BatchQC Report

Tests for checking Batch Effects

Summary

Confounding

Number of samples in each Batch and Condition

  Batch 180305
Condition crowned 7
Condition worker 4

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. 1.457 1.457 0
Median 5.821 5.821 0
Mean 10.69 10.69 0
3rd Qu. 15.19 15.19 0
Max. 84.69 84.69 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 5.946e-05 0.236 0.4748 0.4818 0.7237 1 0.05893

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
LPGAT1 141.4 612 7.048 4.217e-05 0.2929 -4.045
KCNA1 54.21 151 6.881 5.134e-05 0.2929 -4.05
PIK3R4 207.5 1651 6.817 5.538e-05 0.2929 -4.052
RABL3 52.64 258 6.361 9.661e-05 0.3832 -4.068
ZDHHC21 73.5 199.7 6.08 0.000138 0.4133 -4.08
RBM12 276.2 1375 5.983 0.0001563 0.4133 -4.084
ST6GALNAC5 177.3 310.9 5.715 0.0002226 0.5046 -4.096
TMEM245 161.6 532.9 5.417 0.0003333 0.6162 -4.111
OGFOD2 -276.9 1982 -5.382 0.0003496 0.6162 -4.113
ACTN1 136 384.7 5.119 0.0005055 0.6466 -4.128

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 28.09 28.09 6.7 6.7 0.4427 0 1
PC2 17.14 45.23 10.7 10.7 0.3253 0 1
PC3 13.17 58.39 29.3 29.3 0.0852 0 1
PC4 9.157 67.55 14.6 14.6 0.2457 0 1
PC5 7.275 74.83 4 4 0.5538 0 1
PC6 6.485 81.31 0 0 0.9937 0 1
PC7 5.305 86.62 2 2 0.6754 0 1
PC8 5.25 91.87 1.9 1.9 0.6825 0 1
PC9 4.39 96.26 18.1 18.1 0.1925 0 1
PC10 3.744 100 12.5 12.5 0.2856 0 1
PC11 1.421e-28 100 15.3 15.3 0.2348 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