Skip to content

Data_format.md

Latest commit

 

History

History
164 lines (141 loc) · 5.58 KB

Data_format.md

File metadata and controls

164 lines (141 loc) · 5.58 KB

Data input, cleaning and pre-processing

This is the first step of any network analysis. We show here how to load typical expression data, pre-process them into a format suitable for network analysis, and clean the data by removing obvious outlier samples as well as genes and samples with excessive numbers of missing entries.

Data Input

We store raw expression data along information in anndata format in geneExpr variable. you can pass your expression data, gene and sample information all together or separately:

expression data, gene and sample information all together in anndata format

If you already have your expression data in anndata format you can define your pyWGCNA object by passing your variable in anndata format. keep in mind X should be expression matrix. var is gene information and obs is sample information.

expression data, gene and sample information separately

you can pass the paths that store each information or the table contains them.

Gene Expression

The expression data is a table which the rows are samples and columns are genes. The first column (index of dataframe) is going to be sample id or sample name and first column (column of dataframe) should be gene id or gene name which both of them should be unique.

sample_id ENSMUSG00000000003 ENSMUSG00000000028 ENSMUSG00000000031 ENSMUSG00000000037
sample_11615 12.04 11.56 16.06 13.18
sample_11616 1.35 1.63 1.28 1

Gene Information

The gene information is a table which contains additional information about each genes. First column should be your index which should be the same name as first column of gene expression data (gene ID).

gene_id gene_name gene_type
ENSMUSG00000000003 Pbsn protein_coding
ENSMUSG00000000028 Cdc45 protein_coding
ENSMUSG00000000031 H19 lncRNA
ENSMUSG00000000037 Scml2 protein_coding

Sample Information

The sample information is a table which contains additional information about each sample. First column should be your index which should be the same name as first row of gene expression data (sample ID).

Sample_id Age Tissue Sex Genotype
sample_11615 4mon Cortex Female 5xFADHEMI
sample_11616 4mon Cortex Female 5xFADWT

Other parameters

These are other parameters we suggest checking them before starting any analysis.

  • name: name of the WGCNA we used to visualize data (default: WGCNA)

  • save: define whether you want to save result of important steps or not (If you want to set it TRUE you should have a write access on the output directory)

  • outputPath: define where you want to save your data, otherwise it will be store near the code.

  • TPMcutoff: cut off for removing genes that expressed under this number along samples

  • networkType : Type of networks (default: signed hybrid and Options: unsigned, signed and signed hybrid)

  • adjacencyType: Type of adjacency matrix (default: signed hybrid and Options: unsigned, signed and signed hybrid)

  • TOMType: Type of topological overlap matrix(TOM) (default: signed and Options: unsigned and signed)

For depth-in documents look at here.

Data cleaning and pre-processing

PyWGCNA checks data for genes and samples with too many missing values.

  1. Remove genes without any expression more than TPMcutoff value (default one) across all samples.
  2. goodSamplesGenes() function to find genes and samples with too many missing values.
  3. Cluster the samples (use Hierarchical clustering from scipy) to see if there are any obvious outliers. you can define value the height by cut value. By default, we don't remove any sample by hierarchical clustering