 1.3979157 0.9972642
## fat-2-sms-1 0.27405423 -1.76428041 2.3123889 0.9999998
## fat-3-sms-1 0.83455026 -1.20378438 2.8728849 0.9777720
## fat-4-sms-1 0.17855820 -1.85977644 2.2168928 1.0000000
## fat-5-sms-1 -0.02191799 -2.06025263 2.0164167 1.0000000
## fat-7-sms-1 -0.70525132 -2.74358596 1.3330833 0.9947681
## fat-2-elo-6 0.93972535 -1.07017924 2.9496299 0.9390829
## fat-3-elo-6 1.50022139 -0.50968321 3.5101260 0.3811995
## fat-4-elo-6 0.84422932 -1.16567527 2.8541339 0.9727374
## fat-5-elo-6 0.64375313 -1.36615146 2.6536577 0.9974421
## fat-7-elo-6 -0.03958020 -2.04948479 1.9703244 1.0000000
## fat-3-fat-2 0.56049603 -1.42347322 2.5444653 0.9992456
## fat-4-fat-2 -0.09549603 -2.07946528 1.8884732 1.0000000
## fat-5-fat-2 -0.29597222 -2.27994147 1.6879970 0.9999993
## fat-7-fat-2 -0.97930556 -2.96327481 1.0046637 0.9115627
## fat-4-fat-3 -0.65599206 -2.63996132 1.3279772 0.9965522
## fat-5-fat-3 -0.85646825 -2.84043751 1.1275010 0.9662488
## fat-7-fat-3 -1.53980159 -3.52377084 0.4441677 0.3181739
## fat-5-fat-4 -0.20047619 -2.18444544 1.7834931 1.0000000
## fat-7-fat-4 -0.88380952 -2.86777878 1.1001597 0.9571650
## fat-7-fat-5 -0.68333333 -2.66730259 1.3006359 0.9949795
##
## $age
## diff lwr upr p adj
## 5-1 5.961414 5.516692 6.406135 0
##
## $`RNAi:age`
## diff lwr upr p adj
## asm-3:1-L4440:1 0.929624060 -1.79622049 3.65546862 0.9999487
## hyl-1:1-L4440:1 -0.980375940 -3.70622049 1.74546862 0.9998683
## hyl-2:1-L4440:1 -0.735375940 -3.46122049 1.99046862 0.9999994
## lagr-1:1-L4440:1 -0.755375940 -3.48122049 1.97046862 0.9999990
## sphk-1:1-L4440:1 0.932957393 -1.79288716 3.65880195 0.9999453
## sms-1:1-L4440:1 -0.071209273 -3.01182974 2.86941119 1.0000000
## elo-6:1-L4440:1 -0.398524088 -3.22184242 2.42479424 1.0000000
## fat-2:1-L4440:1 -0.087042607 -2.81288716 2.63880195 1.0000000
## fat-3:1-L4440:1 -0.605375940 -3.33122049 2.12046862 1.0000000
## fat-4:1-L4440:1 -0.717042607 -3.44288716 2.00880195 0.9999997
## fat-5:1-L4440:1 -0.608709273 -3.33455383 2.11713528 1.0000000
## fat-7:1-L4440:1 -0.503709273 -3.22955383 2.22213528 1.0000000
## L4440:5-L4440:1 5.322858186 3.08764273 7.55807365 0.0000000
## asm-3:5-L4440:1 6.273235171 3.54739062 8.99907973 0.0000000
## hyl-1:5-L4440:1 5.442005011 2.71616046 8.16784957 0.0000000
## hyl-2:5-L4440:1 5.773512949 3.04766839 8.49935750 0.0000000
## lagr-1:5-L4440:1 4.426290726 1.70044617 7.15213528 0.0000018
## sphk-1:5-L4440:1 3.838909775 1.11306522 6.56475433 0.0001042
## sms-1:5-L4440:1 5.392243107 2.66639855 8.11808766 0.0000000
## elo-6:5-L4440:1 4.725576441 1.99973189 7.45142100 0.0000002
## fat-2:5-L4440:1 6.563235171 3.83739062 9.28907973 0.0000000
## fat-3:5-L4440:1 8.202560567 5.47671601 10.92840512 0.0000000
## fat-4:5-L4440:1 7.002243108 4.27639855 9.72808766 0.0000000
## fat-5:5-L4440:1 6.492957393 3.76711284 9.21880195 0.0000000
## fat-7:5-L4440:1 5.021290727 2.29544617 7.74713528 0.0000000
## hyl-1:1-asm-3:1 -1.910000000 -5.03028228 1.21028228 0.8544929
## hyl-2:1-asm-3:1 -1.665000000 -4.78528228 1.45528228 0.9611122
## lagr-1:1-asm-3:1 -1.685000000 -4.80528228 1.43528228 0.9557296
## sphk-1:1-asm-3:1 0.003333333 -3.11694895 3.12361562 1.0000000
## sms-1:1-asm-3:1 -1.000833333 -4.31039248 2.30872581 0.9999946
## elo-6:1-asm-3:1 -1.328148148 -4.53393345 1.87763715 0.9986506
## fat-2:1-asm-3:1 -1.016666667 -4.13694895 2.10361562 0.9999778
## fat-3:1-asm-3:1 -1.535000000 -4.65528228 1.58528228 0.9850370
## fat-4:1-asm-3:1 -1.646666667 -4.76694895 1.47361562 0.9656020
## fat-5:1-asm-3:1 -1.538333333 -4.65861562 1.58194895 0.9846261
## fat-7:1-asm-3:1 -1.433333333 -4.55361562 1.68694895 0.9939134
## L4440:5-asm-3:1 4.393234126 1.69099040 7.09547785 0.0000017
## asm-3:5-asm-3:1 5.343611111 2.22332883 8.46389340 0.0000003
## hyl-1:5-asm-3:1 4.512380951 1.39209867 7.63266324 0.0000527
## hyl-2:5-asm-3:1 4.843888889 1.72360661 7.96417117 0.0000072
## lagr-1:5-asm-3:1 3.496666666 0.37638438 6.61694895 0.0104539
## sphk-1:5-asm-3:1 2.909285714 -0.21099657 6.02956800 0.1066915
## sms-1:5-asm-3:1 4.462619047 1.34233676 7.58290133 0.0000704
## elo-6:5-asm-3:1 3.795952381 0.67567010 6.91623467 0.0025460
## fat-2:5-asm-3:1 5.633611111 2.51332883 8.75389340 0.0000000
## fat-3:5-asm-3:1 7.272936507 4.15265422 10.39321879 0.0000000
## fat-4:5-asm-3:1 6.072619048 2.95233676 9.19290133 0.0000000
## fat-5:5-asm-3:1 5.563333333 2.44305105 8.68361562 0.0000001
## fat-7:5-asm-3:1 4.091666667 0.97138438 7.21194895 0.0005557
## hyl-2:1-hyl-1:1 0.245000000 -2.87528228 3.36528228 1.0000000
## lagr-1:1-hyl-1:1 0.225000000 -2.89528228 3.34528228 1.0000000
## sphk-1:1-hyl-1:1 1.913333333 -1.20694895 5.03361562 0.8523942
## sms-1:1-hyl-1:1 0.909166667 -2.40039248 4.21872581 0.9999992
## elo-6:1-hyl-1:1 0.581851852 -2.62393345 3.78763715 1.0000000
## fat-2:1-hyl-1:1 0.893333333 -2.22694895 4.01361562 0.9999982
