BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 150629 Batch 150722 Batch 151218
Condition crowned 4 3 5
Condition worker 0 3 2

Measures of confounding between Batch and Condition

  Standardized Pearson Correlation Coefficient Cramer’s V
Confounding Coefficients (0=no confounding, 1=complete confounding) 0.5394 0.4126

Variation Analysis

Variation explained by Batch and Condition

  Full (Condition+Batch) Condition Batch
Min. 0.123 0 0
1st Qu. 10.78 0.176 7.822
Median 15.68 0.883 12.21
Mean 17.94 2.754 14.28
3rd Qu. 22.47 3.241 18.49
Max. 82.73 59.89 81.65

P-value Analysis

Distribution of Batch and Condition Effect p-values Across Genes

  Min. 1st Qu. Median Mean 3rd Qu. Max. Ps<0.05
Batch P-values 1.385e-05 0.2384 0.3798 0.4021 0.5415 0.9999 0.0397
Condition P-values 0.0005223 0.4024 0.5892 0.5786 0.7781 1 0.01303

Differential Expression

Expression Plot

Boxplots for all values for each of the samples and are colored by batch membership.

LIMMA

  Condition: worker (logFC) AveExpr t P.Value adj.P.Val B
PCSK1N 387.3 502.2 3.969 0.001486 1 -4.595
RHOV -24.68 36.29 -3.91 0.001665 1 -4.595
OR2F1 -495.2 1277 -3.593 0.003082 1 -4.595
HBB -181.8 275.9 -3.581 0.003155 1 -4.595
ABLIM2 42.34 54.94 3.454 0.004046 1 -4.595
KCNJ15 -51.44 106.9 -3.392 0.004571 1 -4.595
WNT4 -67.54 90.12 -3.387 0.004617 1 -4.595
NEURL1B -42 117 -3.178 0.006949 1 -4.595
DMBT1 36567 20833 3.064 0.008687 1 -4.595
CPVL 340.2 261.6 3.064 0.008691 1 -4.595

Median Correlations

This plot helps identify outlying samples.

Heatmaps

Heatmap

This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.

Sample Correlations

This is a heatmap of the correlation between samples.

Circular Dendrogram

This is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.

PCA: Principal Component Analysis

PCA

This is a plot of the top two principal components colored by batch to show the batch effects.

Explained Variation

  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 55.3 55.3 12.2 0 0.6149 10.4 0.4291
PC2 8.733 64.03 43.4 12.2 0.1341 32.3 0.05782
PC3 6.341 70.37 45.8 3.3 0.7231 45.2 0.02334
PC4 5.836 76.21 25.6 0.3 0.712 24.8 0.1493
PC5 3.609 79.82 2.1 1 0.8599 1.8 0.9328
PC6 3.164 82.98 8.6 1.9 0.3946 3.2 0.6323
PC7 2.765 85.74 8.3 1.7 0.4393 3.8 0.6336
PC8 1.958 87.7 32.9 30.1 0.0263 0.5 0.7651
PC9 1.933 89.64 24.7 12.8 0.1323 9.8 0.3859
PC10 1.823 91.46 2.2 0.7 0.8576 2 0.9061
PC11 1.72 93.18 8.4 4.7 0.5749 6.1 0.7741
PC12 1.659 94.84 15 3.2 0.2593 6 0.4289
PC13 1.523 96.36 2.5 0.3 0.9218 2.5 0.8635
PC14 1.37 97.73 21.3 0 0.4087 16.9 0.212
PC15 1.168 98.9 34.6 15.6 0.4315 31.3 0.1909
PC16 1.102 100 12.3 11.8 0.2757 3.5 0.9698
PC17 6.851e-29 100 33.9 23.5 0.1607 22.6 0.3892

Shape

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.

Combat Plots

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 = 0.1042
## p-value = 0
## 
## 
## Batch Variance distribution across genes: Inverse Gamma vs Empirical distribution
## Two-sided Kolmogorov-Smirnov test
## Selected Batch: 1
## Statistic D = 0.1983
## 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.

SVA

Summary

## Number of Surrogate Variables found in the given data: 1