Publications by Author: Ahmad Ali
RBIF_115_Assignment_3
1 Establishing Variables and Initial Code load("Genetics_DataSets_forR.RData") # load("C:/Users/ahmad/OneDrive/Brandeis University/BFIF115/Assignment_2/Genetics_DataSets_forR.RData") ############################### #Chi-sqr and Fisher's ############################### #Use this code to identify the names of esr1 SNP: attach(fms) NamesEsr1Snps<-nam...
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Network Analysis and Identification of Key microRNA Hubs in IgA Nephropathy: Uncovering the Role of miRNA Biomarkers in IgA1 Glycosylation
1 Introduction 1.1 Background Information on the Biological Question IgA nephropathy (IgAN), recognized as the most common form of primary glomerulonephritis worldwide, manifests as a complex interplay between genetic and environmental factors, leading to the aberrant regulation of immune and glycosylation processes (Rajasekaran et al., 2021)....
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Homework 4 - Ahmad Ali
1 Establishing Variables and Saving Gene Expression # GEO dataset, tried with others, but the data was not as clean as this example. library(airway) data(airway) airway ## class: RangedSummarizedExperiment ## dim: 64102 8 ## metadata(1): '' ## assays(1): counts ## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99 ## row...
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Discussion Week 8
1 Establishing Variables miRNA.normalized.results <- read.csv("GSE64977_HD_C_normalized_miRNA_counts.csv.gz", row.names = 1) miRNA.results <- read.csv("GSE64977_HD_C_miRNA_DE_results.csv.gz", row.names = 1) meta.data <- data.frame( id = colnames(miRNA.normalized.results), condition = c(rep("control", times = 36), rep("huntingtons", times ...
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RBIF_114_HW3
1 Establishing A Few Variables data(airway) dds=DESeq2::DESeqDataSet(airway, design = ~cell+dex) dds <- dds[ rowSums(counts(dds)) > 1, ] dds <- estimateSizeFactors(dds) countdata <- assays(airway)[[1]] countdata <- countdata[rowSums(countdata) > 0, ] countdata.deseq <- sweep(countdata, 2 , sizeFactors(dds), FUN = "/") rld=rlog(dds) vars <- apply(a...
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RBIF_114_HW2
1 Biomarker Dataset: Airway This dataset serves to primarily understand transcriptional changes from treatment with dexamethasone on lung airway smooth muscle cell lines. I pulled the original data and cleaned it by dichotomizing the four cell lines into treated and untreated samples. I then further took their counts, explored the nature of th...
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Homework 8 - Ahmad Ali
1 Problem 1 (30 points) In the Notes we have retrieved from HRPD data table all direct protein-protein interactions among the top differentially expressed genes (we considered differential expression wrt the outcome of interest, i.e. recurrent/non-recurrent disease). It would be clearly interesting to find out if the interactions are “signi...
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RBIF_112_HW4
1 Establishing Variables library(DESeq2) library(gplots) library(Homo.sapiens) library(topGO) library(airway) data(airway) dds=DESeq2::DESeqDataSet(airway, design = ~cell+dex) dds <- dds[ rowSums(counts(dds)) > 1, ] dds <- estimateSizeFactors(dds) countdata <- assays(airway)[[1]] countdata <- countdata[rowSums(countdata) > 0, ] countda...
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RBIF_112_HW3
1 Problem 1 (30 points) Let us study how the ratio of within-cluster variance to the total variance changes as function of the number of clusters and the number of variables in the problem. We will be clustering a dataset without any internal structure, so in a sense the numbers we obtain will serve as the expected values under the “null hypoth...
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RBIF_112_HW1
1 Problem 1 (25 points) Examine the description of the humanHprdP2P table at the USCS web portal (it is hyperlinked as a “connected table” from the same knownGene description page we were looking at in the Notes). This data table lists all the protein-protein interactions in the HPRD database. As you can see, the format is very simple: each r...
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