This is a summary of the given data matrix for the first 12 genes.
V1 | V2 | V3 | V4 | V5 | V6 |
---|---|---|---|---|---|
Min. : 3.0 | Min. : 0.0 | Min. : 0 | Min. : 0 | Min. : 0 | Min. : 0.0 |
1st Qu.: 952.5 | 1st Qu.: 12.8 | 1st Qu.: 2541 | 1st Qu.: 1231 | 1st Qu.: 2982 | 1st Qu.: 107.2 |
Median : 16976.0 | Median : 2723.5 | Median : 18304 | Median : 5343 | Median : 13666 | Median : 6351.5 |
Mean : 38512.8 | Mean : 50149.8 | Mean : 58634 | Mean : 29463 | Mean : 33469 | Mean : 42320.7 |
3rd Qu.: 43151.0 | 3rd Qu.: 39270.5 | 3rd Qu.: 59952 | 3rd Qu.: 25138 | 3rd Qu.: 38146 | 3rd Qu.: 43330.8 |
Max. :433393.0 | Max. :674276.0 | Max. :825606 | Max. :562960 | Max. :338182 | Max. :773652.0 |
V7 | V8 | V9 | V10 | V11 | V12 |
---|---|---|---|---|---|
Min. : 0 | Min. : 0 | Min. : 0.0 | Min. : 0.0 | Min. : 0.0 | Min. : 0 |
1st Qu.: 3512 | 1st Qu.: 3320 | 1st Qu.: 976.5 | 1st Qu.: 217.8 | 1st Qu.: 333.8 | 1st Qu.: 2430 |
Median : 14372 | Median : 14070 | Median : 11117.5 | Median : 10362.0 | Median : 2640.5 | Median : 26740 |
Mean : 43933 | Mean : 40692 | Mean : 38255.1 | Mean : 58424.7 | Mean : 38351.8 | Mean : 62510 |
3rd Qu.: 54410 | 3rd Qu.: 56977 | 3rd Qu.: 47964.0 | 3rd Qu.: 56310.5 | 3rd Qu.: 20305.8 | 3rd Qu.: 78459 |
Max. :362156 | Max. :238133 | Max. :234333.0 | Max. :780273.0 | Max. :794217.0 | Max. :411485 |
This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.
This is a heatmap of the correlation between samples.
This plot helps identify outlying samples.
This is a plot of the top two principal components and showing the variation with respect to batch effects and different conditions.
Percentage Variation | Cumulative Percentage Variation | Condition Correlation | Batch Correlation |
---|---|---|---|
21.3 | 21.3 | 6.66 | 3.61 |
10.78 | 32.08 | 0.04 | 7.22 |
7.63 | 39.71 | 0.49 | 0.81 |
6.68 | 46.39 | 0.03 | 0.84 |
5.79 | 52.18 | 0.09 | 3.89 |
5.49 | 57.67 | 9.1 | 11.76 |
5.04 | 62.71 | 6.9 | 0.69 |
4.43 | 67.14 | 0.93 | 0.02 |
3.85 | 70.99 | 0.45 | 0.2 |
3.34 | 74.33 | 0 | 3.34 |
3.07 | 77.4 | 0.02 | 3.72 |
2.75 | 80.15 | 0.89 | 4.79 |
2.2 | 82.35 | 0.09 | 0.04 |
1.93 | 84.28 | 0.48 | 0.11 |
1.79 | 86.07 | 1.19 | 9.36 |
1.65 | 87.72 | 1.53 | 2.08 |
1.41 | 89.13 | 0.86 | 1.84 |
1.2 | 90.33 | 0.38 | 2.02 |
1.16 | 91.49 | 0.36 | 0.6 |
0.87 | 92.36 | 4.91 | 0.01 |
0.83 | 93.19 | 8.47 | 0.4 |
0.74 | 93.93 | 0 | 1.9 |
0.72 | 94.65 | 0.08 | 0.08 |
0.65 | 95.3 | 0.44 | 0.8 |
0.56 | 95.86 | 1.72 | 0.03 |
0.55 | 96.41 | 0.14 | 1.66 |
0.47 | 96.88 | 2.51 | 2.34 |
0.4 | 97.28 | 0.43 | 3.44 |
0.36 | 97.64 | 2.2 | 0.5 |
0.32 | 97.96 | 1.95 | 0.28 |
0.28 | 98.24 | 0.12 | 0.14 |
0.26 | 98.5 | 0.01 | 0.72 |
0.22 | 98.72 | 0.02 | 1.42 |
0.2 | 98.92 | 2.07 | 0.02 |
0.19 | 99.11 | 0.98 | 1.18 |
0.17 | 99.28 | 0.7 | 8.36 |
0.14 | 99.42 | 0.44 | 0.99 |
0.12 | 99.54 | 5.25 | 0.9 |
0.1 | 99.64 | 0.29 | 0.25 |
0.08 | 99.72 | 2.06 | 0.06 |
0.05 | 99.77 | 1.51 | 0.81 |
0.05 | 99.82 | 1.6 | 0.53 |
0.04 | 99.86 | 0.06 | 0.16 |
0.03 | 99.89 | 0.01 | 1.01 |
0.03 | 99.92 | 0 | 1.16 |
0.02 | 99.94 | 0.55 | 1.47 |
0.02 | 99.96 | 0.02 | 0.66 |
0.01 | 99.97 | 3.91 | 0.32 |
0 | 99.97 | 0.66 | 0.51 |
0 | 99.97 | 5.59 | 0.06 |
This is a plot showing whether parametric or non-parameteric prior is appropriate for this data. It also shows the Kolmogorov-Smirnov test comparing the parametric and non-parameteric prior distribution.
