RankCompV3

RankCompV3 is a differential expression analysis algorithm based on relative expression ordering (REO) of gene pairs. It can be applied to bulk or single-cell RNA-sequencing (scRNA-seq) data, microarray gene expression profiles and proteomics profiles, etc. When applied in scRNA-seq data, it can run in single-cell mode or pseudo-bulk mode. The pseudo-bulk mode is expected to improve the accuracy while decreasing runntime and memory cost.

The algorithm is implemented in Julia. Version 1.7 or later is recommended. The simpliest way to install is using the Pkg facility in Julia.

using Pkg
Pkg.add("RankCompV3")

Run a test job with the input files distributed with the package.

julia> using RankCompV3
# Use the default values for the following other parameters. If you need to modify the parameters, add them directly.
julia> result = reoa(use_testdata="yes")

The analysis results and a few plots will be generated and saved in the current work directory. They are also returned by the reoa function and can be captured by assign the returned values to a variable, e.g., result in the above example.

The first return value is a DataFrame, where rows are genes and columns are statistical values for each gene. All the genes passing the basic preprocessing step are retained.

julia> result
19999×2 DataFrame
   Row │ gene_name  group1_vs_group2
       │ Any        Any
───────┼─────────────────────────────
     1 │ DE1        up
     2 │ DE2        up
     3 │ DE3        up
     4 │ DE4        up
     5 │ DE5        up
     6 │ DE6        up
   ⋮   │     ⋮             ⋮
 19995 │ EE19996    up
 19996 │ EE19997    up
 19997 │ EE19998    up
 19998 │ EE19999    up
 19999 │ EE20000    up
                   19988 rows omitted

You need to prepare two input files before the analysis: metadata file and expression matrix. .rds, .csv, .txt, .tsv and .RData files are supported.

• metadata file (required).

The metadata file contains at least two columns. The first column is the sample names, and the second column is the grouping information. Only two groups are supported at present, therefore, do not include more than two groups.

Column names for a metadata should be Name and Group.

See an example metadata file, fn_meta.txt.

• expression matrix file (required).

The first column is the gene name and the column header should be Name and the rest columns are profiles for each cell or each sample. Each column header should be the sample name which appears in the metadata file.

See an example expression matrix file, fn_expr.txt.

Raw counts or expression values are recommended to use. Other values, e.g, FPKM, RPKM, TPM, log(counts) and log(normalized counts), can also be used, though normalization and batch effect removal are neither necessary nor recommended.

Once the files are ready, you can carry out the DEG analysis with the default settings as follows.

julia> using RankCompV3
# Use the default values for the following other parameters. If you want to modify the parameters, add them directly.
julia> reoa("/public/yanj/data/fn_expr.txt",
	"/public/yanj/data/fn_metadata.txt")

Other parameters can be set by passing the value to the corresponding keyword.

julia> reoa("/public/yanj/data/fn_expr.txt",
    	"/public/yanj/data/fn_meta.txt",
    	expr_threshold = 0,
    	min_profiles = 0,
    	min_features = 0,
    	pval_reo = 0.01,
     	pval_deg = 0.05,
     	padj_deg = 0.05,
    	n_pseudo = 0,
    	use_hk_genes = "yes"
    	hk_file = "HK_genes_info.tsv",
    	gene_name_type = "ENSEMBL",
    	ref_gene_max = 3000,
    	ref_gene_min = 100
    	n_iter = 128,
    	n_conv = 5,
    	work_dir = "./",
    	use_testdata = "no")

For scRNA-seq data, one can carry out a pseudobulk analysis. Rather than using the original single-cell profiles, pseudobulk profiles can be generated and used for DEG analysis. In this method, a random subset of cells from a group is aggregated into a pseudo-bulk profile.

The pseudobulk method can be turned on by setting n_pseudo > 0.

julia> reoa("scRNA_expr.txt",
    	"scRNA_metadata.txt",
    	n_pseudo = 1)

By default, profiling does not use the pseudo-bulk method (n_pseudo = 0). 0 indicates that the pseudo-bulk mode is not used, and other values indicate the number of samples in each group after pseudo-bulk is combined.

Below lists the optional keyword parameters and their default values.