## fat-3:1-hyl-1:1 0.375000000 -2.74528228 3.49528228 1.0000000
## fat-4:1-hyl-1:1 0.263333333 -2.85694895 3.38361562 1.0000000
## fat-5:1-hyl-1:1 0.371666667 -2.74861562 3.49194895 1.0000000
## fat-7:1-hyl-1:1 0.476666667 -2.64361562 3.59694895 1.0000000
## L4440:5-hyl-1:1 6.303234126 3.60099040 9.00547785 0.0000000
## asm-3:5-hyl-1:1 7.253611111 4.13332883 10.37389340 0.0000000
## hyl-1:5-hyl-1:1 6.422380951 3.30209867 9.54266324 0.0000000
## hyl-2:5-hyl-1:1 6.753888889 3.63360661 9.87417117 0.0000000
## lagr-1:5-hyl-1:1 5.406666666 2.28638438 8.52694895 0.0000002
## sphk-1:5-hyl-1:1 4.819285714 1.69900343 7.93956800 0.0000083
## sms-1:5-hyl-1:1 6.372619047 3.25233676 9.49290133 0.0000000
## elo-6:5-hyl-1:1 5.705952381 2.58567010 8.82623467 0.0000000
## fat-2:5-hyl-1:1 7.543611111 4.42332883 10.66389340 0.0000000
## fat-3:5-hyl-1:1 9.182936507 6.06265422 12.30321879 0.0000000
## fat-4:5-hyl-1:1 7.982619048 4.86233676 11.10290133 0.0000000
## fat-5:5-hyl-1:1 7.473333333 4.35305105 10.59361562 0.0000000
## fat-7:5-hyl-1:1 6.001666667 2.88138438 9.12194895 0.0000000
## lagr-1:1-hyl-2:1 -0.020000000 -3.14028228 3.10028228 1.0000000
## sphk-1:1-hyl-2:1 1.668333333 -1.45194895 4.78861562 0.9602509
## sms-1:1-hyl-2:1 0.664166667 -2.64539248 3.97372581 1.0000000
## elo-6:1-hyl-2:1 0.336851852 -2.86893345 3.54263715 1.0000000
## fat-2:1-hyl-2:1 0.648333333 -2.47194895 3.76861562 1.0000000
## fat-3:1-hyl-2:1 0.130000000 -2.99028228 3.25028228 1.0000000
## fat-4:1-hyl-2:1 0.018333333 -3.10194895 3.13861562 1.0000000
## fat-5:1-hyl-2:1 0.126666667 -2.99361562 3.24694895 1.0000000
## fat-7:1-hyl-2:1 0.231666667 -2.88861562 3.35194895 1.0000000
## L4440:5-hyl-2:1 6.058234126 3.35599040 8.76047785 0.0000000
## asm-3:5-hyl-2:1 7.008611111 3.88832883 10.12889340 0.0000000
## hyl-1:5-hyl-2:1 6.177380951 3.05709867 9.29766324 0.0000000
## hyl-2:5-hyl-2:1 6.508888889 3.38860661 9.62917117 0.0000000
## lagr-1:5-hyl-2:1 5.161666666 2.04138438 8.28194895 0.0000010
## sphk-1:5-hyl-2:1 4.574285714 1.45400343 7.69456800 0.0000366
## sms-1:5-hyl-2:1 6.127619047 3.00733676 9.24790133 0.0000000
## elo-6:5-hyl-2:1 5.460952381 2.34067010 8.58123467 0.0000001
## fat-2:5-hyl-2:1 7.298611111 4.17832883 10.41889340 0.0000000
## fat-3:5-hyl-2:1 8.937936507 5.81765422 12.05821879 0.0000000
## fat-4:5-hyl-2:1 7.737619048 4.61733676 10.85790133 0.0000000
## fat-5:5-hyl-2:1 7.228333333 4.10805105 10.34861562 0.0000000
## fat-7:5-hyl-2:1 5.756666667 2.63638438 8.87694895 0.0000000
## sphk-1:1-lagr-1:1 1.688333333 -1.43194895 4.80861562 0.9547816
## sms-1:1-lagr-1:1 0.684166667 -2.62539248 3.99372581 1.0000000
## elo-6:1-lagr-1:1 0.356851852 -2.84893345 3.56263715 1.0000000
## fat-2:1-lagr-1:1 0.668333333 -2.45194895 3.78861562 1.0000000
## fat-3:1-lagr-1:1 0.150000000 -2.97028228 3.27028228 1.0000000
## fat-4:1-lagr-1:1 0.038333333 -3.08194895 3.15861562 1.0000000
## fat-5:1-lagr-1:1 0.146666667 -2.97361562 3.26694895 1.0000000
## fat-7:1-lagr-1:1 0.251666667 -2.86861562 3.37194895 1.0000000
## L4440:5-lagr-1:1 6.078234126 3.37599040 8.78047785 0.0000000
## asm-3:5-lagr-1:1 7.028611111 3.90832883 10.14889340 0.0000000
## hyl-1:5-lagr-1:1 6.197380951 3.07709867 9.31766324 0.0000000
## hyl-2:5-lagr-1:1 6.528888889 3.40860661 9.64917117 0.0000000
## lagr-1:5-lagr-1:1 5.181666666 2.06138438 8.30194895 0.0000008
## sphk-1:5-lagr-1:1 4.594285714 1.47400343 7.71456800 0.0000325
## sms-1:5-lagr-1:1 6.147619047 3.02733676 9.26790133 0.0000000
## elo-6:5-lagr-1:1 5.480952381 2.36067010 8.60123467 0.0000001
## fat-2:5-lagr-1:1 7.318611111 4.19832883 10.43889340 0.0000000
## fat-3:5-lagr-1:1 8.957936507 5.83765422 12.07821879 0.0000000
## fat-4:5-lagr-1:1 7.757619048 4.63733676 10.87790133 0.0000000
## fat-5:5-lagr-1:1 7.248333333 4.12805105 10.36861562 0.0000000
## fat-7:5-lagr-1:1 5.776666667 2.65638438 8.89694895 0.0000000
## sms-1:1-sphk-1:1 -1.004166667 -4.31372581 2.30539248 0.9999943
## elo-6:1-sphk-1:1 -1.331481481 -4.53726678 1.87430382 0.9985975
## fat-2:1-sphk-1:1 -1.020000000 -4.14028228 2.10028228 0.9999764
## fat-3:1-sphk-1:1 -1.538333333 -4.65861562 1.58194895 0.9846261
## fat-4:1-sphk-1:1 -1.650000000 -4.77028228 1.47028228 0.9648164
## fat-5:1-sphk-1:1 -1.541666667 -4.66194895 1.57861562 0.9842062
## fat-7:1-sphk-1:1 -1.436666667 -4.55694895 1.68361562 0.9937165
## L4440:5-sphk-1:1 4.389900793 1.68765707 7.09214452 0.0000017
## asm-3:5-sphk-1:1 5.340277778 2.21999549 8.46056006 0.0000003
## hyl-1:5-sphk-1:1 4.509047618 1.38876533 7.62932990 0.0000538
## hyl-2:5-sphk-1:1 4.840555556 1.72027327 7.96083784 0.0000073
## lagr-1:5-sphk-1:1 3.493333333 0.37305105 6.61361562 0.0106112
## sphk-1:5-sphk-1:1 2.905952381 -0.21432990 6.02623467 0.1078942
## sms-1:5-sphk-1:1 4.459285714 1.33900343 7.57956800 0.0000718
## elo-6:5-sphk-1:1 3.792619048 0.67233676 6.91290133 0.0025882