## Found 3 batches
## Adjusting for 1 covariate(s) or covariate level(s)
## Standardizing Data across genes
## Fitting L/S model and finding priors
##
## One-sample Kolmogorov-Smirnov test
##
## data: gamma.hat[1, ]
## D = 0.1321, p-value = 0.3187
## alternative hypothesis: two-sided
This is a summary of the statistical test for batch effects.
##
## Call:
## lm(formula = pc[, 1] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2124 -0.9805 -0.2123 0.6178 11.7385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.70903 0.53661 -1.321 0.1918
## fbatch2 -0.08874 0.65721 -0.135 0.8931
## fbatch3 1.35003 0.65721 2.054 0.0446 *
## fcond1 0.57721 0.53661 1.076 0.2867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.078 on 56 degrees of freedom
## Multiple R-squared: 0.1136, Adjusted R-squared: 0.06612
## F-statistic: 2.392 on 3 and 56 DF, p-value: 0.07812
##
##
## Call:
## lm(formula = pc[, 2] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5546 -0.5030 0.0587 0.7284 5.7549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06335 0.47433 0.134 0.894
## fbatch2 -0.31186 0.58094 -0.537 0.594
## fbatch3 0.37279 0.58094 0.642 0.524
## fcond1 -0.16733 0.47433 -0.353 0.726
##
## Residual standard error: 1.837 on 56 degrees of freedom
## Multiple R-squared: 0.02637, Adjusted R-squared: -0.02578
## F-statistic: 0.5057 on 3 and 56 DF, p-value: 0.68
##
##
## Call:
## lm(formula = pc[, 3] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9648 -0.9155 0.0228 0.6581 5.2799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.779111 0.430342 1.810 0.0756 .
## fbatch2 -0.793963 0.527059 -1.506 0.1376
## fbatch3 -0.005443 0.527059 -0.010 0.9918
## fcond1 -1.025284 0.430342 -2.382 0.0206 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.667 on 56 degrees of freedom
## Multiple R-squared: 0.1342, Adjusted R-squared: 0.08784
## F-statistic: 2.894 on 3 and 56 DF, p-value: 0.04318
##
##
## Call:
## lm(formula = pc[, 4] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6322 -0.8311 -0.2285 0.4871 7.2334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4656 0.4426 -1.052 0.297
## fbatch2 0.2736 0.5421 0.505 0.616
## fbatch3 0.5671 0.5421 1.046 0.300
## fcond1 0.3708 0.4426 0.838 0.406
##
## Residual standard error: 1.714 on 56 degrees of freedom
## Multiple R-squared: 0.03108, Adjusted R-squared: -0.02082
## F-statistic: 0.5988 on 3 and 56 DF, p-value: 0.6184
##
##
## Call:
## lm(formula = pc[, 5] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7811 -0.6142 0.1258 0.6663 3.5820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7510 0.4236 1.773 0.0817 .
## fbatch2 -0.9589 0.5188 -1.848 0.0699 .
## fbatch3 -0.7490 0.5188 -1.444 0.1544
## fcond1 -0.3634 0.4236 -0.858 0.3947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.641 on 56 degrees of freedom
## Multiple R-squared: 0.07455, Adjusted R-squared: 0.02497
## F-statistic: 1.504 on 3 and 56 DF, p-value: 0.2235