This is a summary of the given data matrix for the first 12 genes.
V1 | V2 | V3 | V4 | V5 | V6 |
---|---|---|---|---|---|
Min. :-12604 | Min. :-44084 | Min. :-44292 | Min. :-41722 | Min. :-30883 | Min. :-39013 |
1st Qu.: 2989 | 1st Qu.: -991 | 1st Qu.: -7291 | 1st Qu.: 2211 | 1st Qu.: 7771 | 1st Qu.:-13232 |
Median : 15994 | Median : 6606 | Median : 14936 | Median : 7893 | Median : 14603 | Median : 9192 |
Mean : 37683 | Mean : 49338 | Mean : 57977 | Mean : 29429 | Mean : 33795 | Mean : 42882 |
3rd Qu.: 44817 | 3rd Qu.: 68296 | 3rd Qu.: 79724 | 3rd Qu.: 45117 | 3rd Qu.: 47537 | 3rd Qu.: 78539 |
Max. :307823 | Max. :440159 | Max. :556646 | Max. :360476 | Max. :235209 | Max. :506643 |
V7 | V8 | V9 | V10 | V11 | V12 |
---|---|---|---|---|---|
Min. : -5795 | Min. :-10572 | Min. : -4450 | Min. :-49930 | Min. :-11622 | Min. :-49517 |
1st Qu.: 4536 | 1st Qu.: 2962 | 1st Qu.: 4015 | 1st Qu.: -3530 | 1st Qu.: -5586 | 1st Qu.: 3550 |
Median : 16088 | Median : 14421 | Median : 12456 | Median : 13522 | Median : 1783 | Median : 29970 |
Mean : 43854 | Mean : 40153 | Mean : 39100 | Mean : 57801 | Mean : 38532 | Mean : 61492 |
3rd Qu.: 59052 | 3rd Qu.: 61603 | 3rd Qu.: 54528 | 3rd Qu.: 78275 | 3rd Qu.: 20032 | 3rd Qu.: 94972 |
Max. :325854 | Max. :207735 | Max. :236775 | Max. :580687 | Max. :533742 | Max. :311919 |
This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.
## Warning in t(log2(t(qcounts + 0.5)/(lib.size + 1) * 1e+06)): NaNs produced
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 |
---|---|---|---|
17.62 | 17.62 | 1.18 | 0.1 |
8.32 | 25.94 | 0.04 | 0.75 |
7.35 | 33.29 | 0.96 | 0 |
6.79 | 40.08 | 0.96 | 0.06 |
5.86 | 45.94 | 0.06 | 0.05 |
5.76 | 51.7 | 1.07 | 0.13 |
4.76 | 56.46 | 0.05 | 0.38 |
4.13 | 60.59 | 0.28 | 0.88 |
3.75 | 64.34 | 0.17 | 0.02 |
3.47 | 67.81 | 7.53 | 0.47 |
3.01 | 70.82 | 0.19 | 0.12 |
2.77 | 73.59 | 0.37 | 0.04 |
2.64 | 76.23 | 0.54 | 0 |
2.31 | 78.54 | 0.08 | 0.79 |
2.24 | 80.78 | 0.95 | 0.24 |
1.93 | 82.71 | 0.23 | 0.82 |
1.76 | 84.47 | 0.2 | 0.44 |
1.65 | 86.12 | 0.77 | 0.01 |
1.4 | 87.52 | 6.91 | 1.56 |
1.4 | 88.92 | 0.02 | 0.01 |
1.23 | 90.15 | 0.38 | 0.54 |
1.13 | 91.28 | 2.2 | 0.14 |
0.97 | 92.25 | 1.83 | 0.02 |
0.87 | 93.12 | 0.35 | 0.72 |
0.84 | 93.96 | 0.03 | 0 |
0.78 | 94.74 | 0.03 | 1.04 |
0.64 | 95.38 | 0.13 | 3.91 |
0.64 | 96.02 | 2.06 | 1.9 |
0.52 | 96.54 | 0.6 | 0.21 |
0.47 | 97.01 | 0.15 | 0.09 |
0.44 | 97.45 | 1.83 | 0.56 |
0.38 | 97.83 | 0.35 | 0.09 |
0.34 | 98.17 | 2.37 | 0.24 |
0.33 | 98.5 | 0.55 | 0.05 |
0.29 | 98.79 | 3.3 | 0.01 |
0.22 | 99.01 | 1.28 | 19.19 |
0.2 | 99.21 | 0.44 | 0.22 |
0.15 | 99.36 | 7.16 | 0.93 |
0.14 | 99.5 | 2.09 | 5.57 |
0.12 | 99.62 | 4.16 | 0.03 |
0.1 | 99.72 | 3.26 | 0.79 |
0.08 | 99.8 | 5.66 | 0.25 |
0.06 | 99.86 | 8.96 | 2.78 |