• The expression profile (after preprocessing). (See fn_expr_df_expr.tsv)
• The meta data (after preprocessing). (See fn_expr_df_meta.tsv)
• This file contains Name, pval, padj, n11, n12, n13, n21, n22, n23, n31, n32, n33, Δ1, Δ2, se, z1, up_down. (See fn_expr_group1_group2_result.tsv)
• Up-down-regulation of all genes in all groups (up is up-regulation, down is down, no change means that the gene is not recognized as up-regulation/down-regulation) (See fn_expr_gene_up_down.tsv)
• Graph of Distribution of Expression Values. (See fn_expr_group1_group2_expr_dist.pdf)
• Heat maps of expression values for the ctrl and treat groups. (See fn_expr_group1_group2_expr_heat.pdf)
• Distribution of parameters in 3 x 3 contingency tables. (See fn_expr_group1_group2_contigency_table.pdf)
• Delta distribution. (See fn_expr_group1_group2_delta_value.pdf)
• Distribution of Standard Error (SE). (See fn_expr_group1_group2_se.pdf)
• Distribution of z1. (See fn_expr_group1_group2_z1.pdf)
• Distribution of p and FDR values. (See fn_expr_group1_group2_p_value.pdf)
• Distribution of expression values for DEGs. (See fn_expr_group1_group2_degs_expr_dist.pdf)
• Heat map of the expression values of DEGs in the ctrl and treat groups. (See fn_expr_group1_group2_degs_expr_heat.pdf)

• RankCompV3_test_data_output.log

install.packages("JuliaCall")

library(JuliaCall)
install_julia()

library(JuliaCall)
julia <-julia_setup()

julia_install_package_if_needed("RankCompV3")

Run a test job with the input files distributed with the package.

julia_library("RankCompV3")
result <- julia_do.call("reoa",list(use_testdata="yes"),need_return="Julia",show_value=FALSE)

The analysis results and a few plots will be generated and saved in the current work directory. They are also returned by the reoa function and can be captured by assign the returned values to a variable, e.g., result in the above example.

The first return value is a DataFrame, where rows are genes and columns are statistical values for each gene. All the genes passing the basic preprocessing step are retained.

> result
Julia Object of type Tuple{DataFrames.DataFrame, DataFrames.DataFrame, DataFrames.DataFrame}.
19999×2 DataFrame
   Row │ gene_name  group1_vs_group2
       │ Any        Any
───────┼─────────────────────────────
     1 │ DE1        up
     2 │ DE2        up
     3 │ DE3        up
     4 │ DE4        up
     5 │ DE5        up
     6 │ DE6        up
   ⋮   │     ⋮             ⋮
 19995 │ EE19996    up
 19996 │ EE19997    up
 19997 │ EE19998    up
 19998 │ EE19999    up
 19999 │ EE20000    up
                   19988 rows omitted

You need to prepare two input files before the analysis: metadata file and expression matrix. You need to prepare two input files before the analysis: metadata file and expression matrix. .rds, .csv, .txt, .tsv and .RData files are supported.

• metadata file (required).

The metadata file contains at least two columns. The first column is the sample names, and the second column is the grouping information. Only two groups are supported at present, therefore, do not include more than two groups.

Column names for a metadata should be Name and Group.

See an example metadata file, fn_metadata.txt.

• expression matrix file (required).

The first column is the gene name and the column header should be Name and the rest columns are profiles for each cell or each sample. Each column header should be the sample name which appears in the metadata file.

See an example expression matrix file, fn_expr.txt.

Raw counts or expression values are recommended to use. Other values, e.g, FPKM, RPKM, TPM, log(counts) and log(normalized counts), can also be used, though normalization and batch effect removal are neither necessary nor recommended.