## fat-2:5-sphk-1:1 5.630277778 2.50999549 8.75056006 0.0000000
## fat-3:5-sphk-1:1 7.269603174 4.14932089 10.38988546 0.0000000
## fat-4:5-sphk-1:1 6.069285714 2.94900343 9.18956800 0.0000000
## fat-5:5-sphk-1:1 5.560000000 2.43971772 8.68028228 0.0000001
## fat-7:5-sphk-1:1 4.088333334 0.96805105 7.20861562 0.0005657
## elo-6:1-sms-1:1 -0.327314815 -3.71760678 3.06297715 1.0000000
## fat-2:1-sms-1:1 -0.015833333 -3.32539248 3.29372581 1.0000000
## fat-3:1-sms-1:1 -0.534166667 -3.84372581 2.77539248 1.0000000
## fat-4:1-sms-1:1 -0.645833333 -3.95539248 2.66372581 1.0000000
## fat-5:1-sms-1:1 -0.537500000 -3.84705914 2.77205914 1.0000000
## fat-7:1-sms-1:1 -0.432500000 -3.74205914 2.87705914 1.0000000
## L4440:5-sms-1:1 5.394067460 2.47531065 8.31282427 0.0000000
## asm-3:5-sms-1:1 6.344444445 3.03488530 9.65400359 0.0000000
## hyl-1:5-sms-1:1 5.513214285 2.20365514 8.82277343 0.0000008
## hyl-2:5-sms-1:1 5.844722223 2.53516308 9.15428137 0.0000001
## lagr-1:5-sms-1:1 4.497500000 1.18794086 7.80705914 0.0002478
## sphk-1:5-sms-1:1 3.910119048 0.60055990 7.21967819 0.0043356
## sms-1:5-sms-1:1 5.463452381 2.15389324 8.77301152 0.0000010
## elo-6:5-sms-1:1 4.796785715 1.48722657 8.10634486 0.0000497
## fat-2:5-sms-1:1 6.634444445 3.32488530 9.94400359 0.0000000
## fat-3:5-sms-1:1 8.273769840 4.96421070 11.58332898 0.0000000
## fat-4:5-sms-1:1 7.073452381 3.76389324 10.38301152 0.0000000
## fat-5:5-sms-1:1 6.564166667 3.25460752 9.87372581 0.0000000
## fat-7:5-sms-1:1 5.092500000 1.78294086 8.40205914 0.0000093
## fat-2:1-elo-6:1 0.311481481 -2.89430382 3.51726678 1.0000000
## fat-3:1-elo-6:1 -0.206851852 -3.41263715 2.99893345 1.0000000
## fat-4:1-elo-6:1 -0.318518519 -3.52430382 2.88726678 1.0000000
## fat-5:1-elo-6:1 -0.210185185 -3.41597049 2.99560012 1.0000000
## fat-7:1-elo-6:1 -0.105185185 -3.31097049 3.10060012 1.0000000
## L4440:5-elo-6:1 5.721382274 2.92084322 8.52192133 0.0000000
## asm-3:5-elo-6:1 6.671759259 3.46597396 9.87754456 0.0000000
## hyl-1:5-elo-6:1 5.840529099 2.63474380 9.04631440 0.0000000
## hyl-2:5-elo-6:1 6.172037037 2.96625174 9.37782234 0.0000000
## lagr-1:5-elo-6:1 4.824814814 1.61902951 8.03060012 0.0000177
## sphk-1:5-elo-6:1 4.237433863 1.03164856 7.44321916 0.0004662
## sms-1:5-elo-6:1 5.790767195 2.58498189 8.99655250 0.0000000
## elo-6:5-elo-6:1 5.124100529 1.91831523 8.32988583 0.0000029
## fat-2:5-elo-6:1 6.961759259 3.75597396 10.16754456 0.0000000
## fat-3:5-elo-6:1 8.601084655 5.39529935 11.80686996 0.0000000
## fat-4:5-elo-6:1 7.400767196 4.19498190 10.60655250 0.0000000
## fat-5:5-elo-6:1 6.891481481 3.68569618 10.09726678 0.0000000
## fat-7:5-elo-6:1 5.419814815 2.21402951 8.62560012 0.0000005
## fat-3:1-fat-2:1 -0.518333333 -3.63861562 2.60194895 1.0000000
## fat-4:1-fat-2:1 -0.630000000 -3.75028228 2.49028228 1.0000000
## fat-5:1-fat-2:1 -0.521666667 -3.64194895 2.59861562 1.0000000
## fat-7:1-fat-2:1 -0.416666667 -3.53694895 2.70361562 1.0000000
## L4440:5-fat-2:1 5.409900793 2.70765707 8.11214452 0.0000000
## asm-3:5-fat-2:1 6.360277778 3.23999549 9.48056006 0.0000000
## hyl-1:5-fat-2:1 5.529047618 2.40876533 8.64932990 0.0000001
## hyl-2:5-fat-2:1 5.860555556 2.74027327 8.98083784 0.0000000
## lagr-1:5-fat-2:1 4.513333333 1.39305105 7.63361562 0.0000524
## sphk-1:5-fat-2:1 3.925952381 0.80567010 7.04623467 0.0013231
## sms-1:5-fat-2:1 5.479285714 2.35900343 8.59956800 0.0000001
## elo-6:5-fat-2:1 4.812619048 1.69233676 7.93290133 0.0000087
## fat-2:5-fat-2:1 6.650277778 3.52999549 9.77056006 0.0000000
## fat-3:5-fat-2:1 8.289603174 5.16932089 11.40988546 0.0000000
## fat-4:5-fat-2:1 7.089285714 3.96900343 10.20956800 0.0000000
## fat-5:5-fat-2:1 6.580000000 3.45971772 9.70028228 0.0000000
## fat-7:5-fat-2:1 5.108333334 1.98805105 8.22861562 0.0000013
## fat-4:1-fat-3:1 -0.111666667 -3.23194895 3.00861562 1.0000000
## fat-5:1-fat-3:1 -0.003333333 -3.12361562 3.11694895 1.0000000
## fat-7:1-fat-3:1 0.101666667 -3.01861562 3.22194895 1.0000000
## L4440:5-fat-3:1 5.928234126 3.22599040 8.63047785 0.0000000
## asm-3:5-fat-3:1 6.878611111 3.75832883 9.99889340 0.0000000
## hyl-1:5-fat-3:1 6.047380951 2.92709867 9.16766324 0.0000000
## hyl-2:5-fat-3:1 6.378888889 3.25860661 9.49917117 0.0000000
## lagr-1:5-fat-3:1 5.031666666 1.91138438 8.15194895 0.0000022
## sphk-1:5-fat-3:1 4.444285714 1.32400343 7.56456800 0.0000783
## sms-1:5-fat-3:1 5.997619047 2.87733676 9.11790133 0.0000000
## elo-6:5-fat-3:1 5.330952381 2.21067010 8.45123467 0.0000003
## fat-2:5-fat-3:1 7.168611111 4.04832883 10.28889340 0.0000000
## fat-3:5-fat-3:1 8.807936507 5.68765422 11.92821879 0.0000000
## fat-4:5-fat-3:1 7.607619048 4.48733676 10.72790133 0.0000000
## fat-5:5-fat-3:1 7.098333333 3.97805105 10.21861562 0.0000000
## fat-7:5-fat-3:1 5.626666667 2.50638438 8.74694895 0.0000000
## fat-5:1-fat-4:1 0.108333333 -3.01194895 