BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 180305
Condition crowned 12
Condition worker 10

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. 0.319 0.319 0
Median 1.407 1.407 0
Mean 3.165 3.165 0
3rd Qu. 4.232 4.232 0
Max. 44.7 44.7 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 0.0006709 0.3584 0.599 0.5733 0.8028 1 0.01735

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
EXTL2 29.33 263 3.864 0.0009237 1 -4.593
SIGLEC1 18.23 14.45 3.384 0.002857 1 -4.593
SRM -65.63 519.5 -3.363 0.002998 1 -4.593
PLA2G2D 13.87 13.14 3.214 0.004236 1 -4.594
TBXAS1 -20.02 106.8 -3.212 0.004253 1 -4.594
PLA2G2F 11.63 58.95 3.199 0.004379 1 -4.594
ISPD 39.95 277.9 3.118 0.00528 1 -4.594
PAM 596 4391 3.096 0.00555 1 -4.594
IL1R2 11.38 13.09 3.061 0.006013 1 -4.594
LOC100862671 -5.417 14.95 -3.039 0.006322 1 -4.594

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 32.33 32.33 0.9 0.9 0.6757 0 1
PC2 10.44 42.76 3.4 3.4 0.4109 0 1
PC3 7.226 49.99 0 0 0.9426 0 1
PC4 5.074 55.06 8.8 8.8 0.1811 0 1
PC5 4.909 59.97 2.5 2.5 0.4818 0 1
PC6 4.513 64.49 3.2 3.2 0.4267 0 1
PC7 3.915 68.4 1 1 0.6512 0 1
PC8 3.106 71.51 12.1 12.1 0.1134 0 1
PC9 2.901 74.41 0.4 0.4 0.7824 0 1
PC10 2.647 77.05 0.1 0.1 0.9007 0 1
PC11 2.483 79.54 0 0 0.989 0 1
PC12 2.368 81.9 0.8 0.8 0.6922 0 1
PC13 2.23 84.13 0.7 0.7 0.7185 0 1
PC14 2.173 86.31 27.4 27.4 0.01251 0 1
PC15 2.121 88.43 4.7 4.7 0.3317 0 1
PC16 2.099 90.53 3.7 3.7 0.3916 0 1
PC17 2.081 92.61 0.3 0.3 0.8218 0 1
PC18 1.921 94.53 13.5 13.5 0.09197 0 1
PC19 1.885 96.41 1.1 1.1 0.6352 0 1
PC20 1.85 98.26 15.4 15.4 0.071 0 1
PC21 1.736 100 0.1 0.1 0.8925 0 1
PC22 6.567e-29 100 0.5 0.5 0.748 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: 3