0.05 | 99.91 | 0.47 | 3 |
0.05 | 99.96 | 0.11 | 0.28 |
0.03 | 99.99 | 2.89 | 0.74 |
0.01 | 100 | 1.05 | 1.72 |
0.01 | 100 | 0.2 | 0.56 |
0.01 | 100 | 5.43 | 2.26 |
0 | 100 | 0.51 | 18.22 |
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 = 0.1314, p-value = 0.3247
## 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
## -3.5145 -1.4447 -0.0521 1.0365 7.8306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.037806 0.501615 -0.075 0.940
## fbatch2 -0.158730 0.614350 -0.258 0.797
## fbatch3 0.260921 0.614350 0.425 0.673
## fcond1 0.007485 0.501615 0.015 0.988
##
## Residual standard error: 1.943 on 56 degrees of freedom
## Multiple R-squared: 0.008429, Adjusted R-squared: -0.04469
## F-statistic: 0.1587 on 3 and 56 DF, p-value: 0.9236
##
##
## Call:
## lm(formula = pc[, 2] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7701 -0.9974 0.1485 1.0928 4.1527
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21686 0.47747 0.454 0.651
## fbatch2 0.05129 0.58477 0.088 0.930
## fbatch3 -0.35070 0.58477 -0.600 0.551
## fcond1 -0.23410 0.47747 -0.490 0.626
##
## Residual standard error: 1.849 on 56 degrees of freedom
## Multiple R-squared: 0.01409, Adjusted R-squared: -0.03873
## F-statistic: 0.2668 on 3 and 56 DF, p-value: 0.8491
##
##
## Call:
## lm(formula = pc[, 3] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9757 -1.0662 -0.0300 0.7505 6.6574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3296 0.4737 0.696 0.489
## fbatch2 -0.3233 0.5801 -0.557 0.580
## fbatch3 -0.1741 0.5801 -0.300 0.765
## fcond1 -0.3276 0.4737 -0.692 0.492
##
## Residual standard error: 1.835 on 56 degrees of freedom
## Multiple R-squared: 0.01391, Adjusted R-squared: -0.03892
## F-statistic: 0.2632 on 3 and 56 DF, p-value: 0.8516
##
##
## Call:
## lm(formula = pc[, 4] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7969 -0.9759 -0.1549 0.6747 5.7618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.056593 0.430820 -0.131 0.896
## fbatch2 -0.148710 0.527645 -0.282 0.779
## fbatch3 0.004913 0.527645 0.009 0.993
## fcond1 0.209051 0.430820 0.485 0.629
##
## Residual standard error: 1.669 on 56 degrees of freedom
## Multiple R-squared: 0.006123, Adjusted R-squared: -0.04712
## F-statistic: 0.115 on 3 and 56 DF, p-value: 0.951
##
##
## Call:
## lm(formula = pc[, 5] ~ fbatch + fcond)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1618 -1.0288 0.1618 0.9011 5.2336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2596 0.4177 -0.621 0.537
## fbatch2 0.2651 0.5115 0.518 0.606
## fbatch3 -0.3842 0.5115 -0.751 0.456
## fcond1 0.5985 0.4177 1.433 0.157
##
## Residual standard error: 1.618 on 56 degrees of freedom
## Multiple R-squared: 0.06171, Adjusted R-squared: 0.01145
## F-statistic: 1.228 on 3 and 56 DF, p-value: 0.3082