3.22861562 1.0000000
## fat-7:1-fat-4:1 0.213333334 -2.90694895 3.33361562 1.0000000
## L4440:5-fat-4:1 6.039900793 3.33765707 8.74214452 0.0000000
## asm-3:5-fat-4:1 6.990277778 3.86999549 10.11056006 0.0000000
## hyl-1:5-fat-4:1 6.159047618 3.03876533 9.27932990 0.0000000
## hyl-2:5-fat-4:1 6.490555556 3.37027327 9.61083784 0.0000000
## lagr-1:5-fat-4:1 5.143333333 2.02305105 8.26361562 0.0000011
## sphk-1:5-fat-4:1 4.555952381 1.43567010 7.67623467 0.0000408
## sms-1:5-fat-4:1 6.109285714 2.98900343 9.22956800 0.0000000
## elo-6:5-fat-4:1 5.442619048 2.32233676 8.56290133 0.0000001
## fat-2:5-fat-4:1 7.280277778 4.15999549 10.40056006 0.0000000
## fat-3:5-fat-4:1 8.919603174 5.79932089 12.03988546 0.0000000
## fat-4:5-fat-4:1 7.719285715 4.59900343 10.83956800 0.0000000
## fat-5:5-fat-4:1 7.210000000 4.08971772 10.33028228 0.0000000
## fat-7:5-fat-4:1 5.738333334 2.61805105 8.85861562 0.0000000
## fat-7:1-fat-5:1 0.105000000 -3.01528228 3.22528228 1.0000000
## L4440:5-fat-5:1 5.931567460 3.22932373 8.63381118 0.0000000
## asm-3:5-fat-5:1 6.881944445 3.76166216 10.00222673 0.0000000
## hyl-1:5-fat-5:1 6.050714285 2.93043200 9.17099657 0.0000000
## hyl-2:5-fat-5:1 6.382222223 3.26193994 9.50250451 0.0000000
## lagr-1:5-fat-5:1 5.035000000 1.91471772 8.15528228 0.0000022
## sphk-1:5-fat-5:1 4.447619048 1.32733676 7.56790133 0.0000768
## sms-1:5-fat-5:1 6.000952381 2.88067010 9.12123466 0.0000000
## elo-6:5-fat-5:1 5.334285715 2.21400343 8.45456800 0.0000003
## fat-2:5-fat-5:1 7.171944445 4.05166216 10.29222673 0.0000000
## fat-3:5-fat-5:1 8.811269840 5.69098756 11.93155212 0.0000000
## fat-4:5-fat-5:1 7.610952381 4.49067010 10.73123467 0.0000000
## fat-5:5-fat-5:1 7.101666667 3.98138438 10.22194895 0.0000000
## fat-7:5-fat-5:1 5.630000000 2.50971772 8.75028228 0.0000000
## L4440:5-fat-7:1 5.826567460 3.12432373 8.52881118 0.0000000
## asm-3:5-fat-7:1 6.776944445 3.65666216 9.89722673 0.0000000
## hyl-1:5-fat-7:1 5.945714285 2.82543200 9.06599657 0.0000000
## hyl-2:5-fat-7:1 6.277222223 3.15693994 9.39750451 0.0000000
## lagr-1:5-fat-7:1 4.930000000 1.80971772 8.05028228 0.0000042
## sphk-1:5-fat-7:1 4.342619048 1.22233676 7.46290133 0.0001398
## sms-1:5-fat-7:1 5.895952381 2.77567010 9.01623466 0.0000000
## elo-6:5-fat-7:1 5.229285715 2.10900343 8.34956800 0.0000006
## fat-2:5-fat-7:1 7.066944445 3.94666216 10.18722673 0.0000000
## fat-3:5-fat-7:1 8.706269840 5.58598756 11.82655212 0.0000000
## fat-4:5-fat-7:1 7.505952381 4.38567010 10.62623467 0.0000000
## fat-5:5-fat-7:1 6.996666667 3.87638438 10.11694895 0.0000000
## fat-7:5-fat-7:1 5.525000000 2.40471772 8.64528228 0.0000001
## asm-3:5-L4440:5 0.950376985 -1.75186674 3.65262071 0.9999111
## hyl-1:5-L4440:5 0.119146825 -2.58309690 2.82139055 1.0000000
## hyl-2:5-L4440:5 0.450654763 -2.25158896 3.15289849 1.0000000
## lagr-1:5-L4440:5 -0.896567460 -3.59881118 1.80567626 0.9999690
## sphk-1:5-L4440:5 -1.483948412 -4.18619214 1.21829531 0.9471317
## sms-1:5-L4440:5 0.069384921 -2.63285880 2.77162865 1.0000000
## elo-6:5-L4440:5 -0.597281745 -3.29952547 2.10496198 1.0000000
## fat-2:5-L4440:5 1.240376985 -1.46186674 3.94262071 0.9939755
## fat-3:5-L4440:5 2.879702381 0.17745866 5.58194611 0.0220084
## fat-4:5-L4440:5 1.679384921 -1.02285880 4.38162865 0.8355334
## fat-5:5-L4440:5 1.170099207 -1.53214452 3.87234293 0.9973739
## fat-7:5-L4440:5 -0.301567459 -3.00381118 2.40067627 1.0000000
## hyl-1:5-asm-3:5 -0.831230160 -3.95151244 2.28905212 0.9999996
## hyl-2:5-asm-3:5 -0.499722222 -3.62000451 2.62056006 1.0000000
## lagr-1:5-asm-3:5 -1.846944445 -4.96722673 1.27333784 0.8908754
## sphk-1:5-asm-3:5 -2.434325397 -5.55460768 0.68595689 0.4020219
## sms-1:5-asm-3:5 -0.880992064 -4.00127435 2.23929022 0.9999986
## elo-6:5-asm-3:5 -1.547658730 -4.66794101 1.57262355 0.9834283
## fat-2:5-asm-3:5 0.290000000 -2.83028228 3.41028228 1.0000000
## fat-3:5-asm-3:5 1.929325396 -1.19095689 5.04960768 0.8420856
## fat-4:5-asm-3:5 0.729007936 -2.39127435 3.84929022 1.0000000
## fat-5:5-asm-3:5 0.219722222 -2.90056006 3.34000451 1.0000000
## fat-7:5-asm-3:5 -1.251944444 -4.37222673 1.86833784 0.9991833
## hyl-2:5-hyl-1:5 0.331507938 -2.78877435 3.45179022 1.0000000
## lagr-1:5-hyl-1:5 -1.015714285 -4.13599657 2.10456800 0.9999782
## sphk-1:5-hyl-1:5 -1.603095237 -4.72337752 1.51718705 0.9746927
## sms-1:5-hyl-1:5 -0.049761904 -3.17004419 3.07052038 1.0000000
## elo-6:5-hyl-1:5 -0.716428570 -3.83671085 2.40385371 1.0000000
## fat-2:5-hyl-1:5 1.121230160 -1.99905212 4.24151244 0.9998703
## fat-3:5-hyl-1:5 2.760555556 -0.35972673 5.88083784 0.1716461
## fat-4:5-hyl-1:5 1.560238096 -1.56004419 4.68052038 0.9816957
## fat-5:5-hyl-1:5 1.050952382 -2.06932990 4.17123467 0.9999591
## fat-7:5-hyl-1:5 -0.420714284 -3.54099657 2.69956800 1.0000000
## lagr-1:5-hyl-2:5 -1.347222223 -4.46750451 1.77306006 0.9974833
