Project measure / variable:   Wiltshire4   Tcells_Dox_ctrl


  STRAIN COMPARISON PLOT
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Wiltshire4 - T cells, percent viability control



  MEASURE SUMMARY
Measure Summary Male
Number of strains tested35 strains
Mean of the strain means100   %
Median of the strain means100   %
SD of the strain means± 0
Coefficient of variation (CV)0
Min–max range of strain means100   –   100   %
Mean sample size per strain4.0   mice


  ANOVA, Q-Q NORMALITY ASSESSMENT
ANOVA summary      
FactorDFSum of squaresMean sum of squaresF valuep value (Pr>F)
strain 34 0.0002 0.0 0.0 1.0
Residuals 102 12274.9803 120.3429


Q-Q normality assessment based on residuals

  


  STRAIN MEANS (UNADJUSTED)
  
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Strain Sex Mean SD N mice SEM CV Min, Max Z score
129S1/SvImJ m 100.0 7.85   4 3.92 0.0785 91.7, 109.0 0.0
129X1/SvJ m 100.0 6.19   4 3.09 0.0619 92.3, 105.0 0.0
A/J m 100.0 4.27   4 2.14 0.0427 97.0, 106.0 0.0
AKR/J m 100.0 0.474   4 0.237 0.00474 99.7, 101.0 0.0
BALB/cByJ m 100.0 2.82   4 1.41 0.0282 97.1, 103.0 0.0
BTBR T+ Itpr3tf/J m 100.0 2.68   4 1.34 0.0268 97.4, 103.0 0.0
C3H/HeJ m 100.0 0.51   4 0.255 0.0051 99.3, 100.0 0.0
C57BL/6J m 100.0 0.995   4 0.498 0.00995 99.3, 101.0 0.0
C57BLKS/J m 100.0 3.95   4 1.97 0.0395 95.3, 104.0 0.0
C57BR/cdJ m 100.0 1.72   4 0.862 0.0172 98.5, 102.0 0.0
C58/J m 100.0 2.12   2   1.5 0.0212 98.5, 102.0 0.0
CBA/J m 100.0 2.87   4 1.43 0.0287 98.0, 104.0 0.0
CZECHII/EiJ m 100.0 4.82   4 2.41 0.0482 92.9, 103.0 0.0
DBA/2J m 100.0 2.18   4 1.09 0.0218 97.1, 102.0 0.0
FVB/NJ m 100.0 1.14   4 0.57 0.0114 99.2, 102.0 0.0
I/LnJ m 100.0 1.64   4 0.818 0.0164 98.6, 102.0 0.0
KK/HlJ m 100.0 0.115   4 0.0577 0.00115 99.9, 100.0 0.0
LG/J m 100.0 10.8   4 5.38 0.108 90.8, 113.0 0.0
LP/J m 100.0 0.641   4 0.32 0.00641 99.3, 101.0 0.0
MA/MyJ m 100.0 0.101   4 0.0505 0.00101 99.9, 100.0 0.0
NOD/ShiLtJ m 100.0 0.926   4 0.463 0.00926 99.1, 101.0 0.0
NON/ShiLtJ m 100.0 7.3   4 3.65 0.073 89.5, 106.0 0.0
NZB/BlNJ m 100.0 1.49   4 0.743 0.0149 97.9, 101.0 0.0
NZO/HlLtJ m 100.0 7.04   4 3.52 0.0704 90.3, 107.0 0.0
NZW/LacJ m 100.0 3.23   4 1.61 0.0323 96.1, 103.0 0.0
PERA/EiJ m 100.0 5.95   4 2.98 0.0595 93.8, 107.0 0.0
PL/J m 100.0 0.387   4 0.194 0.00387 99.5, 100.0 0.0
PWD/PhJ m 100.0 1.33   4 0.667 0.0133 98.1, 101.0 0.0
PWK/PhJ m 100.0 4.18   4 2.09 0.0418 94.0, 104.0 0.0
RIIIS/J m 100.0 8.78   4 4.39 0.0878 94.6, 113.0 0.0
SEA/GnJ m 100.0 0.481   4 0.241 0.00481 99.3, 100.0 0.0
SJL/J m 100.0 6.64   4 3.32 0.0664 91.4, 106.0 0.0
SM/J m 100.0 2.08   4 1.04 0.0208 97.7, 102.0 0.0
SWR/J m 100.0 57.2   4 28.6 0.572 14.2, 130.0 0.0
WSB/EiJ m 100.0 17.6   3 10.2 0.176 81.6, 117.0 0.0


  LEAST SQUARES MEANS (MODEL-ADJUSTED)
Strain Sex Mean SEM UpperCL LowerCL
129S1/SvImJ m 100.0 5.485 110.8796 89.1204
129X1/SvJ m 100.0 5.485 110.8796 89.1204
A/J m 99.9975 5.485 110.8771 89.1179
AKR/J m 100.0025 5.485 110.8821 89.1229
BALB/cByJ m 99.9975 5.485 110.8771 89.1179
BTBR T+ Itpr3tf/J m 99.9975 5.485 110.8771 89.1179
C3H/HeJ m 100.0 5.485 110.8796 89.1204
C57BL/6J m 100.0 5.485 110.8796 89.1204
C57BLKS/J m 99.9975 5.485 110.8771 89.1179
C57BR/cdJ m 100.0 5.485 110.8796 89.1204
C58/J m 100.0 7.757 115.386 84.614
CBA/J m 100.0 5.485 110.8796 89.1204
CZECHII/EiJ m 100.0 5.485 110.8796 89.1204
DBA/2J m 100.0 5.485 110.8796 89.1204
FVB/NJ m 100.0 5.485 110.8796 89.1204
I/LnJ m 100.0 5.485 110.8796 89.1204
KK/HlJ m 100.0 5.485 110.8796 89.1204
LG/J m 100.0 5.485 110.8796 89.1204
LP/J m 100.0025 5.485 110.8821 89.1229
MA/MyJ m 100.0 5.485 110.8796 89.1204
NOD/ShiLtJ m 100.0025 5.485 110.8821 89.1229
NON/ShiLtJ m 100.0 5.485 110.8796 89.1204
NZB/BlNJ m 100.0 5.485 110.8796 89.1204
NZO/HlLtJ m 100.0 5.485 110.8796 89.1204
NZW/LacJ m 100.0 5.485 110.8796 89.1204
PERA/EiJ m 100.0 5.485 110.8796 89.1204
PL/J m 100.0 5.485 110.8796 89.1204
PWD/PhJ m 99.9975 5.485 110.8771 89.1179
PWK/PhJ m 100.0 5.485 110.8796 89.1204
RIIIS/J m 100.0025 5.485 110.8821 89.1229
SEA/GnJ m 100.0 5.485 110.8796 89.1204
SJL/J m 100.0 5.485 110.8796 89.1204
SM/J m 100.0 5.485 110.8796 89.1204
SWR/J m 100.0 5.485 110.8796 89.1204
WSB/EiJ m 100.0 6.3336 112.5626 87.4374




  GWAS USING LINEAR MIXED MODELS