Tests for checking Batch Effects
Batch 180306 | |
---|---|
Condition crowned | 12 |
Condition worker | 10 |
Standardized Pearson Correlation Coefficient | Cramer’s V | |
---|---|---|
Confounding Coefficients (0=no confounding, 1=complete confounding) | NA | NA |
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 |
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 |
Boxplots for all values for each of the samples and are colored by batch membership.
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 |
This plot helps identify outlying samples.
This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.
This is a heatmap of the correlation between samples.
This is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.
This is a plot of the top two principal components colored by batch to show the batch effects.
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 |
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.
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
## Number of Surrogate Variables found in the given data: 0