## sphk-1:5-hyl-2:5 -1.934603175 -5.05488546 1.18567911 0.8385973
## sms-1:5-hyl-2:5 -0.381269842 -3.50155213 2.73901244 1.0000000
## elo-6:5-hyl-2:5 -1.047936508 -4.16821879 2.07234578 0.9999612
## fat-2:5-hyl-2:5 0.789722222 -2.33056006 3.91000451 0.9999999
## fat-3:5-hyl-2:5 2.429047618 -0.69123467 5.54932990 0.4066463
## fat-4:5-hyl-2:5 1.228730158 -1.89155213 4.34901244 0.9993951
## fat-5:5-hyl-2:5 0.719444444 -2.40083784 3.83972673 1.0000000
## fat-7:5-hyl-2:5 -0.752222222 -3.87250451 2.36806006 0.9999999
## sphk-1:5-lagr-1:5 -0.587380952 -3.70766324 2.53290133 1.0000000
## sms-1:5-lagr-1:5 0.965952381 -2.15432990 4.08623467 0.9999916
## elo-6:5-lagr-1:5 0.299285715 -2.82099657 3.41956800 1.0000000
## fat-2:5-lagr-1:5 2.136944445 -0.98333784 5.25722673 0.6772820
## fat-3:5-lagr-1:5 3.776269841 0.65598756 6.89655212 0.0028053
## fat-4:5-lagr-1:5 2.575952381 -0.54432990 5.69623467 0.2874844
## fat-5:5-lagr-1:5 2.066666667 -1.05361562 5.18694895 0.7386008
## fat-7:5-lagr-1:5 0.595000001 -2.52528228 3.71528228 1.0000000
## sms-1:5-sphk-1:5 1.553333333 -1.56694895 4.67361562 0.9826637
## elo-6:5-sphk-1:5 0.886666667 -2.23361562 4.00694895 0.9999984
## fat-2:5-sphk-1:5 2.724325397 -0.39595689 5.84460768 0.1912082
## fat-3:5-sphk-1:5 4.363650793 1.24336851 7.48393308 0.0001241
## fat-4:5-sphk-1:5 3.163333333 0.04305105 6.28361562 0.0423606
## fat-5:5-sphk-1:5 2.654047619 -0.46623467 5.77432990 0.2335730
## fat-7:5-sphk-1:5 1.182380953 -1.93790133 4.30266324 0.9996787
## elo-6:5-sms-1:5 -0.666666666 -3.78694895 2.45361562 1.0000000
## fat-2:5-sms-1:5 1.170992064 -1.94929022 4.29127435 0.9997269
## fat-3:5-sms-1:5 2.810317460 -0.30996482 5.93059974 0.1472398
## fat-4:5-sms-1:5 1.610000000 -1.51028228 4.73028228 0.9733922
## fat-5:5-sms-1:5 1.100714286 -2.01956800 4.22099657 0.9999062
## fat-7:5-sms-1:5 -0.370952380 -3.49123466 2.74932990 1.0000000
## fat-2:5-elo-6:5 1.837658730 -1.28262355 4.95794101 0.8956950
## fat-3:5-elo-6:5 3.476984126 0.35670184 6.59726641 0.0114152
## fat-4:5-elo-6:5 2.276666666 -0.84361562 5.39694895 0.5469612
## fat-5:5-elo-6:5 1.767380952 -1.35290133 4.88766324 0.9277193
## fat-7:5-elo-6:5 0.295714286 -2.82456800 3.41599657 1.0000000
## fat-3:5-fat-2:5 1.639325396 -1.48095689 4.75960768 0.9672855
## fat-4:5-fat-2:5 0.439007936 -2.68127435 3.55929022 1.0000000
## fat-5:5-fat-2:5 -0.070277778 -3.19056006 3.05000451 1.0000000
## fat-7:5-fat-2:5 -1.541944444 -4.66222673 1.57833784 0.9841708
## fat-4:5-fat-3:5 -1.200317459 -4.32059974 1.91996482 0.9995874
## fat-5:5-fat-3:5 -1.709603174 -4.82988546 1.41067911 0.9483779
## fat-7:5-fat-3:5 -3.181269840 -6.30155212 -0.06098756 0.0394912
## fat-5:5-fat-4:5 -0.509285714 -3.62956800 2.61099657 1.0000000
## fat-7:5-fat-4:5 -1.980952381 -5.10123466 1.13932990 0.8061825
## fat-7:5-fat-5:5 -1.471666666 -4.59194895 1.64861562 0.9913081d <- c$`RNAi:age`
write.csv(d, file = "size.csv")Laphat’s thrashing data
thrash <- read.csv("thrashing.csv")
str(thrash)## 'data.frame': 141 obs. of 2 variables:
## $ RNAi : chr "L4440" "L4440" "L4440" "L4440" ...
## $ movement: int 40 43 38 38 40 36 41 40 39 39 ...summarySE(thrash,
measurevar="movement",
groupvars=c("RNAi"))## RNAi N movement sd se ci
## 1 asm-3 49 43.46939 3.470723 0.4958176 0.9969081
## 2 hyl-2 45 28.68889 2.794656 0.4166027 0.8396076
## 3 L4440 47 39.53191 2.500879 0.3647906 0.7342855thrash$RNAi = factor(thrash$RNAi, levels = c("L4440", "asm-3", "hyl-2"))
levels(thrash$RNAi)## [1] "L4440" "asm-3" "hyl-2"ggplot(thrash, aes(y=movement, x=RNAi, fill=RNAi)) +
geom_boxplot() +
scale_fill_manual(values=c("black", "#DDAD4B", "cornflowerblue")) +
geom_dotplot(aes(x=RNAi, y=movement),
binaxis = "y", stackdir = "center",
position=position_dodge(0.8),
dotsize = 0.7, colour = "white")## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
stats for thrashing
thrash.lm <- lm(movement~RNAi, data=thrash)
anova(thrash.lm)## Analysis of Variance Table
##
## Response: movement
## Df Sum Sq Mean Sq F value Pr(>F)
## RNAi 2 5433.2 2716.59 309.94 < 2.2e-16 ***
## Residuals 138 1209.6 8.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(movement~RNAi, data=thrash))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = movement ~ RNAi, data = thrash)
##
## $RNAi
## diff lwr upr p adj
## asm-3-L4440 3.937473 2.505364 5.369582 0
## hyl-2-L4440 -10.843026 -12.305965 -9.380087 0
## hyl-2-asm-3 -14.780499 -16.228761 -13.332237 0qPCR
qPCR <- read.csv("autophagyqpcrgraph.csv")
str(qPCR)## 'data.frame': 42 obs. of 5 variables:
## $ gene : chr "rpl-2" "rpl-2" "rpl-2" "rpl-2" ...
## $ animal: chr "N2" "N2" "hyl-2" "hyl-2" ...
## $ age : int 1 10 1 10 1 10 1 10 1 10 ...
## $ X2ddct: num 1 0.946 0.979 0.839 1.454 ...
## $ se : num 0.465 0.45 0.665 0.508 0.831 ...first visualize with a bar plot
qPCR2 <- qPCR[-c(1:6),]
str(qPCR2)## 'data.frame': 36 obs. of 5 variables:
