BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 150629 Batch 150722 Batch 170208
Condition crowned 4 2 0
Condition worker 0 2 3

Measures of confounding between Batch and Condition

  Standardized Pearson Correlation Coefficient Cramer’s V
Confounding Coefficients (0=no confounding, 1=complete confounding) 0.8806 0.7958

Variation Analysis

Variation explained by Batch and Condition

  Full (Condition+Batch) Condition Batch
Min. 0 0 0
1st Qu. 24.2 1.767 19.22
Median 38.28 7.787 33.41
Mean 39.37 12.23 35
3rd Qu. 53.38 19.41 49.11
Max. 96.67 78.59 96.21

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 4.26e-05 0.1307 0.2864 0.3481 0.5239 1 0.09862
Condition P-values 0.002146 0.4369 0.6676 0.6284 0.846 1 0.008761

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
XIRP2 -103 42.55 -3.905 0.004956 1 -4.595
TIMM9 -240 651.1 -3.827 0.00552 1 -4.595
RSRC1 -110.5 388.7 -3.824 0.005543 1 -4.595
CCDC28A -113.5 293.5 -3.615 0.007413 1 -4.595
HAGHL -108.5 101.7 -3.605 0.007514 1 -4.595
NDUFA12 -117 323.7 -3.602 0.007552 1 -4.595
RHOBTB2 -80.5 276.5 -3.421 0.009754 1 -4.595
ISPD -70 154.8 -3.377 0.01039 1 -4.595
VPS51 -227.5 1884 -3.375 0.01043 1 -4.595
FAM189A1 -168.5 184.7 -3.303 0.01157 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 32.85 32.85 45.6 4.6 0.9173 45.5 0.1399
PC2 26.29 59.14 56 24.2 0.5767 53.9 0.1489
PC3 13.14 72.28 29.6 22.8 0.4331 22.7 0.722
PC4 6.055 78.33 10.8 0.5 0.8333 10.2 0.6817
PC5 4.794 83.13 10.2 0.9 0.8087 9.4 0.7064
PC6 4.42 87.55 34.5 3.3 0.1131 3.8 0.2557
PC7 4.091 91.64 4.8 0.3 0.8693 4.4 0.849
PC8 3.185 94.82 27.1 9.3 0.4941 21.7 0.4645
PC9 2.813 97.64 27.3 7.1 0.9054 27.2 0.4239
PC10 2.363 100 53.9 27.1 0.02538 1.2 0.2008
PC11 1.214e-28 100 23.6 1.1 0.3867 14.3 0.4047

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 = 0.01989
## p-value = 9.227e-06
## 
## 
## Batch Variance distribution across genes: Inverse Gamma vs Empirical distribution
## Two-sided Kolmogorov-Smirnov test
## Selected Batch: 1
## Statistic D = 0.1364
## 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: 2