Gursharan Singh wrote over two hundred drama scripts. Many of these were original plays, others were based on short stories, novels and even poems from contemporary writings. In 2010-11, writer and artistic director, Kewal Dhaliwal, published seven volumes of Gursharan Singh’s collected plays and released them in Chandigarh in the presence of Gursharan Singh. We discovered a few more scripts after the publication of these seven volumes. These will be brought out in another volume in the coming year. The seven volumes are being added with much gratitude to Kewal Dhaliwal, who is also a member of the Trust.
# Statistical analysis fit2 <- eBayes(fit, contrast = con)
# Find top differentially expressed genes result <- topTable(fit2, adjust = TRUE, n = 10) This example assumes a very simple scenario. For actual experiments, more complex designs and thorough quality control checks are typically required. limma is a powerful tool for differential expression analysis in genomics. Its ability to handle complex experimental designs and provide robust statistical tests makes it a preferred choice among researchers. However, like all bioinformatics tools, careful attention to data preparation, model assumptions, and interpretation of results is crucial. limm-c.f
# Install and load necessary packages install.packages("limma") library(limma) # Statistical analysis fit2 <- eBayes(fit, contrast =
# Design matrix design <- model.matrix(~ group) # Statistical analysis fit2 <
# Fit the model fit <- lmFit(expr, design)
# Statistical analysis fit2 <- eBayes(fit, contrast = con)
# Find top differentially expressed genes result <- topTable(fit2, adjust = TRUE, n = 10) This example assumes a very simple scenario. For actual experiments, more complex designs and thorough quality control checks are typically required. limma is a powerful tool for differential expression analysis in genomics. Its ability to handle complex experimental designs and provide robust statistical tests makes it a preferred choice among researchers. However, like all bioinformatics tools, careful attention to data preparation, model assumptions, and interpretation of results is crucial.
# Install and load necessary packages install.packages("limma") library(limma)
# Design matrix design <- model.matrix(~ group)
# Fit the model fit <- lmFit(expr, design)