## $ gene : chr "lgg-1" "lgg-1" "lgg-1" "lgg-1" ...
## $ animal: chr "N2" "N2" "hyl-2" "hyl-2" ...
## $ age : int 1 10 1 10 1 10 1 10 1 10 ...
## $ X2ddct: num 1 1.39 1.56 1.8 1.64 ...
## $ se : num 0.304 0.223 0.436 0.505 0.823 ...qPCR2$age <- as.factor(qPCR2$age)
qPCR2$animal = factor(qPCR2$animal, levels = c("N2", "asm-3", "hyl-2"))
levels(qPCR2$gene) <- c("atg-7", "atg-18", "bec-1", "lgg-1", "unc-51", "hlh-30")
ggplot(qPCR2, aes(x=animal, y=X2ddct, fill=age)) +
geom_bar(stat="identity", color="black",
position=position_dodge()) +
geom_errorbar(aes(ymin=X2ddct-se, ymax=X2ddct+se), width=.2,
position=position_dodge(.9)) +
facet_wrap(~gene, ncol = 6)
ggplot(qPCR2, aes(x=animal, y=X2ddct, fill=age)) +
geom_bar(stat="identity", color="black",
position=position_dodge()) +
geom_errorbar(aes(ymin=X2ddct-se, ymax=X2ddct+se), width=.2,
position=position_dodge(.9)) +
facet_wrap(~gene)
Stats
qPCRstats <- read.csv("autophagyqpcr.csv")
str(qPCRstats)## 'data.frame': 126 obs. of 5 variables:
## $ gene : chr "rpl-2" "rpl-2" "rpl-2" "rpl-2" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : int 1 1 1 1 1 1 1 1 1 10 ...
## $ dct : num 1.34 1.38 1.01 1.75 1.06 1.01 -0.4 0.77 1.74 1.85 ...
## $ X : logi NA NA NA NA NA NA ...qPCRstats$age <- as.factor(qPCRstats$age)rpl-2
rpl <- qPCRstats[qPCRstats$gene=="rpl-2",]
str(rpl)## 'data.frame': 18 obs. of 5 variables:
## $ gene : chr "rpl-2" "rpl-2" "rpl-2" "rpl-2" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : Factor w/ 2 levels "1","10": 1 1 1 1 1 1 1 1 1 2 ...
## $ dct : num 1.34 1.38 1.01 1.75 1.06 1.01 -0.4 0.77 1.74 1.85 ...
## $ X : logi NA NA NA NA NA NA ...rpl.lm <- lm(dct~strain*age, data=rpl)
anova(rpl.lm)## Analysis of Variance Table
##
## Response: dct
## Df Sum Sq Mean Sq F value Pr(>F)
## strain 2 0.7966 0.39832 1.3852 0.2876
## age 1 0.2381 0.23805 0.8279 0.3808
## strain:age 2 0.0706 0.03532 0.1228 0.8855
## Residuals 12 3.4505 0.28754TukeyHSD(aov(dct~strain*age, data=rpl))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dct ~ strain * age, data = rpl)
##
## $strain
## diff lwr upr p adj
## hyl-2-asm-3 0.4883333 -0.3376200 1.3142866 0.2923098
## N2-asm-3 0.3866667 -0.4392866 1.2126200 0.4487825
## N2-hyl-2 -0.1016667 -0.9276200 0.7242866 0.9425688
##
## $age
## diff lwr upr p adj
## 10-1 0.23 -0.3207648 0.7807648 0.3808012
##
## $`strain:age`
## diff lwr upr p adj
## hyl-2:1-asm-3:1 0.5700000 -0.9006409 2.040641 0.7789431
## N2:1-asm-3:1 0.5400000 -0.9306409 2.010641 0.8130781
## asm-3:10-asm-3:1 0.3866667 -1.0839742 1.857308 0.9435637
## hyl-2:10-asm-3:1 0.7933333 -0.6773075 2.263974 0.4935359
## N2:10-asm-3:1 0.6200000 -0.8506409 2.090641 0.7178842
## N2:1-hyl-2:1 -0.0300000 -1.5006409 1.440641 0.9999997
## asm-3:10-hyl-2:1 -0.1833333 -1.6539742 1.287308 0.9979125
## hyl-2:10-hyl-2:1 0.2233333 -1.2473075 1.693974 0.9947471
## N2:10-hyl-2:1 0.0500000 -1.4206409 1.520641 0.9999964
## asm-3:10-N2:1 -0.1533333 -1.6239742 1.317308 0.9991106
## hyl-2:10-N2:1 0.2533333 -1.2173075 1.723974 0.9906664
## N2:10-N2:1 0.0800000 -1.3906409 1.550641 0.9999632
## hyl-2:10-asm-3:10 0.4066667 -1.0639742 1.877308 0.9313181
## N2:10-asm-3:10 0.2333333 -1.2373075 1.703974 0.9935761
## N2:10-hyl-2:10 -0.1733333 -1.6439742 1.297308 0.9984005lgg-1
lgg <- qPCRstats[qPCRstats$gene=="lgg-1",]
str(lgg)## 'data.frame': 18 obs. of 5 variables:
## $ gene : chr "lgg-1" "lgg-1" "lgg-1" "lgg-1" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : Factor w/ 2 levels "1","10": 1 1 1 1 1 1 1 1 1 2 ...
## $ dct : num 3.34 3.39 3.74 2.72 2.65 3.17 1.73 2.72 3.88 3.1 ...
## $ X : logi NA NA NA NA NA NA ...lgg.lm <- lm(dct~strain*age, data=lgg)
anova(lgg.lm)## Analysis of Variance Table
##
## Response: dct
## Df Sum Sq Mean Sq F value Pr(>F)
## strain 2 1.14431 0.57216 2.3257 0.1401
## age 1 0.34445 0.34445 1.4001 0.2596
## strain:age 2 0.08573 0.04287 0.1742 0.8422
## Residuals 12 2.95220 0.24602TukeyHSD(aov(dct~strain*age, data=lgg))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dct ~ strain * age, data = lgg)
##
## $strain
## diff lwr upr p adj
## hyl-2-asm-3 0.04666667 -0.7173191 0.8106524 0.9854838
## N2-asm-3 0.55666667 -0.2073191 1.3206524 0.1689679
## N2-hyl-2 0.51000000 -0.2539858 1.2739858 0.2172533
##
## $age
## diff lwr upr p adj
## 10-1 -0.2766667 -0.7861101 0.2327767 0.2596175
##
## $`strain:age`
## diff lwr upr p adj
## hyl-2:1-asm-3:1 0.07000000 -1.2903054 1.4303054 0.9999720
## N2:1-asm-3:1 0.71333333 -0.6469720 2.0736387 0.5215300
## asm-3:10-asm-3:1 -0.15666667 -1.5169720 1.2036387 0.9985673
## hyl-2:10-asm-3:1 -0.13333333 -1.4936387 1.2269720 0.9993398
## N2:10-asm-3:1 0.24333333 -1.1169720 1.6036387 0.9889446
## N2:1-hyl-2:1 0.64333333 -0.7169720 2.0036387 0.6200276
## asm-3:10-hyl-2:1 -0.22666667 -1.5869720 1.1336387 0.9919688
## hyl-2:10-hyl-2:1 -0.20333333 -1.5636387 1.1569720 0.9951173
## N2:10-hyl-2:1 0.17333333 -1.1869720 1.5336387 0.9976848
## asm-3:10-N2:1 -0.87000000 -2.2303054 0.4903054 0.3267829
## hyl-2:10-N2:1 -0.84666667 -2.2069720 0.5136387 0.3524902
## N2:10-N2:1 -0.47000000 -1.8303054 0.8903054 0.8467100
## hyl-2:10-asm-3:10 0.02333333 -1.3369720 1.3836387 0.9999999
## N2:10-asm-3:10 0.40000000 -0.9603054 1.7603054 0.9133364
## N2:10-hyl-2:10 0.37666667 -0.9836387 1.7369720 0.9309592unc-51
unc <- qPCRstats[qPCRstats$gene=="unc-51",]
str(unc)## 'data.frame': 18 obs. of 5 variables:
## $ gene : chr "unc-51" "unc-51" "unc-51" "unc-51" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : Factor w/ 2 levels "1","10": 1 1 1 1 1 1 1 1 1 2 ...
