BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 180305
Condition crowned 5
Condition worker 5

Measures of confounding between Batch and Condition

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

Variation Analysis

Variation explained by Batch and Condition

  Full (Condition+Batch) Condition Batch
Min. 0 0 0
1st Qu. 6.947 6.947 0
Median 20.7 20.7 0
Mean 25.09 25.09 0
3rd Qu. 39.22 39.22 0
Max. 92.99 92.99 0

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 1 1 1 1 1 0
Condition P-values 6.797e-06 0.05274 0.1864 0.2876 0.4618 1 0.2424

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
CAPN6 1834 1888 10.18 4.369e-06 0.04769 -4.103
COL4A3 1497 1429 9.273 9.17e-06 0.04769 -4.112
KEL 206.8 501.4 8.608 1.642e-05 0.04769 -4.12
POLE2 40.4 60 8.501 1.811e-05 0.04769 -4.122
SKA1 86.4 108.4 8.37 2.042e-05 0.04769 -4.124
GINS3 64.4 92.2 8.348 2.085e-05 0.04769 -4.124
MASTL 58.4 109 8.202 2.388e-05 0.04769 -4.126
DIAPH3 137.6 169.4 7.986 2.932e-05 0.04769 -4.13
ESCO2 88.2 106.9 7.912 3.15e-05 0.04769 -4.131
GAS6 929.6 2904 7.901 3.183e-05 0.04769 -4.131

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 46.5 46.5 37.2 37.2 0.06124 0 1
PC2 17.69 64.18 35.2 35.2 0.07062 0 1
PC3 10.57 74.76 0 0 0.963 0 1
PC4 6.874 81.63 17 17 0.2368 0 1
PC5 5.089 86.72 1.7 1.7 0.7167 0 1
PC6 3.744 90.46 4.7 4.7 0.5484 0 1
PC7 3.671 94.14 3 3 0.6329 0 1
PC8 3.354 97.49 0.7 0.7 0.8209 0 1
PC9 2.51 100 0.5 0.5 0.84 0 1
PC10 1.275e-28 100 21.5 21.5 0.1771 0 1

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.

## Warning in combatPlot(shinyInput$lcounts, batch = shinyInput$batch, mod = mod): There is no batch

SVA

Summary

## Number of Surrogate Variables found in the given data: 2