Tests for checking Batch Effects
Batch 151113 | Batch 170203 | Batch 170208 | |
---|---|---|---|
Condition crowned | 12 | 0 | 0 |
Condition worker | 6 | 4 | 2 |
Standardized Pearson Correlation Coefficient | Cramer’s V | |
---|---|---|
Confounding Coefficients (0=no confounding, 1=complete confounding) | 0.7071 | 0.5774 |
Full (Condition+Batch) | Condition | Batch | |
---|---|---|---|
Min. | 0.013 | 0 | 0 |
1st Qu. | 8.312 | 0.838 | 5.114 |
Median | 16.63 | 3.771 | 12.77 |
Mean | 20.28 | 6.33 | 17.57 |
3rd Qu. | 29.03 | 9.73 | 26.58 |
Max. | 97.96 | 46.05 | 97.95 |
Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | Ps<0.05 | |
---|---|---|---|---|---|---|---|
Batch P-values | 0 | 0.07976 | 0.289 | 0.3589 | 0.6046 | 1 | 0.1923 |
Condition P-values | 0.0009045 | 0.354 | 0.5976 | 0.5737 | 0.809 | 1 | 0.01706 |
Boxplots for all values for each of the samples and are colored by batch membership.
Condition: worker (logFC) | AveExpr | t | P.Value | adj.P.Val | B | |
---|---|---|---|---|---|---|
KNTC1 | 92 | 314.5 | 3.942 | 0.0007669 | 1 | -4.595 |
GPR157 | -22.58 | 33.46 | -3.672 | 0.001452 | 1 | -4.595 |
POU2F2 | 207.2 | 518.7 | 3.288 | 0.003565 | 1 | -4.595 |
HELLS | 113.5 | 607.2 | 3.236 | 0.004026 | 1 | -4.595 |
CTSO | -212.3 | 913.8 | -3.21 | 0.004277 | 1 | -4.595 |
IKZF2 | 146.6 | 545.5 | 3.148 | 0.004922 | 1 | -4.595 |
STOML1 | 60.67 | 190.2 | 3.083 | 0.005717 | 1 | -4.595 |
SWSAP1 | -25.92 | 141.8 | -3.041 | 0.006292 | 1 | -4.595 |
CEP55 | 38.75 | 99.67 | 3.016 | 0.006672 | 1 | -4.595 |
FCAMR | 74.92 | 73.54 | 2.935 | 0.008006 | 1 | -4.595 |
This plot helps identify outlying samples.
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 is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.
This is a plot of the top two principal components colored by batch to show the batch effects.
Proportion of Variance (%) | Cumulative Proportion of Variance (%) | Percent Variation Explained by Either Condition or Batch | Percent Variation Explained by Condition | Condition Significance (p-value) | Percent Variation Explained by Batch | Batch Significance (p-value) | |
---|---|---|---|---|---|---|---|
PC1 | 33.93 | 33.93 | 28.9 | 9.1 | 0.9626 | 28.9 | 0.08619 |
PC2 | 8.822 | 42.75 | 32.4 | 4 | 0.4455 | 30.3 | 0.03015 |
PC3 | 7.268 | 50.02 | 8.1 | 6.2 | 0.2879 | 2.7 | 0.8124 |
PC4 | 6.219 | 56.24 | 32.6 | 18.5 | 0.1274 | 24.1 | 0.1486 |
PC5 | 4.803 | 61.04 | 4.3 | 0.3 | 0.597 | 2.9 | 0.6654 |
PC6 | 4.368 | 65.41 | 24.2 | 2.9 | 0.9104 | 24.2 | 0.08399 |
PC7 | 3.84 | 69.25 | 2.9 | 0.2 | 0.6988 | 2.1 | 0.7602 |
PC8 | 3.064 | 72.31 | 27.7 | 3.9 | 0.8422 | 27.5 | 0.05826 |
PC9 | 2.857 | 75.17 | 19.9 | 0.3 | 0.8061 | 19.7 | 0.1109 |
PC10 | 2.795 | 77.97 | 2.8 | 0.1 | 0.6466 | 1.8 | 0.7582 |
PC11 | 2.519 | 80.48 | 32.2 | 22 | 0.00829 | 3.2 | 0.2444 |
PC12 | 2.382 | 82.87 | 10.7 | 0 | 0.6898 | 10 | 0.3224 |
PC13 | 2.231 | 85.1 | 0.5 | 0 | 0.8961 | 0.4 | 0.95 |
PC14 | 2.047 | 87.14 | 1.4 | 0.3 | 0.9254 | 1.4 | 0.8889 |
PC15 | 1.827 | 88.97 | 13.9 | 2.1 | 0.143 | 3.9 | 0.277 |
PC16 | 1.766 | 90.74 | 4.1 | 1.5 | 0.3868 | 0.4 | 0.7651 |
PC17 | 1.631 | 92.37 | 9.4 | 7 | 0.2495 | 3 | 0.7748 |
PC18 | 1.447 | 93.82 | 4.2 | 1.2 | 0.8075 | 3.9 | 0.7294 |
PC19 | 1.366 | 95.18 | 9.1 | 1.1 | 0.4559 | 6.5 | 0.4299 |
PC20 | 1.326 | 96.51 | 3.6 | 1.2 | 0.4432 | 0.7 | 0.7781 |
PC21 | 1.231 | 97.74 | 1.1 | 0.2 | 0.7498 | 0.6 | 0.9116 |
PC22 | 1.155 | 98.89 | 7.7 | 5.8 | 0.2535 | 1.4 | 0.8079 |
PC23 | 1.106 | 100 | 17.9 | 12 | 0.05222 | 0.4 | 0.5023 |
PC24 | 6.891e-29 | 100 | 18.9 | 4.2 | 0.8346 | 18.7 | 0.1881 |
This is a heatmap plot showing the variation of gene expression mean, variance, skewness and kurtosis between samples grouped by batch to see the batch effects variation
## Note: Sample-wise p-value is calculated for the variation across samples on the measure across genes. Gene-wise p-value is calculated for the variation of each gene between batches on the measure across each batch. If the data is quantum normalized, then the Sample-wise measure across genes is same for all samples and Gene-wise p-value is a good measure.
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
## Warning in ks.test(gamma.hat[1, ], "pnorm", gamma.bar[1], sqrt(t2[1])): ties should not be present for the Kolmogorov-Smirnov test
## Warning in ks.test(gamma.hat[1, ], "pnorm", gamma.bar[1], sqrt(shinyInput$t2[1])): ties should not be present for the Kolmogorov-Smirnov test
## Warning in ks.test(delta.hat[1, ], invgam): p-value will be approximate in the presence of ties
## Batch mean distribution across genes: Normal vs Empirical distribution
## Two-sided Kolmogorov-Smirnov test
## Selected Batch: 1
## Statistic D = 0.01573
## p-value = 0.001456
##
##
## Batch Variance distribution across genes: Inverse Gamma vs Empirical distribution
## Two-sided Kolmogorov-Smirnov test
## Selected Batch: 1
## Statistic D = 0.1765
## p-value = 0Note: The non-parametric version of ComBat takes much longer time to run and we recommend it only when the shape of the non-parametric curve widely differs such as a bimodal or highly skewed distribution. Otherwise, the difference in batch adjustment is very negligible and parametric version is recommended even if p-value of KS test above is significant.
## Number of Surrogate Variables found in the given data: 1