## $ dct : num 8.13 7.93 7.9 7.64 7.12 7.87 6.39 7.53 8.58 7.84 ...
## $ X : logi NA NA NA NA NA NA ...unc.lm <- lm(dct~strain*age, data=unc)
anova(unc.lm)## Analysis of Variance Table
##
## Response: dct
## Df Sum Sq Mean Sq F value Pr(>F)
## strain 2 1.4582 0.72911 2.6533 0.111124
## age 1 2.7534 2.75342 10.0201 0.008138 **
## strain:age 2 0.3281 0.16407 0.5971 0.565972
## Residuals 12 3.2975 0.27479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(dct~strain*age, data=unc))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dct ~ strain * age, data = unc)
##
## $strain
## diff lwr upr p adj
## hyl-2-asm-3 -0.2700000 -1.0774257 0.5374257 0.6552413
## N2-asm-3 0.4216667 -0.3857591 1.2290924 0.3748617
## N2-hyl-2 0.6916667 -0.1157591 1.4990924 0.0967175
##
## $age
## diff lwr upr p adj
## 10-1 -0.7822222 -1.320632 -0.2438121 0.0081379
##
## $`strain:age`
## diff lwr upr p adj
## hyl-2:1-asm-3:1 0.04333333 -1.3943185 1.4809852 0.9999980
## N2:1-asm-3:1 0.48666667 -0.9509852 1.9243185 0.8569107
## asm-3:10-asm-3:1 -0.53000000 -1.9676518 0.9076518 0.8106940
## hyl-2:10-asm-3:1 -1.11333333 -2.5509852 0.3243185 0.1703211
## N2:10-asm-3:1 -0.17333333 -1.6109852 1.2643185 0.9982183
## N2:1-hyl-2:1 0.44333333 -0.9943185 1.8809852 0.8968185
## asm-3:10-hyl-2:1 -0.57333333 -2.0109852 0.8643185 0.7593104
## hyl-2:10-hyl-2:1 -1.15666667 -2.5943185 0.2809852 0.1456563
## N2:10-hyl-2:1 -0.21666667 -1.6543185 1.2209852 0.9949285
## asm-3:10-N2:1 -1.01666667 -2.4543185 0.4209852 0.2383644
## hyl-2:10-N2:1 -1.60000000 -3.0376518 -0.1623482 0.0263884
## N2:10-N2:1 -0.66000000 -2.0976518 0.7776518 0.6467522
## hyl-2:10-asm-3:10 -0.58333333 -2.0209852 0.8543185 0.7468634
## N2:10-asm-3:10 0.35666667 -1.0809852 1.7943185 0.9552429
## N2:10-hyl-2:10 0.94000000 -0.4976518 2.3776518 0.3063809bec-1
bec <- qPCRstats[qPCRstats$gene=="bec-1",]
str(bec)## 'data.frame': 18 obs. of 5 variables:
## $ gene : chr "bec-1" "bec-1" "bec-1" "bec-1" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : Factor w/ 2 levels "1","10": 1 1 1 1 1 1 1 1 1 2 ...
## $ dct : num 9.63 9.74 9.16 8.3 8.37 8.18 6.77 8.29 8.47 8.39 ...
## $ X : logi NA NA NA NA NA NA ...bec.lm <- lm(dct~strain*age, data=bec)
anova(bec.lm)## Analysis of Variance Table
##
## Response: dct
## Df Sum Sq Mean Sq F value Pr(>F)
## strain 2 3.14098 1.57049 6.3538 0.01312 *
## age 1 0.64601 0.64601 2.6136 0.13192
## strain:age 2 1.52818 0.76409 3.0913 0.08263 .
## Residuals 12 2.96607 0.24717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(dct~strain*age, data=bec))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dct ~ strain * age, data = bec)
##
## $strain
## diff lwr upr p adj
## hyl-2-asm-3 0.1733333 -0.59244458 0.9391112 0.8207117
## N2-asm-3 0.9600000 0.19422209 1.7257779 0.0149370
## N2-hyl-2 0.7866667 0.02088876 1.5524446 0.0439806
##
## $age
## diff lwr upr p adj
## 10-1 -0.3788889 -0.8895273 0.1317495 0.1319204
##
## $`strain:age`
## diff lwr upr p adj
## hyl-2:1-asm-3:1 0.44000000 -0.9234963 1.80349633 0.8786946
## N2:1-asm-3:1 1.66666667 0.3031703 3.03016300 0.0141870
## asm-3:10-asm-3:1 0.27000000 -1.0934963 1.63349633 0.9826788
## hyl-2:10-asm-3:1 0.17666667 -1.1868297 1.54016300 0.9974950
## N2:10-asm-3:1 0.52333333 -0.8401630 1.88682967 0.7854119
## N2:1-hyl-2:1 1.22666667 -0.1368297 2.59016300 0.0873838
## asm-3:10-hyl-2:1 -0.17000000 -1.5334963 1.19349633 0.9979112
## hyl-2:10-hyl-2:1 -0.26333333 -1.6268297 1.10016300 0.9844736
## N2:10-hyl-2:1 0.08333333 -1.2801630 1.44682967 0.9999345
## asm-3:10-N2:1 -1.39666667 -2.7601630 -0.03317033 0.0435945
## hyl-2:10-N2:1 -1.49000000 -2.8534963 -0.12650367 0.0295860
## N2:10-N2:1 -1.14333333 -2.5068297 0.22016300 0.1216810
## hyl-2:10-asm-3:10 -0.09333333 -1.4568297 1.27016300 0.9998855
## N2:10-asm-3:10 0.25333333 -1.1101630 1.61682967 0.9869111
## N2:10-hyl-2:10 0.34666667 -1.0168297 1.71016300 0.9506040atg-7
atg <- qPCRstats[qPCRstats$gene=="atg-7",]
str(atg)## 'data.frame': 18 obs. of 5 variables:
## $ gene : chr "atg-7" "atg-7" "atg-7" "atg-7" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : Factor w/ 2 levels "1","10": 1 1 1 1 1 1 1 1 1 2 ...
