BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 180306
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. 0.57 0.57 0
Median 2.566 2.566 0
Mean 4.9 4.9 0
3rd Qu. 6.971 6.971 0
Max. 53.85 53.85 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 0.0001014 0.2351 0.4764 0.4864 0.7384 1 0.04829

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
AIFM1 40.3 492.8 4.716 0.0001228 0.983 -4.592
KCTD5 -8.817 52.41 -4.588 0.0001663 0.983 -4.592
CYP4V2 39.58 150.4 4.488 0.0002108 0.983 -4.592
FAM92B 12.35 10.36 4.094 0.0005352 0.983 -4.592
BAMBI 70.15 223.6 4.028 0.0006258 0.983 -4.592
CRYGS 9.933 26.68 4.02 0.0006382 0.983 -4.592
CYP21A2 8.617 17 3.914 0.0008205 0.983 -4.592
CPS1 12.68 58.18 3.877 0.0008969 0.983 -4.592
KIAA1257 10.77 32.73 3.849 0.0009579 0.983 -4.593
B3GALNT2 10.12 69.68 3.84 0.0009776 0.983 -4.593

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 19.06 19.06 0.9 0.9 0.6692 0 1
PC2 12.74 31.81 14.5 14.5 0.08049 0 1
PC3 10.72 42.52 3.4 3.4 0.4098 0 1
PC4 9.055 51.58 2.1 2.1 0.5152 0 1
PC5 5.458 57.04 0.1 0.1 0.9029 0 1
PC6 5.076 62.11 0.1 0.1 0.8915 0 1
PC7 4.24 66.36 23.8 23.8 0.02134 0 1
PC8 3.639 69.99 5.9 5.9 0.2781 0 1
PC9 3.353 73.35 0.4 0.4 0.7826 0 1
PC10 2.869 76.22 5.2 5.2 0.3064 0 1
PC11 2.591 78.81 5.7 5.7 0.2854 0 1
PC12 2.449 81.26 2.6 2.6 0.4736 0 1
PC13 2.333 83.59 0.1 0.1 0.901 0 1
PC14 2.243 85.83 0.5 0.5 0.7605 0 1
PC15 2.19 88.02 4.4 4.4 0.3502 0 1
PC16 2.136 90.16 0 0 0.9377 0 1
PC17 2.099 92.26 0.4 0.4 0.7822 0 1
PC18 2.058 94.32 12.6 12.6 0.1057 0 1
PC19 1.979 96.29 2 2 0.5265 0 1
PC20 1.922 98.22 15.2 15.2 0.07268 0 1
PC21 1.783 100 0.2 0.2 0.8594 0 1
PC22 9.377e-29 100 0.6 0.6 0.7409 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