BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 180223
Condition crowned 12
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. 0.9878 0.9878 0
Median 4.372 4.372 0
Mean 8.139 8.139 0
3rd Qu. 11.88 11.88 0
Max. 66.93 66.93 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 5.866e-06 0.1261 0.363 0.4075 0.6681 1 0.1353

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
WWC2 1483 2433 6.291 4.269e-06 0.06459 -2.668
SETDB2 115.3 277.4 5.815 1.191e-05 0.06459 -2.791
PFKFB2 2137 4688 5.793 1.25e-05 0.06459 -2.797
FBXO48 84.14 146.8 5.509 2.339e-05 0.07355 -2.878
BCL6 536.3 1218 5.502 2.373e-05 0.07355 -2.88
ST8SIA1 115.7 177.7 5.27 3.987e-05 0.08294 -2.95
TMEM68 141.5 541.4 5.133 5.425e-05 0.08294 -2.993
DCP2 68.5 201.5 5.114 5.658e-05 0.08294 -2.999
SLC9A7 468.5 1054 5.112 5.685e-05 0.08294 -2.999
RND3 2480 6411 5.036 6.747e-05 0.08294 -3.024

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 31.62 31.62 10.3 10.3 0.1559 0 1
PC2 10.55 42.17 6.3 6.3 0.273 0 1
PC3 9.399 51.57 8.8 8.8 0.1919 0 1
PC4 7.492 59.06 22.9 22.9 0.02808 0 1
PC5 6.354 65.42 10.4 10.4 0.1534 0 1
PC6 3.896 69.31 1.9 1.9 0.5524 0 1
PC7 3.718 73.03 2.1 2.1 0.5346 0 1
PC8 3.284 76.31 1.9 1.9 0.5487 0 1
PC9 2.776 79.09 5.8 5.8 0.2917 0 1
PC10 2.727 81.82 5.6 5.6 0.3034 0 1
PC11 2.531 84.35 7.8 7.8 0.2187 0 1
PC12 2.172 86.52 1.9 1.9 0.553 0 1
PC13 2.041 88.56 0.9 0.9 0.6768 0 1
PC14 2.018 90.58 2.2 2.2 0.5187 0 1
PC15 1.774 92.35 5 5 0.3295 0 1
PC16 1.724 94.08 0 0 0.9522 0 1
PC17 1.657 95.73 2.8 2.8 0.4685 0 1
PC18 1.503 97.24 0.1 0.1 0.8779 0 1
PC19 1.468 98.71 1.2 1.2 0.6397 0 1
PC20 1.294 100 2 2 0.5436 0 1
PC21 4.04e-29 100 9.3 9.3 0.1785 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: 1