BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 180223
Condition crowned 10
Condition worker 9

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. 1.721 1.721 0
Median 6.059 6.059 0
Mean 8.418 8.418 0
3rd Qu. 12.25 12.25 0
Max. 65.34 65.34 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 2.816e-05 0.1419 0.3097 0.379 0.5925 1 0.09164

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
PABPC4L 33.71 56.37 5.635 2.657e-05 0.2124 -3.08
ZMAT3 172.7 292.5 5.422 4.128e-05 0.2124 -3.13
DGKH 64.12 128.5 5.419 4.159e-05 0.2124 -3.131
PIEZO1 619.8 1029 5.28 5.556e-05 0.2128 -3.165
CYP7B1 79.16 122.9 4.962 0.0001089 0.3338 -3.248
BMPR2 308.2 576.1 4.656 0.0002104 0.4773 -3.335
KIAA1045 44.9 81.37 4.639 0.0002181 0.4773 -3.339
LMBRD2 173.1 306.9 4.379 0.0003835 0.4843 -3.418
ABCA1 881.6 2577 4.321 0.0004352 0.4843 -3.436
BCL2L11 142.4 270.5 4.291 0.0004651 0.4843 -3.445

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 42.71 42.71 8.1 8.1 0.2388 0 1
PC2 20.21 62.92 11.6 11.6 0.1539 0 1
PC3 8.948 71.87 15.1 15.1 0.1001 0 1
PC4 3.673 75.54 0.1 0.1 0.877 0 1
PC5 2.888 78.43 15.9 15.9 0.0909 0 1
PC6 2.881 81.31 0.1 0.1 0.8766 0 1
PC7 2.086 83.4 0.2 0.2 0.8576 0 1
PC8 1.997 85.4 13.3 13.3 0.1241 0 1
PC9 1.938 87.33 0 0 0.9871 0 1
PC10 1.815 89.15 17.3 17.3 0.07647 0 1
PC11 1.678 90.83 1.2 1.2 0.655 0 1
PC12 1.557 92.38 0 0 0.9769 0 1
PC13 1.398 93.78 1.6 1.6 0.6033 0 1
PC14 1.37 95.15 1.4 1.4 0.6279 0 1
PC15 1.286 96.44 0.8 0.8 0.7136 0 1
PC16 1.229 97.67 2.2 2.2 0.5433 0 1
PC17 1.177 98.84 8.9 8.9 0.214 0 1
PC18 1.156 100 2 2 0.561 0 1
PC19 6.993e-29 100 15 15 0.1012 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: 0