## $ dct : num 6.81 7.26 7.03 6.75 6.72 6.69 5.53 6.44 7.23 7.16 ...
## $ X : logi NA NA NA NA NA NA ...atg.lm <- lm(dct~strain*age, data=atg)
anova(atg.lm)## Analysis of Variance Table
##
## Response: dct
## Df Sum Sq Mean Sq F value Pr(>F)
## strain 2 0.45508 0.22754 1.533 0.25533
## age 1 0.73205 0.73205 4.932 0.04637 *
## strain:age 2 0.24343 0.12172 0.820 0.46365
## Residuals 12 1.78113 0.14843
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(dct~strain*age, data=atg))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dct ~ strain * age, data = atg)
##
## $strain
## diff lwr upr p adj
## hyl-2-asm-3 0.2866667 -0.3067512 0.8800845 0.4276355
## N2-asm-3 0.3716667 -0.2217512 0.9650845 0.2557006
## N2-hyl-2 0.0850000 -0.5084178 0.6784178 0.9231649
##
## $age
## diff lwr upr p adj
## 10-1 0.4033333 0.007628589 0.7990381 0.0463703
##
## $`strain:age`
## diff lwr upr p adj
## hyl-2:1-asm-3:1 0.32000000 -0.7366027 1.3766027 0.9033724
## N2:1-asm-3:1 0.63333333 -0.4232694 1.6899361 0.3889628
## asm-3:10-asm-3:1 0.60000000 -0.4566027 1.6566027 0.4424328
## hyl-2:10-asm-3:1 0.85333333 -0.2032694 1.9099361 0.1433314
## N2:10-asm-3:1 0.71000000 -0.3466027 1.7666027 0.2818238
## N2:1-hyl-2:1 0.31333333 -0.7432694 1.3699361 0.9105745
## asm-3:10-hyl-2:1 0.28000000 -0.7766027 1.3366027 0.9417900
## hyl-2:10-hyl-2:1 0.53333333 -0.5232694 1.5899361 0.5587370
## N2:10-hyl-2:1 0.39000000 -0.6666027 1.4466027 0.8099604
## asm-3:10-N2:1 -0.03333333 -1.0899361 1.0232694 0.9999975
## hyl-2:10-N2:1 0.22000000 -0.8366027 1.2766027 0.9784723
## N2:10-N2:1 0.07666667 -0.9799361 1.1332694 0.9998476
## hyl-2:10-asm-3:10 0.25333333 -0.8032694 1.3099361 0.9610506
## N2:10-asm-3:10 0.11000000 -0.9466027 1.1666027 0.9991170
## N2:10-hyl-2:10 -0.14333333 -1.1999361 0.9132694 0.9968919atg-18
atg2 <- qPCRstats[qPCRstats$gene=="atg-18",]
str(atg2)## 'data.frame': 18 obs. of 5 variables:
## $ gene : chr "atg-18" "atg-18" "atg-18" "atg-18" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : Factor w/ 2 levels "1","10": 1 1 1 1 1 1 1 1 1 2 ...
## $ dct : num 5.47 5.38 5.23 5.02 4.62 5 3.51 4.43 5.27 6.53 ...
## $ X : logi NA NA NA NA NA NA ...atg2.lm <- lm(dct~strain*age, data=atg2)
anova(atg2.lm)## Analysis of Variance Table
##
## Response: dct
## Df Sum Sq Mean Sq F value Pr(>F)
## strain 2 1.8463 0.9232 3.3612 0.069330 .
## age 1 3.3627 3.3627 12.2435 0.004389 **
## strain:age 2 0.1704 0.0852 0.3102 0.738984
## Residuals 12 3.2958 0.2746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(dct~strain*age, data=atg2))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dct ~ strain * age, data = atg2)
##
## $strain
## diff lwr upr p adj
## hyl-2-asm-3 0.255 -0.55222166 1.062222 0.6847203
## N2-asm-3 0.770 -0.03722166 1.577222 0.0620023
## N2-hyl-2 0.515 -0.29222166 1.322222 0.2443174
##
## $age
## diff lwr upr p adj
## 10-1 0.8644444 0.3261704 1.402719 0.0043893
##
## $`strain:age`
## diff lwr upr p adj
## hyl-2:1-asm-3:1 0.47666667 -0.9606218 1.913955 0.8665984
## N2:1-asm-3:1 0.95666667 -0.4806218 2.393955 0.2902724
## asm-3:10-asm-3:1 1.13666667 -0.3006218 2.573955 0.1564605
## hyl-2:10-asm-3:1 1.17000000 -0.2672885 2.607288 0.1385763
## N2:10-asm-3:1 1.72000000 0.2827115 3.157288 0.0164039
## N2:1-hyl-2:1 0.48000000 -0.9572885 1.917288 0.8633665
## asm-3:10-hyl-2:1 0.66000000 -0.7772885 2.097288 0.6465287
## hyl-2:10-hyl-2:1 0.69333333 -0.7439551 2.130622 0.6017599
## N2:10-hyl-2:1 1.24333333 -0.1939551 2.680622 0.1055079
## asm-3:10-N2:1 0.18000000 -1.2572885 1.617288 0.9978667
## hyl-2:10-N2:1 0.21333333 -1.2239551 1.650622 0.9952737
## N2:10-N2:1 0.76333333 -0.6739551 2.200622 0.5090036
## hyl-2:10-asm-3:10 0.03333333 -1.4039551 1.470622 0.9999995
## N2:10-asm-3:10 0.58333333 -0.8539551 2.020622 0.7466784
## N2:10-hyl-2:10 0.55000000 -0.8872885 1.987288 0.7873756hlh-30
hlh <- qPCRstats[qPCRstats$gene=="hlh-30",]
str(hlh)## 'data.frame': 18 obs. of 5 variables:
## $ gene : chr "hlh-30" "hlh-30" "hlh-30" "hlh-30" ...
## $ strain: chr "N2" "N2" "N2" "hyl-2" ...
## $ age : Factor w/ 2 levels "1","10": 1 1 1 1 1 1 1 1 1 2 ...
## $ dct : num 13.2 13.1 13.1 12.4 12.5 ...
## $ X : logi NA NA NA NA NA NA ...hlh$age <- as.factor(hlh$age)
hlh.lm <- lm(dct~strain*age, data=hlh)
anova(hlh.lm)## Analysis of Variance Table
##
## Response: dct
## Df Sum Sq Mean Sq F value Pr(>F)
## strain 2 0.8764 0.4382 2.0141 0.1761
## age 1 8.1878 8.1878 37.6333 5.063e-05 ***
## strain:age 2 0.9560 0.4780 2.1971 0.1538
## Residuals 12 2.6108 0.2176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(dct~strain*age, data=hlh))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = dct ~ strain * age, data = hlh)
##
## $strain
## diff lwr upr p adj
## hyl-2-asm-3 -0.41 -1.1284544 0.3084544 0.3154639
## N2-asm-3 0.10 -0.6184544 0.8184544 0.9272618
## N2-hyl-2 0.51 -0.2084544 1.2284544 0.1827566
##
## $age
## diff lwr upr p adj
## 10-1 -1.348889 -1.827971 -0.8698069 5.06e-05
##
## $`strain:age`
## diff lwr upr p adj
## hyl-2:1-asm-3:1 -0.9733333 -2.2525683 0.3059016 0.1825246
## N2:1-asm-3:1 -0.2133333 -1.4925683 1.0659016 0.9919387
## asm-3:10-asm-3:1 -1.9333333 -3.2125683 -0.6540984 0.0028607
## hyl-2:10-asm-3:1 -1.7800000 -3.0592350 -0.5007650 0.0055053
## N2:10-asm-3:1 -1.5200000 -2.7992350 -0.2407650 0.0172122
## N2:1-hyl-2:1 0.7600000 -0.5192350 2.0392350 0.3976776
## asm-3:10-hyl-2:1 -0.9600000 -2.2392350 0.3192350 0.1924171
## hyl-2:10-hyl-2:1 -0.8066667 -2.0859016 0.4725683 0.3400748
## N2:10-hyl-2:1 -0.5466667 -1.8259016 0.7325683 0.7071601
## asm-3:10-N2:1 -1.7200000 -2.9992350 -0.4407650 0.0071417
## hyl-2:10-N2:1 -1.5666667 -2.8459016 -0.2874317 0.0140004
## N2:10-N2:1 -1.3066667 -2.5859016 -0.0274317 0.0443098
## hyl-2:10-asm-3:10 0.1533333 -1.1259016 1.4325683 0.9982672
## N2:10-asm-3:10 0.4133333 -0.8659016 1.6925683 0.8781530
## N2:10-hyl-2:10 0.2600000 -1.0192350 1.5392350 0.9806027