Once the files are ready, you can carry out the DEG analysis with the default settings as follows.

julia_library("RankCompV3")
# Use the default values for the following other parameters. If you want to modify the parameters, add them directly.
julia_do.call("reoa",list("/public/yanj/data/fn_expr.txt",
						"/public/yanj/data/fn_meta.txt"),need_return="Julia",show_value=FALSE)

Other parameters can be set by passing the value to the corresponding keyword.

julia_do.call("reoa",list("/public/yanj/data/fn_expr.txt",
    	"/public/yanj/data/fn_meta.txt",
    	expr_threshold = 0,
    	min_profiles = 0,
    	min_features = 0,
    	pval_reo = 0.01,
     	pval_deg = 0.05,
     	padj_deg = 0.05,
    	n_pseudo = 0,
    	use_hk_genes = "yes"
    	hk_file = "HK_genes_info.tsv",
    	gene_name_type = "ENSEMBL",
    	ref_gene_max = 3000,
    	ref_gene_min = 100
    	n_iter = 128,
    	n_conv = 5,
    	work_dir = "./",
    	use_testdata = "no"),need_return="Julia",show_value=FALSE)

For scRNA-seq data, one can carry out a pseudobulk analysis. Rather than using the original single-cell profiles, pseudobulk profiles can be generated and used for DEG analysis. In this method, a random subset of cells from a group is aggregated into a pseudo-bulk profile.

The pseudobulk method can be turned on by setting n_pseudo = 1.

julia_do.call("reoa",list("scRNA_expr.txt",
						"scRNA_metadata.txt",
        				n_pseudo = 1),need_return="Julia",show_value=FALSE)

By default, profiling does not use the pseudo-bulk method (n_pseudo = 0). 0 indicates that the pseudo-bulk mode is not used, and other values indicate the number of samples in each group after pseudo-bulk is combined.

See 1.3 Optional Parameters.

See 1.4 Example output file.

In this chapter, we will present some examples of the use of experimental designs.

Differential expression analysis often involves datasets from different sources, such as different sequencing platforms, different sample sources, or different sample processing methods. RankCompV3 supports direct column concatenation of expression profiles for integrated analysis of different datasets.

julia> expr
5×11 DataFrame
 Row │ gene_name  s1     s2     s3     c1     c8     c6     a1     a2     b1     b6
     │ String7    Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64
─────┼─────────────────────────────────────────────────────────────────────────────────
   1 │ DE1           10     53     18     84     18    200    180    176     26     24
   2 │ DE2            5     22     59     11     33     26     37     36     40     79
   3 │ DE3           62     39     18     19      8    157     81     89    220     97
   4 │ DE4            6    116    131      3     49     15    205     63     75    228
   5 │ DE5           28     31     27     58     16    505     92    119    157    261

Among them, s, c, a and b samples are data from different sources. Samples s and c are in the same group, and samples a and b are in the same group.

julia> meta
10×2 DataFrame
 Row │ sample_name  group
     │ String15     String7
─────┼──────────────────────
   1 │ s1           group1
   2 │ s2           group1
   3 │ s3           group1
   4 │ c1           group1
   5 │ c8           group1
   6 │ c6           group1
   7 │ a1           group2
   8 │ a2           group2
   9 │ b1           group2
  10 │ b6           group2

When multiple cell types are included, the characteristics of each cell class need to be analyzed and each cell class compared to the rest. Suppose there are three types of cells, such as cell types s, c, a, and b.

julia> meta
10×2 DataFrame
 Row │ sample_name  group
     │ String       String
─────┼────────────────────────
   1 │ s1           celltype1
   2 │ s2           celltype1
   3 │ s3           celltype1
   4 │ c1           celltype2
   5 │ c8           celltype2
   6 │ c6           celltype2
   7 │ a1           celltype3
   8 │ a2           celltype3
   9 │ b1           celltype4
  10 │ b6           celltype4

By default, conditions are listed in order of precedence.

julia> unique(meta.group)
4-element Vector{String}:
 "celltype1"
 "celltype2"
 "celltype3"
 "celltype4"

For analyzing single-cell data, drop-out phenomenon often exists. The n_pseudo parameter of pseudobulk mode of RankCompV3 can be used to combine different cells to reduce the effect of drop-out.

julia> reoa("/public/yanj/data/fn_expr.txt",
    	"/public/yanj/data/fn_meta.txt",
    	n_pseudo = 10)

By default, profiling does not use the pseudo-bulk method (n_pseudo = 0). 0 indicates that the pseudo-bulk mode is not used, and other values indicate the number of samples in each group after pseudo-bulk is combined.