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Normalized RNA-seq data

Source: R/data.R
TRNSCRPT_NORM_DATA.Rd

Normalized sample-level RNA-seq (TRNSCRPT) data used for visualization

Usage

TRNSCRPT_BLOOD_NORM_DATA

TRNSCRPT_HIPPOC_NORM_DATA

TRNSCRPT_CORTEX_NORM_DATA

TRNSCRPT_HYPOTH_NORM_DATA

TRNSCRPT_SKMGN_NORM_DATA

TRNSCRPT_SKMVL_NORM_DATA

TRNSCRPT_HEART_NORM_DATA

TRNSCRPT_KIDNEY_NORM_DATA

TRNSCRPT_ADRNL_NORM_DATA

TRNSCRPT_COLON_NORM_DATA

TRNSCRPT_SPLEEN_NORM_DATA

TRNSCRPT_TESTES_NORM_DATA

TRNSCRPT_OVARY_NORM_DATA

TRNSCRPT_VENACV_NORM_DATA

TRNSCRPT_LUNG_NORM_DATA

TRNSCRPT_SMLINT_NORM_DATA

TRNSCRPT_LIVER_NORM_DATA

TRNSCRPT_BAT_NORM_DATA

TRNSCRPT_WATSC_NORM_DATA

Format

A data frame with genes in rows (feature_ID) and samples in columns (viallabel)

An object of class data.frame with 16608 rows and 54 columns.

An object of class data.frame with 16443 rows and 54 columns.

An object of class data.frame with 16955 rows and 54 columns.

An object of class data.frame with 13774 rows and 54 columns.

An object of class data.frame with 13973 rows and 54 columns.

An object of class data.frame with 14445 rows and 54 columns.

An object of class data.frame with 15981 rows and 54 columns.

An object of class data.frame with 15809 rows and 54 columns.

An object of class data.frame with 16556 rows and 54 columns.

An object of class data.frame with 16319 rows and 54 columns.

An object of class data.frame with 17359 rows and 29 columns.

An object of class data.frame with 17035 rows and 28 columns.

An object of class data.frame with 16338 rows and 54 columns.

An object of class data.frame with 16505 rows and 54 columns.

An object of class data.frame with 16411 rows and 54 columns.

An object of class data.frame with 14437 rows and 54 columns.

An object of class data.frame with 16154 rows and 54 columns.

An object of class data.frame with 16764 rows and 54 columns.

Source

pass1b-06/analysis/transcriptomics/transcript-rna-seq/normalized-data/*normalized-log-cpm*

Details

Filtering of lowly expressed genes and normalization were performed separately in each tissue. RSEM gene counts were used to remove lowly expressed genes, defined as having 0.5 or fewer counts per million in all but one sample. To generate normalized sample-level data, filtered gene counts were TMM-normalized using edgeR::calcNormFactors(), followed by conversion to log counts per million with edgeR::cpm().